>> Narrator: From the Computer Museum in Mountain View, California, it's theCUBE. Covering AGC Silicon Valley GROW! Awards. Brought to you by ACG Silicon Valley. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. The program is just about to begin here at the ACGSV GROW! Awards, 14th Annual. We're excited to be here for our third year. 300 people are going to be giving out some hardware here shortly. But, before we do that we're excited to have Alex Wolf, all the way up from Santa Cruz. He's the dean of the Baskin School of Engineering at UC Santa Cruz. Welcome, Alex. >> Thank you very much, it's great to be here. >> Absolutely. So, what do you think of this organization? How did you get involved? >> Well, it's been great for us. We've been drawn in by some great alumni who have been involved with the organization, and they're interested in helping Santa Cruz UC Santa Cruz School of Engineering, and partnering with ACG is just a perfect way to do it. >> Excellent. So, I was doing a little homework, obviously, before you came on. I was looking through the curriculum of the school, the engineering school, and you've got CS and E, and all the normal stuff, but two things jumped out to me, biomolecular engineering and computational media. >> That's right. >> What are those disciplines? >> Well, let's start with biomolecular engineering. That's where we are doing a lot of work in health and life. Santa Cruz is famous for one particular thing that happened a number of years ago, which was the sequencing of the human genome. Now, Santa Cruz played a huge role in that. This was the place where we were able to assemble the human genome for the first time, and publish it on the web. >> What year was that? >> That was 2003. >> And back then it took massive amount of computer, massive amount of time. >> Lots of time, millions and millions of dollars. This was a project that was run by the government. Many partners and Santa Cruz researchers in School of Engineering were able to crack that nut and get this genome sequenced. >> And now we can do it-- Now, it's getting cheaper and cheaper, we've got researchers who've been working on that, we've spun out a bunch of companies that have worked on less and less expensive, faster and faster sequencing techniques. >> Really, with the goal to get to individualized medicine, right, to get to individualized treatment. >> That's right, personalized medicine, precision medicine, that's the goal. It's amazing what you can do if you know the genome history, if you can apply that to the drug treatments, it's fantastic. >> I think medical science is so interesting, because from whatever point you are, you look back 10 years and it looks like bloodletting. No matter what we do today, in 10 years from now, we're going to look back >> It's true at cancer treatment, like we give people poison until they almost die, >> That's right. >> that's the way we treat 'em? >> That's right, and the genome will tell you so much about that cancer treatment. We're doing other things too, in stem cell and nanopore technology, so there's just a wonderful set of technologies that people are inventing in the school. >> Great, now what about computational media? >> Computational media is a rather different thing. That is a concept where we're looking at how media can be generated through algorithms, and this has very interesting applications in the game industry, in journalism, in many parts of our interaction with humans. It's great to be able to have a computer that really understands how to generate meaningful, realistic text. >> What is the main benefit in some of the early research that you see, because we've seen some really simple versions of this out there, straight little app that kids play sports, you know, you finish the game, you hit the game over, and it generates a nice little article for you. >> Absolutely. You know, you mentioned personalization before. It's the same thing with computational media. You can get a game to be much more personalized to the player. It can understand that experience, understand the interests of the game player, and then tailor itself to that player. >> So, how much do you work with the psychology department in this world, because it's so much human factors, right? >> Absolutely. We have a great collaboration with psychology. That's really, really important. You know, the computational media department is actually going to be growing into Silicon Valley. You see Santa Cruz has recently opened a campus in Silicon Valley. >> Where? >> It's in Santa Clara, and we're right now hiring faculty into that campus. >> So, is it open then, or when will it be open? >> The facility is open. We held an ACG event there in January. We're going to be holding more of them there. It's a great location. >> Excellent. All right, well, maybe we'll have to come by and do a field trip >> Please do. when you get it all outfitted. >> Absolutely, absolutely. >> All right, well, unfortunately, we have to leave it there. They're going to pull everybody into the keynotes, but thanks taking a few minutes. >> I'm looking forward to it. Thank you very much. >> All right, he's Alex, I'm Jeff. You're watching theCUBE from ACGSV, Mountain View, California. Thanks for watching. (techy music)

(upbeat music) >> Hello and welcome to theCube's coverage of International Women's Day. I'm John Furrier, your host of "theCUBE." We've got great two remote guests coming into our Palo Alto Studios, some tech athletes, as we say, people that've been in the trenches, years of experience, Lena Smart, CISO at MongoDB, Cube alumni, and Tara Hernandez, VP of Developer Productivity at MongoDB as well. Thanks for coming in to this program and supporting our efforts today. Thanks so much. >> Thanks for having us. >> Yeah, everyone talk about the journey in tech, where it all started. Before we get there, talk about what you guys are doing at MongoDB specifically. MongoDB is kind of gone the next level as a platform. You have your own ecosystem, lot of developers, very technical crowd, but it's changing the business transformation. What do you guys do at Mongo? We'll start with you, Lena. >> So I'm the CISO, so all security goes through me. I like to say, well, I don't like to say, I'm described as the ones throat to choke. So anything to do with security basically starts and ends with me. We do have a fantastic Cloud engineering security team and a product security team, and they don't report directly to me, but obviously we have very close relationships. I like to keep that kind of church and state separate and I know I've spoken about that before. And we just recently set up a physical security team with an amazing gentleman who left the FBI and he came to join us after 26 years for the agency. So, really starting to look at the physical aspects of what we offer as well. >> I interviewed a CISO the other day and she said, "Every day is day zero for me." Kind of goofing on the Amazon Day one thing, but Tara, go ahead. Tara, go ahead. What's your role there, developer productivity? What are you focusing on? >> Sure. Developer productivity is kind of the latest description for things that we've described over the years as, you know, DevOps oriented engineering or platform engineering or build and release engineering development infrastructure. It's all part and parcel, which is how do we actually get our code from developer to customer, you know, and all the mechanics that go into that. It's been something I discovered from my first job way back in the early '90s at Borland. And the art has just evolved enormously ever since, so. >> Yeah, this is a very great conversation both of you guys, right in the middle of all the action and data infrastructures changing, exploding, and involving big time AI and data tsunami and security never stops. Well, let's get into, we'll talk about that later, but let's get into what motivated you guys to pursue a career in tech and what were some of the challenges that you faced along the way? >> I'll go first. The fact of the matter was I intended to be a double major in history and literature when I went off to university, but I was informed that I had to do a math or a science degree or else the university would not be paid for. At the time, UC Santa Cruz had a policy that called Open Access Computing. This is, you know, the late '80s, early '90s. And anybody at the university could get an email account and that was unusual at the time if you were, those of us who remember, you used to have to pay for that CompuServe or AOL or, there's another one, I forget what it was called, but if a student at Santa Cruz could have an email account. And because of that email account, I met people who were computer science majors and I'm like, "Okay, I'll try that." That seems good. And it was a little bit of a struggle for me, a lot I won't lie, but I can't complain with how it ended up. And certainly once I found my niche, which was development infrastructure, I found my true love and I've been doing it for almost 30 years now. >> Awesome. Great story. Can't wait to ask a few questions on that. We'll go back to that late '80s, early '90s. Lena, your journey, how you got into it. >> So slightly different start. I did not go to university. I had to leave school when I was 16, got a job, had to help support my family. Worked a bunch of various jobs till I was about 21 and then computers became more, I think, I wouldn't say they were ubiquitous, but they were certainly out there. And I'd also been saving up every penny I could earn to buy my own computer and bought an Amstrad 1640, 20 meg hard drive. It rocked. And kind of took that apart, put it back together again, and thought that could be money in this. And so basically just teaching myself about computers any job that I got. 'Cause most of my jobs were like clerical work and secretary at that point. But any job that had a computer in front of that, I would make it my business to go find the guy who did computing 'cause it was always a guy. And I would say, you know, I want to learn how these work. Let, you know, show me. And, you know, I would take my lunch hour and after work and anytime I could with these people and they were very kind with their time and I just kept learning, so yep. >> Yeah, those early days remind me of the inflection point we're going through now. This major C change coming. Back then, if you had a computer, you had to kind of be your own internal engineer to fix things. Remember back on the systems revolution, late '80s, Tara, when, you know, your career started, those were major inflection points. Now we're seeing a similar wave right now, security, infrastructure. It feels like it's going to a whole nother level. At Mongo, you guys certainly see this as well, with this AI surge coming in. A lot more action is coming in. And so there's a lot of parallels between these inflection points. How do you guys see this next wave of change? Obviously, the AI stuff's blowing everyone away. Oh, new user interface. It's been called the browser moment, the mobile iPhone moment, kind of for this generation. There's a lot of people out there who are watching that are young in their careers, what's your take on this? How would you talk to those folks around how important this wave is? >> It, you know, it's funny, I've been having this conversation quite a bit recently in part because, you know, to me AI in a lot of ways is very similar to, you know, back in the '90s when we were talking about bringing in the worldwide web to the forefront of the world, right. And we tended to think in terms of all the optimistic benefits that would come of it. You know, free passing of information, availability to anyone, anywhere. You just needed an internet connection, which back then of course meant a modem. >> John: Not everyone had though. >> Exactly. But what we found in the subsequent years is that human beings are what they are and we bring ourselves to whatever platforms that are there, right. And so, you know, as much as it was amazing to have this freely available HTML based internet experience, it also meant that the negatives came to the forefront quite quickly. And there were ramifications of that. And so to me, when I look at AI, we're already seeing the ramifications to that. Yes, are there these amazing, optimistic, wonderful things that can be done? Yes. >> Yeah. >> But we're also human and the bad stuff's going to come out too. And how do we- >> Yeah. >> How do we as an industry, as a community, you know, understand and mitigate those ramifications so that we can benefit more from the positive than the negative. So it is interesting that it comes kind of full circle in really interesting ways. >> Yeah. The underbelly takes place first, gets it in the early adopter mode. Normally industries with, you know, money involved arbitrage, no standards. But we've seen this movie before. Is there hope, Lena, that we can have a more secure environment? >> I would hope so. (Lena laughs) Although depressingly, we've been in this well for 30 years now and we're, at the end of the day, still telling people not to click links on emails. So yeah, that kind of still keeps me awake at night a wee bit. The whole thing about AI, I mean, it's, obviously I am not an expert by any stretch of the imagination in AI. I did read (indistinct) book recently about AI and that was kind of interesting. And I'm just trying to teach myself as much as I can about it to the extent of even buying the "Dummies Guide to AI." Just because, it's actually not a dummies guide. It's actually fairly interesting, but I'm always thinking about it from a security standpoint. So it's kind of my worst nightmare and the best thing that could ever happen in the same dream. You know, you've got this technology where I can ask it a question and you know, it spits out generally a reasonable answer. And my team are working on with Mark Porter our CTO and his team on almost like an incubation of AI link. What would it look like from MongoDB? What's the legal ramifications? 'Cause there will be legal ramifications even though it's the wild, wild west just now, I think. Regulation's going to catch up to us pretty quickly, I would think. >> John: Yeah, yeah. >> And so I think, you know, as long as companies have a seat at the table and governments perhaps don't become too dictatorial over this, then hopefully we'll be in a good place. But we'll see. I think it's a really interest, there's that curse, we're living in interesting times. I think that's where we are. >> It's interesting just to stay on this tech trend for a minute. The standards bodies are different now. Back in the old days there were, you know, IEEE standards, ITF standards. >> Tara: TPC. >> The developers are the new standard. I mean, now you're seeing open source completely different where it was in the '90s to here beginning, that was gen one, some say gen two, but I say gen one, now we're exploding with open source. You have kind of developers setting the standards. If developers like it in droves, it becomes defacto, which then kind of rolls into implementation. >> Yeah, I mean I think if you don't have developer input, and this is why I love working with Tara and her team so much is 'cause they get it. If we don't have input from developers, it's not going to get used. There's going to be ways of of working around it, especially when it comes to security. If they don't, you know, if you're a developer and you're sat at your screen and you don't want to do that particular thing, you're going to find a way around it. You're a smart person. >> Yeah. >> So. >> Developers on the front lines now versus, even back in the '90s, they're like, "Okay, consider the dev's, got a QA team." Everything was Waterfall, now it's Cloud, and developers are on the front lines of everything. Tara, I mean, this is where the standards are being met. What's your reaction to that? >> Well, I think it's outstanding. I mean, you know, like I was at Netscape and part of the crowd that released the browser as open source and we founded mozilla.org, right. And that was, you know, in many ways kind of the birth of the modern open source movement beyond what we used to have, what was basically free software foundation was sort of the only game in town. And I think it is so incredibly valuable. I want to emphasize, you know, and pile onto what Lena was saying, it's not just that the developers are having input on a sort of company by company basis. Open source to me is like a checks and balance, where it allows us as a broader community to be able to agree on and enforce certain standards in order to try and keep the technology platforms as accessible as possible. I think Kubernetes is a great example of that, right. If we didn't have Kubernetes, that would've really changed the nature of how we think about container orchestration. But even before that, Linux, right. Linux allowed us as an industry to end the Unix Wars and as someone who was on the front lines of that as well and having to support 42 different operating systems with our product, you know, that was a huge win. And it allowed us to stop arguing about operating systems and start arguing about software or not arguing, but developing it in positive ways. So with, you know, with Kubernetes, with container orchestration, we all agree, okay, that's just how we're going to orchestrate. Now we can build up this huge ecosystem, everybody gets taken along, right. And now it changes the game for what we're defining as business differentials, right. And so when we talk about crypto, that's a little bit harder, but certainly with AI, right, you know, what are the checks and balances that as an industry and as the developers around this, that we can in, you know, enforce to make sure that no one company or no one body is able to overly control how these things are managed, how it's defined. And I think that is only for the benefit in the industry as a whole, particularly when we think about the only other option is it gets regulated in ways that do not involve the people who actually know the details of what they're talking about. >> Regulated and or thrown away or bankrupt or- >> Driven underground. >> Yeah. >> Which would be even worse actually. >> Yeah, that's a really interesting, the checks and balances. I love that call out. And I was just talking with another interview part of the series around women being represented in the 51% ratio. Software is for everybody. So that we believe that open source movement around the collective intelligence of the participants in the industry and independent of gender, this is going to be the next wave. You're starting to see these videos really have impact because there are a lot more leaders now at the table in companies developing software systems and with AI, the aperture increases for applications. And this is the new dynamic. What's your guys view on this dynamic? How does this go forward in a positive way? Is there a certain trajectory you see? For women in the industry? >> I mean, I think some of the states are trying to, again, from the government angle, some of the states are trying to force women into the boardroom, for example, California, which can be no bad thing, but I don't know, sometimes I feel a bit iffy about all this kind of forced- >> John: Yeah. >> You know, making, I don't even know how to say it properly so you can cut this part of the interview. (John laughs) >> Tara: Well, and I think that they're >> I'll say it's not organic. >> No, and I think they're already pulling it out, right. It's already been challenged so they're in the process- >> Well, this is the open source angle, Tara, you are getting at it. The change agent is open, right? So to me, the history of the proven model is openness drives transparency drives progress. >> No, it's- >> If you believe that to be true, this could have another impact. >> Yeah, it's so interesting, right. Because if you look at McKinsey Consulting or Boston Consulting or some of the other, I'm blocking on all of the names. There has been a decade or more of research that shows that a non homogeneous employee base, be it gender or ethnicity or whatever, generates more revenue, right? There's dollar signs that can be attached to this, but it's not enough for all companies to want to invest in that way. And it's not enough for all, you know, venture firms or investment firms to grant that seed money or do those seed rounds. I think it's getting better very slowly, but socialization is a much harder thing to overcome over time. Particularly, when you're not just talking about one country like the United States in our case, but around the world. You know, tech centers now exist all over the world, including places that even 10 years ago we might not have expected like Nairobi, right. Which I think is amazing, but you have to factor in the cultural implications of that as well, right. So yes, the openness is important and we have, it's important that we have those voices, but I don't think it's a panacea solution, right. It's just one more piece. I think honestly that one of the most important opportunities has been with Cloud computing and Cloud's been around for a while. So why would I say that? It's because if you think about like everybody holds up the Steve Jobs, Steve Wozniak, back in the '70s, or Sergey and Larry for Google, you know, you had to have access to enough credit card limit to go to Fry's and buy your servers and then access to somebody like Susan Wojcicki to borrow the garage or whatever. But there was still a certain amount of upfrontness that you had to be able to commit to, whereas now, and we've, I think, seen a really good evidence of this being able to lease server resources by the second and have development platforms that you can do on your phone. I mean, for a while I think Africa, that the majority of development happened on mobile devices because there wasn't a sufficient supply chain of laptops yet. And that's no longer true now as far as I know. But like the power that that enables for people who would otherwise be underrepresented in our industry instantly opens it up, right? And so to me that's I think probably the biggest opportunity that we've seen from an industry on how to make more availability in underrepresented representation for entrepreneurship. >> Yeah. >> Something like AI, I think that's actually going to take us backwards if we're not careful. >> Yeah. >> Because of we're reinforcing that socialization. >> Well, also the bias. A lot of people commenting on the biases of the large language inherently built in are also problem. Lena, I want you to weigh on this too, because I think the skills question comes up here and I've been advocating that you don't need the pedigree, college pedigree, to get into a certain jobs, you mentioned Cloud computing. I mean, it's been around for you think a long time, but not really, really think about it. The ability to level up, okay, if you're going to join something new and half the jobs in cybersecurity are created in the past year, right? So, you have this what used to be a barrier, your degree, your pedigree, your certification would take years, would be a blocker. Now that's gone. >> Lena: Yeah, it's the opposite. >> That's, in fact, psychology. >> I think so, but the people who I, by and large, who I interview for jobs, they have, I think security people and also I work with our compliance folks and I can't forget them, but let's talk about security just now. I've always found a particular kind of mindset with security folks. We're very curious, not very good at following rules a lot of the time, and we'd love to teach others. I mean, that's one of the big things stem from the start of my career. People were always interested in teaching and I was interested in learning. So it was perfect. And I think also having, you know, strong women leaders at MongoDB allows other underrepresented groups to actually apply to the company 'cause they see that we're kind of talking the talk. And that's been important. I think it's really important. You know, you've got Tara and I on here today. There's obviously other senior women at MongoDB that you can talk to as well. There's a bunch of us. There's not a whole ton of us, but there's a bunch of us. And it's good. It's definitely growing. I've been there for four years now and I've seen a growth in women in senior leadership positions. And I think having that kind of track record of getting really good quality underrepresented candidates to not just interview, but come and join us, it's seen. And it's seen in the industry and people take notice and they're like, "Oh, okay, well if that person's working, you know, if Tara Hernandez is working there, I'm going to apply for that." And that in itself I think can really, you know, reap the rewards. But it's getting started. It's like how do you get your first strong female into that position or your first strong underrepresented person into that position? It's hard. I get it. If it was easy, we would've sold already. >> It's like anything. I want to see people like me, my friends in there. Am I going to be alone? Am I going to be of a group? It's a group psychology. Why wouldn't? So getting it out there is key. Is there skills that you think that people should pay attention to? One's come up as curiosity, learning. What are some of the best practices for folks trying to get into the tech field or that's in the tech field and advancing through? What advice are you guys- >> I mean, yeah, definitely, what I say to my team is within my budget, we try and give every at least one training course a year. And there's so much free stuff out there as well. But, you know, keep learning. And even if it's not right in your wheelhouse, don't pick about it. Don't, you know, take a look at what else could be out there that could interest you and then go for it. You know, what does it take you few minutes each night to read a book on something that might change your entire career? You know, be enthusiastic about the opportunities out there. And there's so many opportunities in security. Just so many. >> Tara, what's your advice for folks out there? Tons of stuff to taste, taste test, try things. >> Absolutely. I mean, I always say, you know, my primary qualifications for people, I'm looking for them to be smart and motivated, right. Because the industry changes so quickly. What we're doing now versus what we did even last year versus five years ago, you know, is completely different though themes are certainly the same. You know, we still have to code and we still have to compile that code or package the code and ship the code so, you know, how well can we adapt to these new things instead of creating floppy disks, which was my first job. Five and a quarters, even. The big ones. >> That's old school, OG. There it is. Well done. >> And now it's, you know, containers, you know, (indistinct) image containers. And so, you know, I've gotten a lot of really great success hiring boot campers, you know, career transitioners. Because they bring a lot experience in addition to the technical skills. I think the most important thing is to experiment and figuring out what do you like, because, you know, maybe you are really into security or maybe you're really into like deep level coding and you want to go back, you know, try to go to school to get a degree where you would actually want that level of learning. Or maybe you're a front end engineer, you want to be full stacked. Like there's so many different things, data science, right. Maybe you want to go learn R right. You know, I think it's like figure out what you like because once you find that, that in turn is going to energize you 'cause you're going to feel motivated. I think the worst thing you could do is try to force yourself to learn something that you really could not care less about. That's just the worst. You're going in handicapped. >> Yeah and there's choices now versus when we were breaking into the business. It was like, okay, you software engineer. They call it software engineering, that's all it was. You were that or you were in sales. Like, you know, some sort of systems engineer or sales and now it's,- >> I had never heard of my job when I was in school, right. I didn't even know it was a possibility. But there's so many different types of technical roles, you know, absolutely. >> It's so exciting. I wish I was young again. >> One of the- >> Me too. (Lena laughs) >> I don't. I like the age I am. So one of the things that I did to kind of harness that curiosity is we've set up a security champions programs. About 120, I guess, volunteers globally. And these are people from all different backgrounds and all genders, diversity groups, underrepresented groups, we feel are now represented within this champions program. And people basically give up about an hour or two of their time each week, with their supervisors permission, and we basically teach them different things about security. And we've now had seven full-time people move from different areas within MongoDB into my team as a result of that program. So, you know, monetarily and time, yeah, saved us both. But also we're showing people that there is a path, you know, if you start off in Tara's team, for example, doing X, you join the champions program, you're like, "You know, I'd really like to get into red teaming. That would be so cool." If it fits, then we make that happen. And that has been really important for me, especially to give, you know, the women in the underrepresented groups within MongoDB just that window into something they might never have seen otherwise. >> That's a great common fit is fit matters. Also that getting access to what you fit is also access to either mentoring or sponsorship or some sort of, at least some navigation. Like what's out there and not being afraid to like, you know, just ask. >> Yeah, we just actually kicked off our big mentor program last week, so I'm the executive sponsor of that. I know Tara is part of it, which is fantastic. >> We'll put a plug in for it. Go ahead. >> Yeah, no, it's amazing. There's, gosh, I don't even know the numbers anymore, but there's a lot of people involved in this and so much so that we've had to set up mentoring groups rather than one-on-one. And I think it was 45% of the mentors are actually male, which is quite incredible for a program called Mentor Her. And then what we want to do in the future is actually create a program called Mentor Them so that it's not, you know, not just on the female and so that we can live other groups represented and, you know, kind of break down those groups a wee bit more and have some more granularity in the offering. >> Tara, talk about mentoring and sponsorship. Open source has been there for a long time. People help each other. It's community-oriented. What's your view of how to work with mentors and sponsors if someone's moving through ranks? >> You know, one of the things that was really interesting, unfortunately, in some of the earliest open source communities is there was a lot of pervasive misogyny to be perfectly honest. >> Yeah. >> And one of the important adaptations that we made as an open source community was the idea, an introduction of code of conducts. And so when I'm talking to women who are thinking about expanding their skills, I encourage them to join open source communities to have opportunity, even if they're not getting paid for it, you know, to develop their skills to work with people to get those code reviews, right. I'm like, "Whatever you join, make sure they have a code of conduct and a good leadership team. It's very important." And there are plenty, right. And then that idea has come into, you know, conferences now. So now conferences have codes of contact, if there are any good, and maybe not all of them, but most of them, right. And the ideas of expanding that idea of intentional healthy culture. >> John: Yeah. >> As a business goal and business differentiator. I mean, I won't lie, when I was recruited to come to MongoDB, the culture that I was able to discern through talking to people, in addition to seeing that there was actually women in senior leadership roles like Lena, like Kayla Nelson, that was a huge win. And so it just builds on momentum. And so now, you know, those of us who are in that are now representing. And so that kind of reinforces, but it's all ties together, right. As the open source world goes, particularly for a company like MongoDB, which has an open source product, you know, and our community builds. You know, it's a good thing to be mindful of for us, how we interact with the community and you know, because that could also become an opportunity for recruiting. >> John: Yeah. >> Right. So we, in addition to people who might become advocates on Mongo's behalf in their own company as a solution for themselves, so. >> You guys had great successful company and great leadership there. I mean, I can't tell you how many times someone's told me "MongoDB doesn't scale. It's going to be dead next year." I mean, I was going back 10 years. It's like, just keeps getting better and better. You guys do a great job. So it's so fun to see the success of developers. Really appreciate you guys coming on the program. Final question, what are you guys excited about to end the segment? We'll give you guys the last word. Lena will start with you and Tara, you can wrap us up. What are you excited about? >> I'm excited to see what this year brings. I think with ChatGPT and its copycats, I think it'll be a very interesting year when it comes to AI and always in the lookout for the authentic deep fakes that we see coming out. So just trying to make people aware that this is a real thing. It's not just pretend. And then of course, our old friend ransomware, let's see where that's going to go. >> John: Yeah. >> And let's see where we get to and just genuine hygiene and housekeeping when it comes to security. >> Excellent. Tara. >> Ah, well for us, you know, we're always constantly trying to up our game from a security perspective in the software development life cycle. But also, you know, what can we do? You know, one interesting application of AI that maybe Google doesn't like to talk about is it is really cool as an addendum to search and you know, how we might incorporate that as far as our learning environment and developer productivity, and how can we enable our developers to be more efficient, productive in their day-to-day work. So, I don't know, there's all kinds of opportunities that we're looking at for how we might improve that process here at MongoDB and then maybe be able to share it with the world. One of the things I love about working at MongoDB is we get to use our own products, right. And so being able to have this interesting document database in order to put information and then maybe apply some sort of AI to get it out again, is something that we may well be looking at, if not this year, then certainly in the coming year. >> Awesome. Lena Smart, the chief information security officer. Tara Hernandez, vice president developer of productivity from MongoDB. Thank you so much for sharing here on International Women's Day. We're going to do this quarterly every year. We're going to do it and then we're going to do quarterly updates. Thank you so much for being part of this program. >> Thank you. >> Thanks for having us. >> Okay, this is theCube's coverage of International Women's Day. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

>>Hello everyone. My name is Savannah Peterson, coming to you live from the Kim Con Show floor on the cube here in Detroit, Michigan. The energy is pulsing big event for the Cloud Native Foundation, and I'm joined by John Furrier on my left. John. Hello. >>Great, great, great to have you on the cube. Thanks for being our new host. You look great, Great segment coming up. I'm looking forward to this. Savannah, this is a great segment. A cube alumni, an OG in the cloud, native world or cloud aati. I, as I call it, been there, done that. A lot of respect, a lot of doing some really amazing, I call it the super cloud holy grail. But we'll see >>Your favorite word, >>This favorite word, It's a really strong segment. Looking forward to hearing from this guest. >>Yes, I am very excited and I'm gonna let him tee it up a little bit. But our guest and his project were actually mentioned in the opening keynote this morning, which is very, very exciting. Ladies and gentlemen, please welcome Baam Tobar Baam, thanks for being here with >>Us. Thank you guys. So good to be back here on the show and, and this exciting energy around us. So it was super, super awesome to be here. >>Yeah, it feels great. So let's start with the opening keynote. Did you know you were gonna get that shout out? >>No, not at all. I, it was, it was really cool to see, you know, I think Cruz was up there talking about how they were building their own platform for autonomous cars and what's running behind it. And they mentioned all these projects and you know, we were like, Wow, that sounds super familiar. And then, then, and then they said, Okay, yeah, we we're, you know, cross plane. They mentioned cross plane, they mentioned, Upbound mentioned the work that we're doing in this space to help folks effectively run, you know, their own layer on top of cloud computing. >>And then Tom, we've known each other, >>We're gonna do a bingo super cloud. So how many times is this Super cloud? So >>Super Cloud is super services, super apps around us. He enables a lot of great things that Brian Grace had a great podcast this week on super services. So it's super, super exciting, >>Super great time on the queue. Super, >>Super >>Cloud conversation. All seriously. Now we've known each other for a long time. You've been to every cub com, you've been in open source, you've seen the seen where it's been, where it is now. Super exciting that in mainstream conversations we're talking about super cloud extractions and around interoperability. Things that were once like really hard to do back, even back on the opens stack days. Now we're at a primetime spot where the control plane, the data planes are in play as a viable architectural component of all the biggest conversations. Yeah, you're in the middle of it. What's your take on it? Give some perspective of why this is so important. >>I mean, look, the key here is to standardize, right? Get to standardization, right? And, and what we saw, like early days of cloud native, it was mostly around Kubernetes, but it was Kubernetes as a, you know, essentially a container orchestrator, the container of wars, Docker, Mesos, et cetera. And then Kubernetes emerged as a, a, the winner in containers, right? But containers is a workload, one kind of workload. It's, I run containers on it, not everything's containers, right? And the, you know, what we're seeing now is the Kubernetes API is emerging as a way to standardize on literally everything in cloud. Not just containers, but you know, VMs, serverless, Lambda, et cetera, storage databases that all using a common approach, a common API layer, a common way to do access control, a common way to do policy, all built around open source projects and you know, the cloud data of ecosystem that you were seeing around here. And that's exciting cuz we've, for the first time we're arriving at some kind of standardization. >>Every major inflection point has this defacto standard evolution, then it becomes kind of commonplace. Great. I agree with Kubernetes. The question I wanted to ask you is what's the impact to the DevOps community? DevSecOps absolutely dominated the playbook, if you will. Developers we're saying we'll run companies cuz they'll be running the applications. It's not a department anymore. Yes, it is the business. If you believe the digital transformation finds its final conclusion, which it will at some point. So more developers doing more, ask more stuff. >>Look, if you, I'd be hard pressed to find somebody that's has a title of DevOps or SRE that can't at least spell Kubernetes, if not running in production, right? And so from that perspective, I think this is a welcome change. Standardize on something that's already familiar to everyone is actually really powerful. They don't have to go, Okay, we learned Kubernetes, now you guys are taking us down a different path of standardization. Or something else has emerged. It's the same thing. It's like we have what, eight years now of cloud native roughly. And, and people in the DevOps space welcome a change where they are basically standardizing on things that are working right? They're actually working right? And they could be used in more use cases, in more scenarios than they're actually, you know, become versatile. They become, you know, ubiquitous as >>You will take a minute to just explain what you guys are selling and doing. What's the product, what's the traction, why are people using you? What's the big, big mo position value statement you guys think? >>Yeah, so, so, so the, my company's called Upbound and where the, where the folks behind the, the cross plane project and cross plane is effective, takes Kubernetes and extends it to beyond containers and to ev managing everything in cloud, right? So if you think about that, if you love the model where you're like, I, I go to Kubernetes cluster and I tell it to run a bunch of containers and it does it for me and I walk away, you can do that for the rest of the surface area of cloud, including your VMs and your storage and across cloud vendors, hybrid models, All of it works in a consistent standardized way, you know, using crossline, right? And I found >>What do you solve? What do you solve or eliminate? What happens? Why does this work? Are you replacing something? Are you extracting away something? Are you changing >>Something? I think we're layering on top of things that people have, right? So, so you'll see people are organized differently. We see a common pattern now where there's shared services teams or platform teams as you hear within enterprises that are responsible for basically managing infrastructure and offering a self-service experience to developers, right? Those teams are all about standardization. They're all about creating things that help them reduce the toil, manage things in a common way, and then offer self-service abstractions to their, you know, developers and customers. So they don't have to be in the middle of every request. Things can go faster. We're seeing a pattern now where the, these teams are standardizing on the Kubernetes API or standardizing on cross plane and standardizing on things that make their life easier, right? They don't have to replace what they're doing, they just have to layer and use it. And I layer it's probably a, an opening for you that makes it sound >>More complex, I think, than what you're actually trying to do. I mean, you as a company are all about velocity as an ethos, which I think is great. Do you think that standardization is the key in increasing velocity for teams leveraging both cross claim, Kubernetes? Anyone here? >>Look, I mean, everybody's trying to achieve the same thing. Everybody wants to go faster, they want to innovate faster. They don't want tech to be the friction to innovation, right? Right. They want, they wanna go from feature to production in minutes, right? And so, or less to that extent, standardization is a way to achieve that. It's not the only way to achieve that. It's, it's means to achieve that. And if you've standardized, that means that less people are involved. You can automate more, you can st you can centralize. And by doing that, that means you can innovate faster. And if you don't innovate these days, you're in trouble. Yeah. You're outta business. >>Do you think that, so Kubernetes has a bit of a reputation for complexity. You're obviously creating a tool that makes things easier as you apply Kubernetes outside just an orchestration and container environment. Do you, what do you see those advantages being across the spectrum of tools that people are leveraging you >>For? Yeah, I mean, look, if Kubernetes is a platform, right? To build other things on top of, and as a, as a result, it's something that's used to kind of on the back end. Like you would never, you should put something in front of Kubernetes as an application model or consumption interface of portals or Right, Yeah. To give zero teams. But you should still capture all your policies, you know, automation and compliance governance at the Kubernetes layer, right? At the, or with cross plane at that layer as well, right? Right. And so if you follow that model, you can get the best of world both worlds. You standardize, you centralize, you are able to have, you know, common controls and policies and everything else, but you can expose something that's a dev friendly experience on top of as well. So you get the both, both the best of both worlds. >>So the problem with infrastructure is code you're saying is, is that it's not this new layer to go across environments. Does that? No, >>Infrastructure is code works slightly differently. I mean, you, you can, you can write, you know, infrastructures, codes using whatever tooling you like to go across environments. The problem with is that everybody has to learn a specific language or has to work with understanding the constructs. There's the beauty of the Kubernetes based approach and the cross playing best approach is that it puts APIs first, right? It's basically saying, look, kind of like the API meant that it, that led to AWS being created, right? Teams should interact with APIs. They're super strong contracts, right? They're visionable. Yeah. And if you, if you do that and that's kind of the power of this approach, then you can actually reach a really high level of automation and a really high level of >>Innovation. And this also just not to bring in the clouds here, but this might bring up the idea that common services create interoperability, but yet the hyper scale clouds could still differentiate on value very much faster processors if it's silicon to better functions if glam, right? I mean, so there's still, it's not killing innovation. >>It is not, And in fact I, you know, this idea of building something that looks like the lowest common denominator across clouds, we don't actually see that in practice, right? People want, people want to use the best services available to them because they don't have time to go, you know, build portability layers and everything else. But they still, even in that model want to standardize on how to call these services, how to set policy on them, how to set access control, how to actually invoke them. If you can standardize on that, you can still, you get the, you get to use these services and you get the benefits of standardization. >>Well Savannah, we were talking about this, about the Berkeley paper that came out in May, which is kind of a super cloud version they call sky computing. Their argument is that if you try to standardize too much like the old kind of OSI model back in the day, you actually gonna, the work innovations gonna stunt the growth. Do you agree with that? And how do you see, because standardization is not so much a spec and it it, it e f thing. It's not an i e committee. Yeah. It's not like that's kind of standard. It's more of defacto, >>I mean look, we've had standards emerge like, you know, if you look at my S SQL for example, and the Postgres movement, like there are now lots of vendors that offer interfaces that support Postgres even though they're differentiated completely on how it's implement. So you see that if you can stick to open interfaces and use services that offer them that tons of differentiation yet still, you know, some kind of open interface if you will. But there are also differentiated services that are, don't have open interfaces and that's okay too. As long as you're able to kind of find a way to manage them in a consistent way. I think you sh and it makes sense to your business, you should use >>Them. So enterprises like this and just not to get into the business model side real quick, but like how you guys making money? You got the project, you get the cross playing project, that's community. You guys charging what's, what's the business model? >>We we're in the business of helping people adopt and run controlled lanes that do all this management service managed service services and customer support and services, the, the plethora of things that people need where we're >>Keeping the project while >>Keeping the project. >>Correct. So that's >>The key. That's correct. Yeah. You have to balance both >>And you're all over the show. I mean, outside of the keynote mention looking here, you have four events on where can people find you if they're tuning in. We're just at the beginning and there's a lot of looks here. >>Upbound at IO is the place to find Upbound and where I have a lot of talks, you'll see Crossline mention and lots of talks and a number of talks today. We have a happy hour later today we've got a booth set up. So >>I'll be there folks. Just fyi >>And everyone will be there now. Yeah. Quick update. What's up? What's new with the cross plane project? Can you share a little commercial? What's the most important stories going on there? >>So cross plane is growing obviously, and we're seeing a ton of adoption of cross plane, especially actually in large enterprise, which is really exciting cuz they're usually the slow to move and cross plane is so central, so it's now in hundreds and thousands of deployments in woohoo, which is amazing to see. And so the, the project itself is adding a ton of features, reducing friction in terms of adoption, how people ride these control planes and alter them coverage of the space. As you know, controls are only useful when you connect them to things. And the space is like the amount of things you can connect control planes to is increasing on a day to day basis and the maturity is increasing. So it's just super exciting to see all of this right >>Now. How would you categorize the landscape? We were just talking earlier in another segment, we're in Detroit Motor City, you know, it's like teaching someone how to drive a car. Kubernetes pluss, okay, switch the gears like, you know, don't hit the other guy. You know? Now once you learn how to drive, they want a sports car. How do you keep them that progression going? How do you keep people to grow continuously? Where do you see the DevOps and or folks that are doing cross playing that are API hardcore? Cause that's a good IQ that shows 'em that they're advancing. Where's the IQ level of advancement relative to the industry? Is the adoption just like, you know, getting going? Are people advancing? Yeah. Sounds like your customers are heavily down the road on >>Yeah, the way I would describe it is there's a progression happening, right? It, it DevOps was make, initially it was like how do I keep things running right? And it transitioned to how do I automate things so that I don't have to be involved when things are running, running. Right now we're seeing a next turn, which is how do I build what looks like a product that offers shared services or a platform so that people consume it like a product, right? Yeah. And now I'm now transition becomes, well I'm an, I'm a developer on a product in operations building something that looks like a product and thinking about it as a, as a has a user interface. >>Ops of the new devs. >>That's correct. Yeah. There we go. >>Talk about layers. Talk about layers on layers on >>Layers. It's not confusing at all John. >>Well, you know, when they have the architecture architectural list product that's coming. Yeah. But this is what's, I mean the Debs are got so much DevOps in the front and the C I C D pipeline, the ops teams are now retrofitting themselves to be data and security mainly. And that's just guardrails, automation policy, seeing a lot of that kind of network. Like exactly. >>Function. >>Yep. And they're, they're composing, not maybe coding a little bit, but they not, they're not >>Very much. They're in the composition, you know that as a daily thing. They're, they're writing compositions, they're building things, they're putting them together and making them work. >>How new is this in your mind? Cause you, you've watching this progress, you're in the middle of it, you're in the front wave of this. Is it adopting faster now than ever before? I mean, if we talked five years ago, we were kind of saying this might happen, but it wasn't happening today. It kind, it is, >>It's kind of, it's kind of amazing. Like, like everybody's writing these cloud services now. Everybody's authoring things that look like API services that do things on top of the structure. That move is very much, has a ton of momentum right now and it's happening mainstream. It, it's becoming mainstream. >>Speaking of momentum, but some I saw both on your LinkedIn as well as on your badge today that you are hiring. This is your opportunity to shamelessly plug. What are you looking for? What can people expect in terms of your company culture? >>Yeah, so we're obviously hiring, we're hiring both on the go to market side or we're hiring on the product and engineering side. If you want to build, well a new cloud platform, I won't say the word super cloud again, but if you want to, if you're excited about building a cloud platform that literally sits on top of, you know, the other cloud platforms and offers services on top of this, come talk to us. We're building something amazing. >>You're creating a super cloud tool kit. I'll say it >>On that note, think John Farer has now managed to get seven uses of the word super cloud into this broadcast. We sawm tomorrow. Thank you so much for joining us today. It's been a pleasure. I can't wait to see more of you throughout the course of Cuban. My name is Savannah Peterson, everyone, and thank you so much for joining us here on the Cube where we'll be live from Detroit, Michigan all week.

>>Hello. And welcome back to the cubes live coverage here in Seattle for the cubes coverage of AWS marketplace seller conference. Now part of really big move and news, Amazon partner network combines with AWS marketplace to form one organization, the Amazon partner organization, APO where the efficiencies, the next iteration, as they say in Amazon language, where they make things better, simpler, faster, and, and for customers is happening. We're here with Chris Cruz, who's the general manager, worldwide leader of ISV alliances and marketplace, which includes all the channel partners and the buyer and seller relationships all now under one partner organization, bringing together years of work. Yes. If you work with AWS and are a partner and, or sell with them, all kind of coming together, kind of in a new way for the next generation, Chris, congratulations on the new role and the reor. >>Thank you. Yeah, it's very exciting. We're we think it invent, simplifies the process on how we work with our partners and we're really optimistic so far. The feedback's been great. And I think it's just gonna get even better as we kind of work out the final details. >>This is huge news because one, we've been very close to the partner that we've been working with and we talking to, we cover them. We cover the news, the startups from startups, channel partners, big ISVs, big and small from the dorm room to the board room. You guys have great relationships. So check marketplace, the future of procurement, how software will be bought, implemented and deployed is also changed. So you've got the confluence of two worlds coming together, growth in the ecosystem. Yep. NextGen cloud on the horizon for AWS and the customers as digital transformation goes from lift and shift to refactoring businesses. Yep. This is really a seminal moment. Can you share what you talked about on the keynote stage here, around why this is happening now? Yeah. What's the guiding principle. What's the north star where, why what's what's the big news. >>Yeah. And so, you know, a lot of reasons on why we kind of, we pulled the two teams together, but you know, a lot of it kind gets centered around co-sell. And so if you take a look at marketplace where we started off, where it was really a machine image business, and it was a great self-service model and we were working with ISVs that wanted to have this new delivery mechanism on how to bring in at the time was Amazon machine images and you fast forward, we started adding more product types like SAS and containers. And the experience that we saw was that customers would use marketplace for kind of up to a certain limit on a self-service perspective. But then invariably, they wanted by a quantity discount, they wanted to get an enterprise discount and we couldn't do that through marketplace. And so they would exit us and go do a direct deal with a, an ISV. >>And, and so to remedy that we launched private offers, you know, four years ago. And private offers now allowed ISVs to do these larger deals, but do 'em all through marketplace. And so they could start off doing self-service business. And then as a customer graduated up to buying for a full department or an organization, they can now use private offers to execute that larger agreement. And it, we started to do more and more private offers, really kind of coincided with a lot of the initiatives that were going on within Amazon partner network at the time around co-sell. And, and so we started to launch programs like ISV accelerate that really kind of focused on our co-sell relationship with ISVs. And what we found was that marketplace private offers became this awesome way to automate how we co-sell with ISV. And so we kinda had these two organizations that were parallel. We said, you know what, this is gonna be better together. If we put together, it's gonna invent simplify and we can use marketplace private offers as part of that co-sell experience and really feed that automation layer for all of our ISVs as they interacted with native >>Discussions. Well, I gotta give you props, you and Mona work on stage. You guys did a great job and it reminds me of the humble nature of AWS and Amazon. I used to talk to Andy jazzy about this all the time. That reminds me of 2013 here right now, because you're in that mode where Amazon reinvent was in 2013. Yeah. Where you knew it was breaking out. Yeah. Everyone's it was kind of small, but we haven't made it yet. Yeah. But you guys are doing billions of vows in transactions. Yeah. But this event is really, I think the beginning of what we're seeing as the change over from securing and deploying applications in the cloud, because there's a lot of nuanced things I want to get your reaction on one. I heard making your part product as an ISV, more native to AWS's stack. That was one major call out. I heard the other one was, Hey, if you're a channel partner, you can play too. And by the way, there's more choice. There's a lot going on here. That's about to kind of explode in a good way for customers. Yeah. Buyers get more access to assemble their solutions. Yeah. And you got all kinds of like business logic, compensation, integration, and scale. Yeah. This is like unprecedented. >>Yeah. It's, it's exciting to see what's going on. I mean, I think we kind of saw the tipping point probably about two years ago, which, you know, prior to that, you know, we would be working with ISVs and customers and it was really much more of an evangelism role where we were just getting people to try it. Just, just list a product. We think this is gonna be a good idea. And if you're a buyer, it's like just try out a private offer, try out a self, you know, service subscription. And, and what's happened now is there's no longer a lot of that convincing that needs to happen. It's really become accepted. And so a lot of the conversations I have now with ISVs, it's not about, should I do marketplace it's how do I do it better? And how do I really leverage marketplace as part of my co-sell initiatives as, as part of my go to market strategy. >>And so you've, you've really kind of passed this tipping point where marketplaces are now becoming very accepted ways to buy third party software. And so that's really exciting. And, and we see that we, you know, we can really enhance that experience, you know, and what we saw on the machine image side is we had this awesome integrated experience where you would buy it. It was tied right into the EC two control plane. And you could go from buying to deploying in one single motion. SAS is a little bit different, you know, we can do all the buying in a very simple motion, but then deploying it. There's a whole bunch of other stuff that our customers have to do. And so we see all kinds of ways that we can simplify that. You know, recently we launched the ability to put third party solutions outta marketplace, into control tower, which is how we deploy all of our landing zones for AWS. And now it's like, instead of having to go wire that up as you're adding new AWS environments, why not just use that third party solution that you've already integrated to you and have it there as you're span those landing zones through >>Control towers, again, back to humble nature, you guys have dominated the infrastructure as a service layer. You kind of mentioned it. You didn't really kind of highlight it other than saying you're doing pretty good. Yeah. On the IAS or the technology partners as you call or infrastructure as you guys call it. Okay. I can see how the, the, the pan, the control panel is great for those customers. But outside that, when you get into like CRM, you mentioned E R P these business apps, these horizontal and verticals have data they're gonna have SageMaker, they're gonna have edge. They might have, you know, other services that are coming online from Amazon. How do I, as an ISV, get my stuff in there. Yeah. And how do I succeed? And what are you doing to make that better? Cause I know it's kind of new, but not new. Yeah, >>No, it's not. I mean, that's one of the things that we've really invested on is how do we make it really easy to list marketplace? And, you know, again, when we first start started, it was a big, huge spreadsheet that you had to fill out. It was very cumbersome and we've really automated all those aspects. So now we've exposed an API as an example. So you can go straight out of your own build process and you might have your own C I CD pipeline. And then you have a build step at the end. And now you can have that execute marketplace update from your build script, right across that API all the way over to AWS marketplace. So it's taking that effectively, a C CD pipeline from an ISV and extending it all the way to AWS and then eventually to a customer, because now it's just an automated supply chain for that software coming into their environment. And we see that being super powerful. There's nowhere manual steps >>Along. Yeah. I wanna dig into that because you made a comment and I want you to clarify it here in the cube. Some have said, even us on the cube. Oh, marketplace. Just the website's a catalog. Yeah. Feels old school. Yeah. Feels like 1995 database. I'm kind of just, you know, saying no offense sake. And now you're saying, you're now looking at this and, and implementing more of a API based. Why is that relevant? I'm I know the answer. You already set up with APIs, but explain the transition from the mindset of it's a website. Yeah. Buy stuff on a catalog to full blown API layer. Yeah. Services. >>Absolutely. Well, when you look at all AWS services, you know, our customers will interface, you know, they'll interface them through a console initially, but when they're using them in production, they're, it's all about APIs and marketplace, as you mentioned, did start off as a website. And so we've kind of taken the opposite approach. We've got this great website experience, which is great for demand gen and, you know, highlighting those listings. But what we want to do is really have this API service layer that you're interfacing with so that an ISV effectively is not even in our marketplace. They interfacing over APIs to do a variety of their high, you know, value functions, whether it's listing soy, private offers. We don't have that all available through APIs and the same thing on the buyer side. So it's integrating directly into their AWS environment and then they can view all their third party spend within things like our cost management suites. They can look at things like cost Explorer, see third party software, right next to first party software, and have that all integrated this nice as seamless >>For the customer. That's a nice cloud native kind of native experience. I think that's a huge advantage. I'm gonna track that closer. We're we're gonna follow that. I think that's gonna be the killer killer feature. All right. Now let's get to the killer feature and the business logic. Okay. Yeah. All partners all wanna know what's in it for me. Yeah. How do I make more cash? Yeah. How do I compensate my sales people? Yeah. What do you guys don't compete with me? Give me leads. Yeah. Can I get MDF market development funds? Yeah. So take me through the, how you're thinking about supporting the partners that are leaning in that, you know, the parachute will open when they jump outta the plane. Yeah. It's gonna be, they're gonna land safely with you. Yeah. MDF marketing to leads. What are you doing to support the partners to help them serve their >>Customers? It's interesting. Market marketplace has become much more of an accepted way to buy, you know, our customers are, are really defaulting to that as the way to go get that third party software. So we've had some industry analysts do some studies and in what they found, they interviewed a whole cohort of ISVs across various categories within marketplace, whether it was security or network or even line of business software. And what they've found is that on average, our ISVs will see a 24% increased close rate by using marketplace. Right. So when I go talk to a CRO and say, do you want to close, you know, more deals? Yes. Right. And we've got data to show that we're also finding that customers on average, when an ISV sales marketplace, they're seeing an 80% uplift in the actual deal size. And so if your ASP is a hundred K 180 K has a heck of a lot better, right? >>So we're seeing increased deal sizes by going through marketplace. And then the third thing that we've seen, that's a value prop for ISVs is speed of closure. And so on average, what we're finding is that our ISVs are closing deals 40% faster by using marketplace. So if you've got a 10 month sales cycle, shaving four months off of a sales cycle means you're bringing deals in, in an earlier calendar year, earlier quarter. And for ISVs getting that cash flow early is very important. So those are great metrics that we're seeing. And, and, you know, we think that they're only >>Gonna improve and from startups who also want, they don't have a lot of cash ISVs that are rich and doing well. Yeah. They have good, good, good, good, good to market funding. Yeah. You got the range of partners and you know, the next startup could be the next Figma could be in that batch startups. Exactly. Yeah. You don't know the game is changing. Yeah. The next brand could be one of those batch of startups. Yeah. What's the message to the startup community. Yeah. >>I mean, marketplace in a lot of ways becomes a level in effect, right. Because, you know, if, if you look at pre marketplace, if you were a startup, you were having to go generate sales, have a sales force, go compete, you know, kind of hand to hand with these largest ISVs marketplace is really kind of leveling that because now you can both list in marketplace. You have the same advantage of putting that directly in the AWS bill, taking advantage of all the management go features that we offer all the automation that we bring to the table. And so >>A lot of us joint selling >>And joint selling, right? When it goes through marketplace, you know, it's gonna feed into a number of our APN programs like ISV accelerate, our sales teams are gonna get recognized for those deals. And so, you know, it brings nice co-sell behavior to how we work with our, our field sales teams together. It brings nice automation that, you know, pre marketplaces, they would have to go build all that. And that was a heavy lift that really now becomes just kind of table stakes for any kind of ISV selling to an, any of >>Customer. Well, you know, I'm a big fan of the marketplace. I've always have been, even from the early days, I saw this as a procurement game changer. It makes total sense. It's so obvious. Yeah. Not obvious to everyone, but there's a lot of moving parts behind the scenes behind the curtain. So to speak that you're handling. Yeah. What's your message to the audience out there, both the buyers and the sellers. Yeah. About what your mission is, what you're you wake up every day thinking about. Yeah. And what's your promise to them and what you're gonna work on. Cause it's not easy. You're building a, an operating model. That's not a website. It's a full on cloud service. Yeah. What's your promise. And what's >>Your goals. No. And like, you know, ultimately we're trying to do from an Aus market perspective is, is provide that selection experience to the ABUS customer, right? There's the infamous flywheel that Jeff put together that had the concepts of why Amazon is successful. And one are the concepts he points to is the concept of selection. And, and what we mean by that is if you come to Amazon it's is effectively that everything stored. And when you come across, AWS marketplace becomes that selection experience. And so that's what we're trying to do is provide whatever our AWS customers wanna buy, whatever form factor, whatever software type, whatever data type it's gonna be available in AWS marketplace for consumption. And that ultimately helps our customers because now they can get whatever technologies that they need to use alongside Avis. >>And I want, wanna give you props too. You answered the hard question on stage. I've asked Andy EY this on the cube when he was the CEO, Adam Celski last year, I asked him the same question and the answer has been consistent. We have some solutions that people want a AWS end to end, but your ecosystem, you want people to compete yes. And build a product and mostly point to things like snowflake, new Relic. Yeah. Other people that compete with Amazon services. Yeah. You guys want that. You encourage that. Yeah. You're ratifying that same statement. >>Absolutely. Right. Again, it feeds into that selection experience. Right. If a customer wants something, we wanna make sure it's gonna be a great experience. Right. And so a lot of these ISVs are building on top of AWS. We wanna make sure that they're successful. And, you know, while we have a number of our first party services, we have a variety of third party technologies that run very well in a AWS. And ultimately the customer's gonna make their decision. We're customer obsessed. And if they want to go with a third party product, we're absolutely gonna support them in every way shape we can and make sure that's a successful experience for our customers. >>I, I know you referenced two studies check out the website's got buyer and seller surveys on there for Boer. Yeah. I don't want to get into that. I want to just end on one. Yeah. Kind of final note, you got a lot of successful buyers and a lot of successful sellers. The word billions, yes. With an S was and the slide. Can you say the number, how much, how many billions are sold yeah. Through the marketplace. Yeah. And the buyer experience future what's those two things. >>Yeah. So we went on record at reinvent last year, so it's approaching it birthday, but it was the first year that we've in our 10 year history announced how much was actually being sold to the marketplace. And, you know, we are now selling billions of dollars to our marketplace and that's with an S so you can assume, at least it's two, but it's, it's a, it's a large number and it's going >>Very quickly. Yeah. Can't disclose, you know, >>But it's a, it's been a very healthy part of our business. And you know, we look at this, the experience that we >>Saw, there's a lot of headroom. I mean, oh yeah, you have infrastructure nailed down. That's long, you get better, but you have basically growth up upside with these categor other categories. What's the hot categories. You >>Know, we, we started off with infrastructure related products and we've kind of hit critical mass there. Right? We've, there's very few ISVs left that are in that infrastructure related space that are not in our marketplace. And what's happened now is our customers are saying, well, I've been buying infrastructure products for years. I'm gonna buy everything. I wanna buy my line of business software. I wanna buy my vertical solutions. I wanna buy my data and I wanna buy all my services alongside of that. And so there's tons of upside. We're seeing all of these either horizontal business applications coming to our marketplace or vertical specific solutions. Yeah. Which, you know, when we first designed our marketplace, we weren't sure if that would ever happen. We're starting to see that actually really accelerate because customers are now just defaulting to buying everything through their marketplace. >>Chris, thanks for coming on the queue. I know we went a little extra long. There wanted to get that clarification on the new role. Yeah. New organization. Great, great reorg. It makes a lot of sense. Next level NextGen. Thanks for coming on the cube. Okay. >>Thank you for the opportunity. >>All right here, covering the new big news here of AWS marketplace and the AWS partner network coming together under one coherent organization, serving fires and sellers, billions sold the future of how people are gonna be buying software, deploying it, managing it, operating it. It's all happening in the marketplace. This is the big trend. It's the cue here in Seattle with more coverage here at Davis marketplace sellers conference. After the short break.

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back everybody Jeff Frick here with the cube. This is a special week it's Grace Hopper week, and Grace Hopper is the best name in tech conferences. The celebration of women in computing, and we've been going there for years we're not there this year, but one of the themes that comes up over and over at Grace Hopper is women and girls need to see women in positions that they can envision themselves being in someday. That is a really important piece of the whole diversity conversation is can I see people that I can role model after and I just want to bring up something from a couple years back from 2016 when we were there, we were there with Mimi Valdez, Christina Deoja and Dr. Jeanette Epps, Dr. Jeanette Epps is the astronaut on the right. They were there talking about "The Hidden Figures" movie. If you remember it came out 2016, it was about Katherine Johnson and all the black women working at NASA. They got no credit for doing all the math that basically keep all the astronauts safe and they made a terrific movie about it. And Janet is going up on the very first Blue Origin Space Mission Next year. This was announced a couple of months ago, so again, phenomenal leadership, black lady astronaut, going to go into space and really provide a face for a lot of young girls that want to get into that and its clearly a great STEM opportunity. So we're excited to have four terrific women today that well also are the leaders that the younger women can look up to and follow their career. So we're excited to have them so we're just going to go around. We got four terrific guests, our first one is Annabel Chang, She is the Head of State Policy and Government Regulations at Waymo. Annabel great to see you, where are you coming in from today? >> from San Francisco >> Jeff: Awesome. Next up is Inamarie Johnson. She is the Chief People and Diversity Officer for Zendesk Inamarie, great to see you. Where are you calling in from today? >> Great to be here. I am calling in from Palos Verdes the state >> Jeff: awesome >> in Southern California. >> Jeff: Some of the benefits of a virtual sometimes we can, we couldn't do that without the power of the internet. And next up is Jennifer Cabalquinto she is the Chief Financial Officer of the Golden State Warriors. Jennifer, great to see you Where are you coming in from today? >> Well, I wish I was coming in from the Chase Center in San Francisco but I'm actually calling in from Santa Cruz California today. >> Jeff: Right, It's good to see you and you can surf a lot better down there. So that's probably not all bad. And finally to round out our panelists, Kate Hogan, she is the COO of North America for Accenture. Kate, great to see you as well. Where are you coming in from today? >> Well, it's good to see you too. I am coming in from the office actually in San Jose. >> Jeff: From the office in San Jose. All right, So let's get into it . You guys are all very senior, you've been doing this for a long time. We're in a kind of a crazy period of time in terms of diversity with all the kind of social unrest that's happening. So let's talk about some of your first your journeys and I want to start with you Annabel. You're a lawyer you got into lawyering. You did lawyering with Diane Feinstein, kind of some politics, and also the city of San Francisco. And then you made this move over to tech. Talk about that decision and what went into that decision and how did you get into tech? 'cause we know part of the problem with diversity is a pipeline problem. You came over from the law side of the house. >> Yes, and to be honest politics and the law are pretty homogenous. So when I made the move to tech, it was still a lot of the same, but what I knew is that I could be an attorney anywhere from Omaha Nebraska to Miami Florida. But what I couldn't do was work for a disruptive company, potentially a unicorn. And I seized that opportunity and (indistinct) Lyft early on before Ride Hailing and Ride Sharing was even a thing. So it was an exciting opportunity. And I joined right at the exact moment that made myself really meaningful in the organization. And I'm hoping that I'm doing the same thing right now at Waymo. >> Great, Inamarie you've come from one of my favorite stories I like to talk about from the old school Clorox great product management. I always like to joke that Silicon Valley needs a pipeline back to Cincinnati and Proctor and Gamble to get good product managers out here. You were in the classic, right? You were there, you were at Honeywell Plantronics, and then you jumped over to tech. Tell us a little bit about that move. Cause I'm sure selling Clorox is a lot different than selling the terrific service that you guys provide at Zendesk. I'm always happy when I see Zendesk in my customer service return email, I know I'm going to get taken care of. >> Oh wow, that's great. We love customers like you., so thank you for that. My journey is you're right from a fortune 50 sort of more portfolio type company into tech. And I think one of the reasons is because when tech is starting out and that's what Zendesk was a few five years back or so very much an early stage growth company, two things are top of mind, one, how do we become more global? And how do we make sure that we can go up market and attract enterprise grade customers? And so my experience having only been in those types of companies was very interesting for a startup. And what was interesting for me is I got to live in a world where there were great growth targets and numbers, things I had never seen. And the agility, the speed, the head plus heart really resonated with my background. So super glad to be in tech, but you're right. It's a little different than a consumer products. >> Right, and then Jennifer, you're in a completely different world, right? So you worked for the Golden State Warriors, which everybody knows is an NBA team, but I don't know that everyone knows really how progressive the Warriors are beyond just basketball in terms of the new Chase Center, all the different events that you guys put on it. And really the leadership there has decided we really want to be an entertainment company of which the Golden State Warrior basketball team has a very, very important piece, you've come from the entertainment industry. So that's probably how they found you, but you're in the financial role. You've always been in the financial role, not traditionally thought about as a lot of women in terms of a proportion of total people in that. So tell us a little bit about your experience being in finance, in entertainment, and then making this kind of hop over to, I guess Uber entertainment. I don't know even how you would classify the warriors. >> Sports entertainment, live entertainment. Yeah, it's interesting when the Warriors opportunity came up, I naturally said well no, I don't have any sports background. And it's something that we women tend to do, right? We self edit and we want to check every box before we think that we're qualified. And the reality is my background is in entertainment and the Warriors were looking to build their own venue, which has been a very large construction project. I was the CFO at Universal Studios Hollywood. And what do we do there? We build large attractions, which are just large construction projects and we're in the entertainment business. And so that sort of B to C was a natural sort of transition for me going from where I was with Universal Studios over to the Warriors. I think a finance career is such a great career for women. And I think we're finding more and more women entering it. It is one that you sort of understand your hills and valleys, you know when you're going to be busy and so you can kind of schedule around that. I think it's really... it provides that you have a seat at the table. And so I think it's a career choice that I think is becoming more and more available to women certainly more now than it was when I first started. >> Yeah, It's interesting cause I think a lot of people think of women naturally in human resources roles. My wife was a head of human resources back in the day, or a lot of marketing, but not necessarily on the finance side. And then Kate go over to you. You're one of the rare birds you've been at Accenture  for over 20 years. So you must like airplanes and travel to stay there that long. But doing a little homework for this, I saw a really interesting piece of you talking about your boss challenging you to ask for more work, to ask for a new opportunity. And I thought that was really insightful that you, you picked up on that like Oh, I guess it's incumbent on me to ask for more, not necessarily wait for that to be given to me, it sounds like a really seminal moment in your career. >> It was important but before I tell you that story, because it was an important moment of my career and probably something that a lot of the women here on the panel here can relate to as well. You mentioned airplanes and it made me think of my dad. My father was in the air force and I remember him telling stories when I was little about his career change from the air force into a career in telecommunications. So technology for me growing up Jeff was, it was kind of part of the dinner table. I mean it was just a conversation that was constantly ongoing in our house. And I also, as a young girl, I loved playing video games. We had a Tandy computer down in the basement and I remember spending too many hours playing video games down there. And so for me my history and my really at a young age, my experience and curiosity around tech was there. And so maybe that's, what's fueling my inspiration to stay at Accenture for as long as I have. And you're right It's been two decades, which feels tremendous, but I've had the chance to work across a bunch of different industries, but you're right. I mean, during that time and I relate with what Jennifer said in terms of self editing, right? Women do this and I'm no exception, I did this. And I do remember I'm a mentor and a sponsor of mine who called me up when I'm kind of I was at a pivotal moment in my career and he said you know Kate, I've been waiting for you to call me and tell me you want this job. And I never even thought about it. I mean I just never thought that I'd be a candidate for the job and let alone somebody waiting for me to kind of make the phone call. I haven't made that mistake again, (laughing) but I like to believe I learned from it, but it was an important lesson. >> It's such a great lesson and women are often accused of being a little bit too passive and not necessarily looking out for in salary negotiations or looking for that promotion or kind of stepping up to take the crappy job because that's another thing we hear over and over from successful people is that some point in their career, they took that job that nobody else wanted. They took that challenge that really enabled them to take a different path and really a different Ascension. And I'm just curious if there's any stories on that or in terms of a leader or a mentor, whether it was in the career, somebody that you either knew or didn't know that was someone that you got kind of strength from kind of climbing through your own, kind of career progression. Will go to you first Annabel. >> I actually would love to talk about the salary negotiations piece because I have a group of friends about that we've been to meeting together once a month for the last six years now. And one of the things that we committed to being very transparent with each other about was salary negotiations and signing bonuses and all of the hard topics that you kind of don't want to talk about as a manager and the women that I'm in this group with span all types of different industries. And I've learned so much from them, from my different job transitions about understanding the signing bonus, understanding equity, which is totally foreign to me coming from law and politics. And that was one of the most impactful tools that I've ever had was a group of people that I could be open with talking about salary negotiations and talking about how to really manage equity. Those are totally foreign to me up until this group of women really connected me to these topics and gave me some of that expertise. So that is something I strongly encourage is that if you haven't openly talked about salary negotiations before you should begin to do so. >> It begs the question, how was the sensitivity between the person that was making a lot of money and the person that wasn't? And how did you kind of work through that as a group for the greater good of everyone? >> Yeah, I think what's really eye opening is that for example, We had friends who were friends who were on tech, we had friends who were actually the entrepreneurs starting their own businesses or law firm, associates, law firm partners, people in PR, so we understood that there was going to be differences within industry and frankly in scale, but it was understanding even the tools, whether I think the most interesting one would be signing bonus, right? Because up until a few years ago, recruiters could ask you what you made and how do you avoid that question? How do you anchor yourself to a lower salary range or avoid that happening? I didn't know this, I didn't know how to do that. And a couple of women that had been in more senior negotiations shared ways to make sure that I was pinning myself to a higher salary range that I wanted to be in. >> That's great. That's a great story and really important to like say pin. it's a lot of logistical details, right? You just need to learn the techniques like any other skill. Inamarie, I wonder if you've got a story to share here. >> Sure. I just want to say, I love the example that you just gave because it's something I'm super passionate about, which is transparency and trust. Then I think that we're building that every day into all of our people processes. So sure, talk about sign on bonuses, talk about pay parody because that is the landscape. But a quick story for me, I would say is all about stepping into uncertainty. And when I coach younger professionals of course women, I often talk about, don't be afraid to step into the role where all of the answers are not vetted down because at the end of the day, you can influence what those answers are. I still remember when Honeywell asked me to leave the comfort of California and to come to the East coast to New Jersey and bring my family. And I was doing well in my career. I didn't feel like I needed to do that, but I was willing after some coaching to step into that uncertainty. And it was one of the best pivotal moment in my career. I didn't always know who I was going to work with. I didn't know the challenges and scope I would take on, but those were some of the biggest learning experiences and opportunities and it made me a better executive. So that's always my coaching, like go where the answers aren't quite vetted down because you can influence that as a leader. >> That's great, I mean, Beth Comstock former vice chair at GE, one of her keynotes I saw had a great line, get comfortable with being uncomfortable. And I think that its a really good kind of message, especially in the time we're living in with accelerated change. But I'm curious, Inamarie was the person that got you to take that commitment. Would you consider that a sponsor, a mentor, was it a boss? Was it maybe somebody not at work, your spouse or a friend that said go for it. What kind of pushed you over the edge to take that? >> It's a great question. It was actually the boss I was going to work for. He was the CHRO, and he said something that was so important to me that I've often said it to others. And he said trust me, he's like I know you don't have all the answers, I know we don't have this role all figured out, I know you're going to move your family, but if you trust me, there is a ton of learning on the other side of this. And sometimes that's the best thing a boss can do is say we will go on this journey together. I will help you figure it out. So it was a boss, but I think it was that trust and that willingness for him to stand and go alongside of me that made me pick up my family and be willing to move across the country. And we stayed five years and really, I am not the same executive because of that experience. >> Right, that's a great story, Jennifer, I want to go to you, you work for two owners that are so progressive and I remember when Joe Lacob came on the floor a few years back and was booed aggressively coming into a franchise that hadn't seen success in a very long time, making really aggressive moves in terms of personnel, both at the coaches and the players level, the GM level. But he had a vision and he stuck to it. And the net net was tremendous success. I wonder if you can share any of the stories, for you coming into that organization and being able to feel kind of that level of potential success and really kind of the vision and also really a focus on execution to make the vision real cause vision without execution doesn't really mean much. If you could share some stories of working for somebody like Joe Lacob, who's so visionary but also executes so very, very effectively. >> Yeah, Joe is, well I have the honor of working for Joe, for Rick Welts to who's our president. Who's living legend with the NBA with Peter Guber. Our leadership at the Warriors are truly visionary and they set audacious targets. And I would say from a story the most recent is, right now what we're living through today. And I will say Joe will not accept that we are not having games with fans. I agree he is so committed to trying to solve for this and he has really put the organization sort of on his back cause we're all like well, what do we do? And he has just refused to settle and is looking down every path as to how do we ensure the safety of our fans, the safety of our players, but how do we get back to live entertainment? And this is like a daily mantra and now the entire organization is so focused on this and it is because of his vision. And I think you need leaders like that who can set audacious goals, who can think beyond what's happening today and really energize the entire organization. And that's really what he's done. And when I talked to my peers and other teams in there they're talking about trying to close out their season or do these things. And they're like well, we're talking about, how do we open the building? And we're going to have fans, we're going to do this. And they look at me and they're like, what are you talking about? And I said, well we are so fortunate. We have leadership that just is not going to settle. Like they are just always looking to get out of whatever it is that's happening and fix it. So Joe is so committed His background, he's an epidemiologist major I think. Can you imagine how unique a background that is and how timely. And so his knowledge of just around the pandemic and how the virus is spread. And I mean it's phenomenal to watch him work and leverage sort of his business acumen, his science acumen and really think through how do we solve this. Its amazing. >> The other thing thing that you had said before is that you basically intentionally told people that they need to rethink their jobs, right? You didn't necessarily want to give them permission to get you told them we need to rethink their jobs. And it's a really interesting approach when the main business is just not happening, right? There's just no people coming through the door and paying for tickets and buying beers and hotdogs. It's a really interesting talk. And I'm curious, kind of what was the reception from the people like hey, you're the boss, you just figure it out or were they like hey, this is terrific that he pressed me to come up with some good ideas. >> Yeah, I think when all of this happened, we were resolved to make sure that our workforce is safe and that they had the tools that they needed to get through their day. But then we really challenged them with re imagining what the next normal is. Because when we come out of this, we want to be ahead of everybody else. And that comes again from the vision that Joe set, that we're going to use this time to make ourselves better internally because we have the time. I mean, we had been racing towards opening Chase Center and not having time to pause. Now let's use this time to really rethink how we're doing business. What can we do better? And I think it's really reinvigorated teams to really think and innovate in their own areas because you can innovate anything, right?. We're innovating how you pay payables, we're all innovating, we're rethinking the fan experience and queuing and lines and all of these things because now we have the time that it's really something that top down we want to come out of this stronger. >> Right, that's great. Kate I'll go to you, Julie Sweet, I'm a big fan of Julie Sweet. we went to the same school so go go Claremont. But she's been super aggressive lately on a lot of these things, there was a get to... I think it's called Getting to 50 50 by 25 initiative, a formal initiative with very specific goals and objectives. And then there was a recent thing in terms of doing some stuff in New York with retraining. And then as you said, military being close to your heart, a real specific military recruiting process, that's formal and in place. And when you see that type of leadership and formal programs put in place not just words, really encouraging, really inspirational, and that's how you actually get stuff done as you get even the consulting businesses, if you can't measure it, you can't improve it. >> Yeah Jeff, you're exactly right. And as Jennifer was talking, Julie is exactly who I was thinking about in my mind as well, because I think it takes strong leadership and courage to set bold bold goals, right? And you talked about a few of those bold goals and Julie has certainly been at the forefront of that. One of the goals we set in 2018 actually was as you said to achieve essentially a gender balance workforce. So 50% men, 50% women by 2025, I mean, that's ambitious for any company, but for us at the time we were 400,000 people. They were 500, 6,000 globally. So when you set a goal like that, it's a bold goal and it's a bold vision. And we have over 40% today, We're well on our path to get to 50%, I think by 2025. And I was really proud to share that goal in front of a group of 200 clients the day that it came out, it's a proud moment. And I think it takes leaders like Julie and many others by the way that are also setting bold goals, not just in my company to turn the dial here on gender equality in the workforce, but it's not just about gender equality. You mentioned something I think it's probably at as, or more important right now. And that's the fact that at least our leadership has taken a Stand, a pretty bold stand against social injustice and racism, >> Right which is... >> And so through that we've made some very transparent goals in North America in terms of the recruitment and retention of our black African American, Hispanic American, Latinex communities. We've set a goal to increase those populations in our workforce by 60% by 2025. And we're requiring mandatory training for all of our people to be able to identify and speak up against racism. Again, it takes courage and it takes a voice. And I think it takes setting bold goals to make a change and these are changes we're committed to. >> Right, that's terrific. I mean, we started the conversation with Grace Hopper, they put out an index for companies that don't have their own kind of internal measure to do surveys again so you can get kind of longitudinal studies over time and see how you're improving Inamarie, I want to go to you on the social justice thing. I mean, you've talked a lot about values and culture. It's a huge part of what you say. And I think that the quote that you use, if I can steal it is " no culture eats strategy for breakfast" and with the social injustice. I mean, you came out with special values just about what Zendesk is doing on social injustice. And I thought I was actually looking up just your regular core mission and value statement. And this is what came up on my Google search. So I wanted to A, you published this in a blog in June, taking a really proactive stand. And I think you mentioned something before that, but then you're kind of stuck in this role as a mind reader. I wonder if you can share a little bit of your thoughts of taking a proactive stand and what Zendesk is doing both you personally, as well as a company in supporting this. And then what did you say as a binder Cause I think these are difficult kind of uncharted waters on one hand, on the other hand, a lot of people say, hello, this has been going on forever. You guys are just now seeing cellphone footage of madness. >> Yeah Wow, there's a lot in there. Let me go to the mind reader comments, cause people are probably like, what is that about? My point was last December, November timing. I've been the Chief People Officer for about two years And I decided that it really was time with support from my CEO that Zendesk have a Chief Diversity Officer sitting in at the top of the company, really putting a face to a lot of the efforts we were doing. And so the mind reader part comes in little did I know how important that stance would become, in the may June Timing? So I joked that, it almost felt like I could have been a mind reader, but as to what have we done, a couple of things I would call out that I think are really aligned with who we are as a company because our culture is highly threaded with the concept of empathy it's been there from our beginning. We have always tried to be a company that walks in the shoes of our customers. So in may with the death of George Floyd and the world kind of snapping and all of the racial injustice, what we said is we wanted to not stay silent. And so most of my postings and points of view were that as a company, we would take a stand both internally and externally and we would also partner with other companies and organizations that are doing the big work. And I think that is the humble part of it, we can't do it all at Zendesk, we can't write all the wrongs, but we can be in partnership and service with other organizations. So we used funding and we supported those organizations and partnerships. The other thing that I would say we did that was super important along that empathy is that we posted space for our employees to come together and talk about the hurt and the pain and the experiences that were going on during those times and we called those empathy circles. And what I loved is initially, it was through our mosaic community, which is what we call our Brown and black and persons of color employee resource group. But it grew into something bigger. We ended up doing five of these empathy circles around the globe and as leadership, what we were there to do is to listen and stand as an ally and support. And the stories were life changing. And the stories really talked about a number of injustice and racism aspects that are happening around the world. And so we are committed to that journey, we will continue to support our employees, we will continue to partner and we're doing a number of the things that have been mentioned. But those empathy circles, I think were definitely a turning point for us as an organization. >> That's great, and people need it right? They need a place to talk and they also need a place to listen if it's not their experience and to be empathetic, if you just have no data or no knowledge of something, you need to be educated So that is phenomenal. I want to go to you Jennifer. Cause obviously the NBA has been very, very progressive on this topic both as a league, and then of course the Warriors. We were joking before. I mean, I don't think Steph Curry has ever had a verbal misstep in the history of his time in the NBA, the guy so eloquent and so well-spoken, but I wonder if you can share kind of inside the inner circle in terms of the conversations, that the NBA enabled right. For everything from the jerseys and going out on marches and then also from the team level, how did that kind of come down and what's of the perception inside the building? >> Sure, obviously I'm so proud to be part of a league that is as progressive and has given voice and loud, all the teams, all the athletes to express how they feel, The Warriors have always been committed to creating a diverse and equitable workplace and being part of a diverse and equitable community. I mean that's something that we've always said, but I think the situation really allowed us, over the summer to come up with a real formal response, aligning ourselves with the Black Lives Matter movement in a really meaningful way, but also in a way that allows us to iterate because as you say, it's evolving and we're learning. So we created or discussed four pillars that we wanted to work around. And that was really around wallet, heart, beat, and then tongue or voice. And Wallet is really around putting our money where our mouth is, right? And supporting organizations and groups that aligned with the values that we were trying to move forward. Heart is around engaging our employees and our fan base really, right? And so during this time we actually launched our employee resource groups for the first time and really excited and energized about what that's doing for our workforce. This is about promoting real action, civic engagement, advocacy work in the community and what we've always been really focused in a community, but this really hones it around areas that we can all rally around, right? So registration and we're really focused on supporting the election day results in terms of like having our facilities open to all the electorate. So we're going to have our San Francisco arena be a ballot drop off, our Oakland facilities is a polling site, Santa Cruz site is also a polling location, So really promoting sort of that civic engagement and causing people to really take action. heart is all around being inclusive and developing that culture that we think is really reflective of the community. And voice is really amplifying and celebrating one, the ideas, the (indistinct) want to put forth in the community, but really understanding everybody's culture and really just providing and using the platform really to provide a basis in which as our players, like Steph Curry and the rest want to share their own experiences. we have a platform that can't be matched by any pedigree, right? I mean, it's the Warriors. So I think really getting focused and rallying around these pillars, and then we can iterate and continue to grow as we define the things that we want to get involved in. >> That's terrific. So I have like pages and pages and pages of notes and could probably do this for hours and hours, but unfortunately we don't have that much time we have to wrap. So what I want to do is give you each of you the last word again as we know from this problem, right? It's not necessarily a pipeline problem, it's really a retention problem. We hear that all the time from Girls in Code and Girls in Tech. So what I'd like you to do just to wrap is just a couple of two or three sentences to a 25 year old, a young woman sitting across from you having coffee socially distanced about what you would tell her early in the career, not in college but kind of early on, what would the be the two or three sentences that you would share with that person across the table and Annabel, we'll start with you. >> Yeah, I will have to make a pitch for transportation. So in transportation only 15% of the workforce is made up of women. And so my advice would be that there are these fields, there are these opportunities where you can make a massive impact on the future of how people move or how they consume things or how they interact with the world around them. And my hope is that being at Waymo, with our self driving car technology, that we are going to change the world. And I am one of the initial people in this group to help make that happen. And one thing that I would add is women spend almost an hour a day, shuttling their kids around, and we will give you back that time one day with our self driving cars so that I'm a mom. And I know that that is going to be incredibly powerful on our daily lives. >> Jeff: That's great. Kate, I think I might know what you're already going to say, but well maybe you have something else you wanted to say too. >> I don't know, It'll be interesting. Like if I was sitting across the table from a 25 year old right now I would say a couple of things first I'd say look intentionally for a company that has an inclusive culture. Intentionally seek out the company that has an inclusive culture, because we know that companies that have inclusive cultures retain women in tech longer. And the companies that can build inclusive cultures will retain women in tech, double, double the amount that they are today in the next 10 years. That means we could put another 1.4 million women in tech and keep them in tech by 2030. So I'd really encourage them to look for that. I'd encouraged them to look for companies that have support network and reinforcements for their success, and to obviously find a Waymo car so that they can not have to worry where kids are on for an hour when you're parenting in a few years. >> Jeff: I love the intentional, it's such a great word. Inamarie, >> I'd like to imagine that I'm sitting across from a 25 year old woman of color. And what I would say is be authentically you and know that you belong in the organization that you are seeking and you were there because you have a unique perspective and a voice that needs to be heard. And don't try to be anything that you're not, be who you are and bring that voice and that perspective, because the company will be a better company, the management team will be a better management team, the workforce will be a better workforce when you belong, thrive and share that voice. >> I love that, I love that. That's why you're the Chief People Officer and not Human Resources Officer, cause people are not resources like steel and cars and this and that. All right, Jennifer, will go to you for the wrap. >> Oh my gosh, I can't follow that. But yes, I would say advocate for yourself and know your value. I think really understanding what you're worth and being willing to fight for that is critical. And I think it's something that women need to do more. >> Awesome, well again, I wish we could go all day, but I will let you get back to your very, very busy day jobs. Thank you for participating and sharing your insight. I think it's super helpful. And there and as we said at the beginning, there's no better example for young girls and young women than to see people like you in leadership roles and to hear your voices. So thank you for sharing. >> Thank you. >> All right. >> Thank you. >> Okay thank you. >> Thank you >> All right, so that was our diversity panel. I hope you enjoyed it, I sure did. I'm looking forward to chapter two. We'll get it scheduled as soon as we can. Thanks for watching. We'll see you next time. (upbeat music)

>> from San Francisco. It's the Cube covering the em. Where Radio twenty nineteen brought to you by the PM where >> hi. Welcome to the Cube. Lisa Martin with John Furrier way are in the middle of the excitement and the action at the, um where Radio twenty nineteen in San Francisco. Please welcome back to the Cube. David Tennant House, the chief research officer at the end. Where. David Welcome back. >> Thank you. It's always great to have the Cube here radio, >> and it's we had in a really exciting day. And then suddenly this whole space opens up and you can imagine all the innovation and the collaboration that's going on in here. This is the fifteenth radio. This is just one of several big programs that the M where does that really inspires and fosters this really collaborative, innovative culture? You've been here for five years. You came from Microsoft tells a little bit about what makes not just radio, but the emir's culture of innovation unique and really gives it some competitive advantage in the market. >> Yeah, well, so that you know, I think there's a number of different things there. People are super passionate about technology I think there's also this shared thing at P m. Where which is, you know, we're a little understated, right? We're not a big consumer brand. And, you know, we almost pride ourselves in creating technology that goes under the covers, right? So whether it's inside the data center, you know, can we make, you know, with virtual ization, right, Khun? Oui. Make it so that you can run ten times as many virtual machines as you had physical machines and the applications never have to know, right? So that's kind of, you know, for us, it's perfect, technically hard problem and, you know, a little understated. So that kind of, you know, fits with our culture. I think another thing that we found, you know, having a research group often a challenge. His researchers will go to people in the product teams, and they sort of want to start the discussion. I've got this new idea, and maybe it could really help you with your product. And, you know, meanwhile, of course, the product people are, you know, they're working against deadlines. They want to get stuff out. They don't want people derailing their, you know, their agenda and their work. So something we find at PM where which is really I find unique, is let's say we goto a product team in many other companies environments, and I'm really not naming anyone. What happens is you gotta have a discussion with somebody who sort of, you know, is the expert on whatever name your technology and you say the reason starting point isn't Hey, I've got this whole new way of doing your stuff, right? Starting point is can you tell me how your stuff works? And usually the response that other companies is. Why do you want to know? Right. It's a really pointed defense of we find it. The m where is really people are incredibly open. I don't, you know, know exactly how this got embedded in the culture. Maybe because it was a spin off from the university, but deeply embedded in this culture is Oh, yeah, let me tell you how this stuff works. And, uh, you know, maybe you'LL have a better idea. We don't even have to start with, You know, we have a better idea. It's like, you know, and then from there way can have ongoing discussions about >> Oh, that prints and improve it. That Cruz, why you have a community? Yeah, transparent creates openness that creates solidarity around open >> concept. Exactly. And and that's kind of what you see here. Radio. I don't know if people can see in the background is This is, you know, already day for in the Expo Hall and people don't want to leave and they're walking around. They're looking at each other's posters, they're talking to each other, making connections, and then they're going to build on those connections in the coming, you know, week. It's months and over the next year. And, you know, this is they said, you know, this has been going on for four days. You think that by now people have seen all the posters, they talked about everything there is still finding things that they want to talk to the kid. >> The candy store is a lot to taste here and learn ivory engaged graphic contents good and congratulations and thank you. And I just want to add, >> like something I love is, uh, getting here. Actually, before people arrive on the first day each year because when they come in, it's like greeting old friends right. It's sort of like a reunion except nobody's worried about, you know, like school reunion. You know, people are just playing happy to see each other, so that fits with that community thing, you know, because sometimes they're there in their teams and they don't necessarily get what you're being humble. We've talked last year about some of the content you put together in the team, so it's not. It's a hive mind, but you're the chief researcher, >> So you've gotta figure out on at least some canvas to start shaping framing sets of agendas to go after that. So if you can. So Lisa and I were just talking about this here today about how if you have a tech canvas, you don't want to create barriers of thinking. You want to open it up but not make it two restricted. That's your job. What can you tell us about the research agenda that's here and way out there and how how do you see that aperture range >> of yeah topics? Well, I think you know, I want to re first before even getting into that agenda, reaffirm a key point you made right, which is don't constrain people too much. So radio, by the way, is really very, you know, bottoms up. This is not about saying, you know, here's the four topics people really submit. It's a very competitive process people want to be. Not every engineering BM where gets to come to radio, right? It's it's eighteen hundred developers, which is an incredible commitment by the company. He's still a small fraction of our community, so they're actually submitting, you know, bottoms up, uh, to you know, see you and then we have a program committee that reviews it. So that's Ah, bottoms up part of the process from where I sit, You know what I think happens is whether it's our research team or filtering. You know, we'LL look at what comes. Bottoms up and say, Well, what's the signal to noise? For example, there's you know, we've had this year a tremendous amount of machine learning activity, and you see this in the posters here and in the presentations. However, you know that it wasn't too hard to detect a rising signal a couple years ago. So in that case a couple years ago, we said, OK, this is important. We see it in the external community way see it in the developer community. We see it within our own teams and developers. Clearly important. So starting a few years ago, we pulled together some of the senior most technologists, the principal engineers, a subset of them, and said, Hey, we want you guys to dio what we call a of a test study for tests are going faster, but also Veum, Where? Technology Study. We want you to actually do a strategy, but not a business strategy. Technology strategy. Look at the landscape of this. Look at where we are. Look at where we need to be and start charting a course. So in that sense, what we you know, coming out of that was, for example, information of an internal machine Learning program office? Who? One of them gold. It's billed the ML community. You talked about that before. Inside the company. It's not just a technical goal, it's an organizational on community goal. And that's just sort of, you know, kind of one example that wasn't the only output of that. But it's it's one example and what you see a big surprise, you know, kind of ten x, the engagement in the space so that that would be, you know, one case. I think one of the key things is, well, pick up on different topics. And then the thing that we do that I think it's different from some other companies to stop and say what our enterprise is going to need to do because at the end of the war in enterprise company and our customers, our enterprises and their needs her actually, although they're in different verticals, for example, let me just use machine learning. But it could be blockchain. It could be I ot. Actually, what they need is different from, say, the machine learning that that the hyper scale er's needs. So we realised that actually, there's a very interesting needs for us to explore the underserved parts of machine learning because all of these companies, if you look at them, they have a larger number of machine learning problems to work on the hyper scaler. You know, Facebook, Google, love them. They're actually working on a very focused set of problems, right? It's you know it's at serving. It's the social network graph. It's, you know, cat photo recognition, and I don't mean to knock those and they've got a great business is built around them. But notice it's a small number of problems. They do it it immense scale. Okay, given Enterprise probably wants to apply machine learning to a large number of problems, they're not going to run each of those problems on a million servers there, actually, probably running those problems on tens or hundreds of the EMS, right? And so what's the technology they need to address those problems and you can go through way looked at, you know, machine learning That way. We looked at I ot that when he said, You know, look, we think the analytics and the M L. That's a really cool things. We want to play in that space, too. But you know what everybody's trying to do. That, and not a lot of attention being paid to our enterprise is going to secure right and and managed all these I O T devices and the gateways to the devices. So we chartered a strategy for both research and business in that space, watching same thing, really exciting technology Now for enterprises, it's not about big point. It's not about currency, right? It's a money decentralized trust. It's an infrastructure for decentralized trust and effectively think of this is, you know, a database like thing. Except now it's going to be shared across many different organizations. And it's going to change how organizations work with each other and how they work with their auditors on how they work with their regulators. So this is great. >> But, you know, let's focus on what am I the way I just retweeted while you were talking? I just got a clip from last year. I asked you that question about Blockchain. You nailed it Way talked about how all the hype and fraud and i CEOs and confusing it. Yeah, but the world kept moving along. A lot of progress on the supply chain side, lots of interest, rafters trust. He sat realized that it's not about >> the eye. Indio. Yeah, so wave you, that is, You know that there's the high poker and, you know, they'LL be a deflation after the hiker passes. But there's real signal under there and so, you know, and we just turn our strategy and we keep marching down that path, and we're, you know, building up more partners, more people to work with. So it's it's that sort of thing. Quantum computing, right? We're not, you know, developing our own quantum computers. I can tell you that right now, and we're not even doing quantum algorithms. I have some albums researchers, but they're not doing quantum algorithm. You know, I kind of wish we were doing some of that stuff, but what we did do, and we looked at this and we said, Okay, hold on a key challenges. Uh, when the quantum computers do show up, we're going to need to transition to new cryptography to quantum resistant. You know, our post quantum there. Two terms, they're used. Cryptography enterprise customers are going to need to do that. Well, one second, they can't wait till this shows up. It takes ten years. Change your crypto. And by the way, you know if you've encrypted data and other people got a copy of your encrypted data, if it's long living data like, you know, health care records, you don't want them decrypting that in five or ten years. So you got a sort of start now and again, this goes back to what oh enterprise customers need to do Well, okay. The new crypto standards for miffed and others aren't quite ready, Okay, but But by the time they're ready, it's going to be too late to get started. Okay, But we could start working with our customers to work on crypto agility to change how they handle their cryptography. First off, get a good inventory of it, and then get set up so that they're using essentially plug mobile libraries so that it's easier for them to change their cryptography as soon as the standard shows up. And by the way, even if quantum computing takes a lot longer than we all think, this is good hygiene anyway. In other words, it's just a no regrets move for our customers and Khun. We sort of help them go down that path. And this is an example where we can actually also partner with our colleagues that are, say, other parts of Del technologies to help make that work for We're working with others in the industry, you know, intel, and we've kind of convened a form of players within the industry. You know, start working in that direction again. What do enterprise you know, what's the cool new technology. What oh enterprises need. >> So you talked about this event being open in terms of like the agenda and the topics being driven from the bottom of it, That gets really cool. So in the spirit of talking about customers and, like you were saying designing for what enterprises need and all of the variations that encompasses where is customer influence not just a radio, but within the em wears research and innovation programs and strategy. What's that? I mean, I just Advisors don't like that. It's >> a great question. So, like many companies, you know, we do have various advisory body's right, so we bring them in and, well, we'Ll sort of half like, you know, the sea tabs are customer technical advisory body. So the more technical people in some of our kind of more leading customers and we'LL show them things that we're working on, you know, under any kind of India arrangement, and get their feedback, you know, sort of OK, Does this make sense? You know? If not, why not? If it does, you know often it's not that finery, right? It's how would you use it? And we really sort of them give that feedback backto our teams. Now many people do this kind of thing, so we have lots of other customer engagements. We bring customers into forms like radio to be on panels, breakouts, things like that to give presentation so that basically, let's face it in one or two events, that's not going to convey much signal to our engineers. It's a madam, a six storey engineers way want you to be out talking to customers, right? So getting our engineers to be at PM world but way have programmes to actually allow engineers and encourage them to get out, make customer visits above and beyond. And by the way, if you look at it again, our principal engineers in our fellows I think what you find is the vast bulk of them are distinguished because they love engaging with customers. They don't just do it because it's part of the job. They love getting that feedback, so it actually helps them in their career, and we try to sort of essentially teach that to folks. One of the programs we have that in the CTO office, but I love it's not him. It's not in my part, So you know this is a case of I love all the things that we have that just my own, You know, uh, is it's like it's like loving your nieces and nephews, right? Not just your own children way. You were going to ask you your favorite child so way have, like, the CTO ambassadors program, Uh, which basically is coming from the field. So we have, you know, field engineers. They're not on the development side, but these air super technical people that are out in the field touching our customers all the time in any company, there's always a subset of those folks that just have a really good intuitions for where the customers are going and are good at raising their hands about that. So way actually have a program with CTO Ambassador Program CTO way where, you know, literally we give them a pin right way, give them a bad on DH. So we've tried to identify that subset of the field engineers and way regularly bring them in, you know, to pollo alter or bring them together. Whether it's a V m world or radio or whatever again, same thing. We're going to let them know what we've got cooking. We're going to get their feedback. We're gonna hear from them on. And this is not just on research right away. This is on the product pipe lines. You know what's going on in the road map and everything else now to me again. That's just actually a starting point. Because when I put my people in front of Seo is its telling my people, this is the group of folks. When you have a new idea, don't just talk to the product people go find CTO is because, you know, one of the best ways and I'm gonna be a little selfish. One of the best ways for us to influence the customers it influence the company is to get customers excited about something you were doing right. So you know Helen Lawson talk about technology push. And if you really want to be a success, we'LL get innovating it. In a large company, you need to create whole absolutely, and so the CDOs are great. They help us find people to do posies with, because you have to find just the right. You have to find a customer that has a need for this new stuff, but they also have to be somebody that understands this isn't yet a product. This is a journey, right? We're going to jointly try something out. You're gonna learn about whether this new tech can help you and how it could help you. We're going to learn what the product ultimately means, but, you know, you're not gonna be able to actually take out your checkbook at the end and get it right away. So you have toe, you know, be comfortable investing the time and energy, and then they have >> a spy in three that is really one of the core elements that's essential to drive innovation. >> Absolutely. And you need that. As I said, you need that customer partnership to help fine tune things. It's, you know, one of the things more broadly I try to do with research team is, you know, on the one, and give them the freedom to say, Hey, I have a new idea and I want to explore that new idea. That's great. Now, if you think about it right, then they're running open. Luke, they're running based on, you know, kind of their guesses. What educated guess. Right? And their intuition what people might want in the future. So that's good. What a then do it say. Okay, that's great. Uh, you know, you did a little bit. You wrote a paper build a prototype. Okay, so now they get a prototype bill. Okay, That starts getting this idea little more concrete. They're okay with that. The next step, it sort of is. Okay. Now, >> you got to >> get somebody to use that prototype, because I need you to get. And you need you to get feedback and create a feedback loop. Because otherwise, what's gonna happen is they made that first intuitive guests. So let's say they had their really phenomenal and they have a seventy five percent chance of getting it right. Okay, that that. But if they now continue to make a series of educated guesses and they have, ah, you know, seventy eighty percent chance on each educated guests and they make a siri's of four or five of those they have almost, you know, very quickly, close to zero chance of being in the right spot if you just multiply out the probabilities. But if they make that first big league and they start getting customer feedback That actually helps them right get more and more focused on where the bull's eye is. You have a really great chance of changing, >> so they don't build this great technology with no customers. Crichton second >> don't want somebody for a problem, right? But if you want to, you know, kind of have >> some really big ball change. You've >> got to be. >> Well, you've got to be willing to make that first big step without the feedback because the customers don't wear right. And if you just went to the customers said if you had this, what would you do? And they probably say, No, no, no. Instead of that, I want another feature over here. So you got to go and build that first prototype and take the leap of faith. The issue is, if you compound the leap of faith, your odds of being successful slope. If you quickly get into the hands of the customers, get feedback and start focusing in on where the value is, your chance goes up dramatically. >> Awesome. I wish we had more time with you, David. We're gonna let you get back to all of the amazing innovation that I have no doubt it's going on right behind us. Thank you. Something, Johnny on the Cube today. >> Look forward to seeing you again soon. >> Absolutely. For John Ferrier, I'm least Martin. You're watching the cubes. Exclusive coverage of the young Where? Radio twenty nineteen. Thanks for watching.

>> From Palo Alto, California, it's theCUBE. Covering The Conference Board's 6th Annual Innovation Master Class. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at the Innovation Master Class that's put on by The Conference Board. We're here at Xerox PARC, one of the original innovation centers here in Silicon Valley. Tremendous history, if you don't know the history of Xerox PARC go get a book and do some reading. And we're excited to have our next guest because there's a lot of talk about tech but really not enough talk about people and where the people play in this whole thing. And as we're seeing more and more, especially in downtown San Francisco, an assumption of responsibility by tech companies to use some of the monies that they're making to invest back in the community. And one of the big problems in San Francisco if you've been there lately is homelessness. There's people all over the streets, there's tent cities and it's a problem. And it's great to have our next guest, who's actually doing something about it, small discrete steps, that are really changing people's lives, and I'm excited to have him. He's Kevin Adler, the founder and CEO of Miracle Messages. Kevin, great to meet you. >> Great to meet you too Jeff. >> So, before we did this, doing a little background, you knew I obviously stumbled across your TED Talk and it was a really compelling story so I wonder A, for the people, what is Miracle Messages all about, and then how did it start, how did you start this journey? >> Miracle Messages, we help people experiencing homelessness reconnect to their loved ones and in the process, help us as their neighbors reconnect with them. And we're really tackling what we've come to call the relational poverty on the streets. A lot of people that we walk by every day, Sure, they don't have housing, but their level of disconnection and isolation is mind boggling when you actually find out about it. So, I started it four years ago. I had an uncle who was homeless for about 30 years. Uncle Mark, and I never saw him as a homeless man. He was just a beloved uncle, remembered every birthday, guest of honor at Thanksgiving, Christmas. >> And he was in the neighborhood, he just didn't have a home? >> He was in Santa Cruz, he suffered from schizophrenia. And, when he was on his meds he was good and then he'd do something disruptive and get kicked out of a halfway house. And we wouldn't hear from him for six months or a year. >> Right. So, after he passed away, I was with my dad, and not far from here, visiting his grave site in Santa Cruz. And I was having a conversation with my dad of the significance of having a commemorative plot for Uncle Mark. I said, he meant something to us, this is his legacy. So that's nice, but I'm going to go back in the car, pull out my smartphone, and see status updates from every friend, acquaintance I've ever met, and I'm going to learn more about their stories on Facebook, with a quick scroll, than I will at the grave site of my Uncle Mark. So, I'm actually a Christian. I have a faith background, and I asked this question: "How would Jesus use a smartphone?" "How would Jesus use a GoPro camera?" Cause I didn't think it was going to be surfing pigs on surf boards. And I started a side project where homeless volunteers, like my Uncle Mark, wore GoPro cameras around their chests. And I invited them to narrate those experiences and I was shocked by what I saw. And I won't regale you with stories right now but I heard over and over again, people say "I never realized I was homeless when I lost my housing, "only when I lost my family and friends." >> Right. >> And that led me to say, if that's true, I can just walk down the street and go up to every person I see and say "Do you have any family or friends "you'd like to reconnect with?" And I did that in Market Street, San Francisco four years ago, met a man named Jeffrey, he hadn't seen his family in 22 years. Recorded a video on the spot to his niece and nephew, go home that night, posted the video in a Facebook group connected to his hometown, and within one hour the video was shared hundreds of times, makes the local news that night. Classmates start commenting, "Hey, "I went to high school with this guy, "I work in construction, does he need a job? "I work at the mayor's office does he need healthcare?" His sister gets tagged, we talk the next day. It turns out that Jeffrey had been a missing person for 12 years. And that's when I quit my job and started doing this work full time. >> Right, phenomenal. There's so many great aspects to this story. One of the ones that you talked about in your TED Talk that I found interesting was really just the psychology of people's reaction to homeless people in the streets. And the fact that once they become homeless in our minds that we really see through them. >> Totally. >> Which I guess is a defense mechanism to some point because, when there's just so many. And you brought up that it's not the condition that they don't have a place to sleep at night, but it's really that they become disassociated with everything. >> Yeah, so I mean, you're introduction to me, if you had said hey there's this guy, there's no TED talk, there's nothing else, he's a housed person, let's hear what he has to say. Like, what would I talk... That's what we do every single day with people experiencing homelessness. We define them by their lack of one physical need. And, sure, they need it, but it presumes that's all there is to being human. Not the higher order needs of belonging, love, self-actualization. And some of the research has found that the part of the brain that activates when we see a person, compared to an inanimate object, does not respond when we see a person who's experiencing homelessness. And in one experiment in New York, they had members of a person's very own family, mom and dad, dress up to look homeless on the streets. Not a single person recognized their own member of their own family as they walked by 'em. >> Yeah, it's crazy. It's such a big problem, and there's so many kind of little steps that people are trying to do. There's people that walk around with peanut butter and jelly sandwiches that we see on social media, and there's a couple guys that walk around with scissors and a comb and just give haircuts. These little tiny bits of humanization is probably the best way to describe it makes such a difference to these people. And I was amazed, your website... 80 percent of the people that get reconnected with their family, it's a positive reconnection. That is phenomenal because I would have imagined it's much less than that. >> Every time we reconnect someone, we're blown away at the lived examples of forgiveness, reconciliation. And every reunion, every message we record from a person experiencing homelessness, we have four, five messages from families reaching out to us saying, "Hey I haven't seen "my relative in 15 years, 20 years." The average time disconnect of our clients is 20 years. >> Right, wow. >> So what I've been doing now is, once you see it like this, you walk down the street, you see someone on the streets, you're like that's someone's son or daughter. That's someone's brother or sister. It's not to say that families sometimes aren't the problem. Half of the youth in San Francisco that are homeless, LGBTQ. But it's to say that everyone's someone's somebody that we shouldn't be this disconnected as people in this age of hyper-connectivity and let's have these courageous conversations to try to bring people back in to the fold. >> Right, so I'm just curious this great talk by Jeff Bezos at Amazon talking about some of the homeless situations in Seattle and he talks, there's a lot-- >> He's a wealthy guy, right? >> He's got a few bucks, yeah, just a few bucks. But he talks about there's different kind of classes of homelessness. We tend to think of them all as the same but he talks about young families that aren't necessarily the same as people that have some serious psychological problems and you talked about the youth. So, there's these sub-segments inside the homeless situations. Where do you find in what you offer you have the most success? What is the homeless sub population that you find reconnecting them with their history, their family, their loved ones, their friends has the most benefit, the most impact? >> That's a great question. Our sweet spot right now, we've done 175 reunions. >> And how many films have you put out? >> Films in terms of recording the messages? >> Yeah, to get the 175. >> 175 reunions, we have recorded just north of about 600 messages. And not all of 'em are video messages. So, we have a hotline, 1-800-MISS-YOU. Calls that number, we gather the information over the phone, we have paper for 'em. So 600 messages recorded, about 300, 350 delivered and then half of them lead to a reunion. The sweet spot, I'd say the average time disconnected of our clients is 20 years. And the average age is 50, and they tend to be individuals isolated by their homelessness. So, these are folks for decades who have had the shame, the embarrassment, might not have the highest level of digital literacy. Maybe outside of any other service provider. Not going to the shelter every night, not working with a case worker or social worker, and we say hey, we're not tryna' push anything on ya' but do you have any family or friends you'd like to reconnect with. That opens up a sense of possibility that was kind of dormant otherwise. But then we also go at the other end of the spectrum where we have folks who are maybe in an SRO, a single room occupancy, getting on their feet through a drug rehab program and now's the point that they're sayin' "Hey, I'm stably housed, I feel good, "I don't need anything from anyone. "Now's the time to rebuild that community "and that trust from loved ones." >> Kevin, it's such a great story. You're speaking here later today. >> I think so, I believe so. >> On site for good, which is good 'cause there's so much... There's a lot of negative tech press these days. So, great for you. How do people get involved if they want to contribute time, they want to contribute money, resources? Definitely get a plug in there. >> Now, or later? Right now, yeah, let 'em know. >> No time like the present. We have 1200 volunteer digital detectives. These are people who use social media for social good. Search for the loved ones online, find them, deliver the messages. So, people can join that, they can join us for a street walk or a dinner, where they go around offering miracle messages and if they're interested they can go to our website miraclemessages.org and then sign up to get involved. And we just released these T-shirts, pretty cool. Says, "Everyone is someone's somebody." I'm not a stylish man, but I wear that shirt and people are like "That's a great shirt." I'm like, wow, and this is a volunteer shirt? Okay cool, I'm in business. >> I hope you're putting one on before your thing later tonight. >> I have maybe an image of it, I should of. >> All right Kevin, again, congratulations to you and doing good work. >> Thanks brother, I appreciate it. >> I'm sure it's super fulfilling every single time you match somebody. >> It's great, yeah, check out our videos. >> All right he's Kevin, I'm Jeff. We're going to get teary if we don't get off the air soon so I'm going to let it go from here. We're at the Palo Alto Xerox PARC. Really the head, the beginning of the innovation in a lot of ways in the computer industry. The Conference Board, thanks for hosting us here at the Innovation Master Class. Thanks for watching, we'll see you next time. (bright ambient music)

>> Announcer: Live from Washington, DC, it's theCube, covering AWS Public Sector Summit 2017, brought to you by Amazon Web Services and its partner ecosystem. >> Welcome inside the convention center here in Washington, DC. You're looking at many of the attendees of the AWS Public Sector Summit 2017. We're coming to you live from our nation's capital. Several thousand people on hand here for this three-day event, we're here for two days. John Walls, along with John Furrier. John, good to see you again, sir. >> Sir, thank you. >> We're joined by Jay Littlepage, who is the VP of Infrastructure and Operations at Digital Globe, and Jay, thank you for being with us at theCube. >> My pleasure. >> John W: Good to have you. First off, your company, high-resolution, earth imagery satellite stuff. Out-of-this world business. >> Yep. >> Right, tell our viewers a little bit about what you do, I mean, the magnitude of, obviously, the environmental implications of that or defense, safety security, all those realms. >> Okay, well, stop me when I've said too much because I get pretty excited about this. We work for a very cool company. We've been taking earth imagery since 1999, when our first satellite went up in the sky. And, as we've increased our capabilities with our constellation, our latest satellite went up last November. We're flying, basically, a giant camera that we can fly like a drone. So, and when I say giant camera, it's about the size of a school bus, and the lens is about the size of the front of the school bus, and we can take imagery from 700 miles up in space and resolve a pixel about the size of a laptop. So, that gives us an incredible amount of capability, and the flying like a drone, besides just being really cool and geeky, we can sling the lens from basically Kansas City to here in Washington in 15 seconds and take a shot. And so, when world events happen, when an earthquake happens, you know, they're generally not scheduled events, we don't have to have the satellite right above the point where there's something going on on the ground, we can take a shot from an angle of 1,000 miles away, and with compute power and good algorithms, we can basically resolve the picture of the earth, and it looks like we're right overhead, and we're getting imagery out immediately to first responders, to governmental agencies so they can respond very quickly to a disaster happening to save lives. >> So, obviously, the ramifications are endless, almost, right? >> Yes. >> All that data, I mean, you can't even imagine the amount, talk about storage. So, that's certainly a complexity, and then, they are making it useful too all these different sectors. Without getting too simple, how do you manage that? >> Well, you know, it's a big trade-off because, ideally, if storage was free, all of our imagery in its highest consumable form would be available all the time to everybody. Each high-resolution image might be 35 gig by itself. So, you think of that long of flying a constellation, we've got 100 petabytes of imagery. That's too much, it's too expensive to have online all of the time. And so, we have to balance what's going to be relevant and useful to people versus cost. You know, a lot of the imagery goes through cycle where it's interesting until it's not, and it starts to age off. The thing about the planet, though, is we never know what's going to happen, and when something that aged off is going to be relevant again. And so, the balance for my team is really making sure we're hitting the sweet spot on there. The imagery that is relevant is readily accessible, and the imagery that's not is, in its cheapest form, fact or possible, which for us, is compressed, and it's in some sort of archival storage, which for us, now that we've used the Snowmobile, is Glacier. >> Jay, I want to ask to give your thoughts. I want you to talk about DigitalGlobe, before that, some context. This weekend, I was hanging out with my friends in Santa Cruz and kids were surfing. He's a big drone guy, he used to work for GoPro, and she used to buy the drones and, hey, how's it going with the drones. It got kind of boring, here's a great photo I created, but after a while, it just became like Google Earth, and it got boring. Kind of pointed out that he wanted more, and we got virtual reality, augmented reality, experience is coming to users. That puts imagery, place imagery, the globe, pictures, places and things is what you guys do. So, that's not going away any time soon. So, talk about your business, what you guys do, some of the things that you do, your business model, how that's changing, and how Amazon, here in the public sector, is changing that. >> Well, that's a fantastic questions, and our business is changing pretty rapidly. We have all that imagery, and it's beautiful imagery, but increasingly, there's so much of it, and so many of the use cases aren't about human eyeballs staring at pixels. They're about algorithms extracting information from the pixels. And, increasingly, from either the breadth of pixels, instead of just looking at a small area, you can look around it and see what's happening around it and use that as signal information, or you can go deep into an archive and see the same location on the planet over and over over years and see the changes that had happened in terms of time frame. So, increasingly, our market is about extracting information and extracting insights from the imagery more so than it is the imagery itself. And so that's driving an analytics business for us, and it's also driving a services business for us, which is particularly important in the public sector to actually use that for different purposes. >> You can imagine the creativity involved and developers out there watching or even thinking about using satellite imagery in conflux with other data. Remember, they're in the Web 2.0 craze earlier in the last decade. You saw mashups of API with Google Max. Oh yeah, pull a little pin, and then the mobile came. But now, you're seeing mashups go on with other data. And I've heard stats at Uber, for instance, remaps New York City every five days with all that GPS data of the cars, which are basically sensors. So, you can almost imagine the alchemy, the convergence of data. This is exciting for you, I can imagine. Won't you share with us, anecdotally or statistically what you're seeing, how this is playing out? >> Well, yes, some of our biggest commercial customers of our products now are location-based services. So, Uber's using our imagery because the size of the aperture of our lens means we have great resolution. And so, they've been consuming that and consuming our machine learning algorithms to basically understand where traffic is and where people are so that they can refine, on an ongoing basis, where the best pick-up and drop-off locations are. That really drives their business. Facebook's using the imagery to basically help build out the Internet. You know, they want to move into places on the planet where Internet doesn't exist. Well, in order to really understand that, they need to understand where to build, how to build, how many people are there, and you can actually extract all that from imagery by going in in detail and mapping roofs' shapes and roofs' sizes, and, from there, extracting pretty accurate estimates of how many people live in a particular area, and that's driving their project, which is ultimately going to drive access for... >> Intelligence in software, we look at imagery. I mean, we here at Amazon, recognition's their big product for facial recognition, among other pictures. But this is what's getting at, this notion of actually extracting that data. >> Well, you think about it. You know, once the data is available, once our imagery is available, then the sky's the limit. You know, we have a certain set of algorithms that we apply to help different industries, you know, to look at rooftops, to look at water extractions. After a hurricane, we can actually see how the coverage has changed. But, you look at a Facebook, and they're applying their own algorithms. We don't force our algorithms to be used. We provide the information, we try to provide the data. Companies can bring their own algorithms, and then, it's all about what can you learn, and then, what can you do about it, and it's amazing. >> So, here's the question. With the whole polyglot conversation, multiple languages that people speak that's translated into the tech industry, and interdisciplinary forces are in play: Data science, coding, cognitive, machine learning. So, the question is, for you, is that, okay, as this stuff comes together, do you speak DevOps? It's kind of a word, and we hear people say, is that in Russian or is that like English? DevOps is a cloud language mindset. And so, that brings up the question of, are you guys friendly to developers, and because people want to have microservices, I'm from a developer, I'm like, hey, I want those maps. How do I get them, can I buy it as a service, are they loaded on Amazon, how to I gauge with DataGlobe, as a developer or a company? >> Well, you think about what you just said and the customers I just talked about. They're not geospatial customers. You know, they're not staffed with people that are PhDs in extracting information. They're developers that are working for high-tech companies that have a problem that want to solve. >> There are already mobile apps or doing some cool database working in here. >> So, we're providing the raw imagery and the algorithms to very tried and true systems where people can plug into work benches and build artificial intelligence without necessarily being experts in that. And, as a case in point, my team is an IT team. You know, we've got a part of the organization that is all staffed with PhDs. They're the ones that are driving our global... >> John W: PhD is a service. (laughter) >> Well, kind of. I mean, if you think about it, they're driving the leading edge, for these solutions to our customers. But, I've got an IT team, and I've got this problem with all this data that we talked about earlier. Well, how am I actually going to manage that? I'm going to be pulling in all sorts of different sources of data, and I'm going to be applying machine learning using IT guys that aren't PhDs to actually do that, and I'm not going to send them to graduate school. They're going to be using standard APIs, and they're going to be applying fairly generic algorithms, and... >> So, is that your model, is it just API, is there other... >> I think the real key is the API makes it accessible, but a machine learning algorithm is only as good as its training. So, the more it's used, the more it refines itself, the better our algorithm gets. And so, that is going to be the type of thing that the IT developer, the infrastructure engineer of the future becomes, and I've already, basically, in the last couple of years, as we started this journey at AWS, 20% of my staff now, same size staff, but they're software developers now. >> So, I'll take this to the government side. We talked a lot about commercial use. But at the government side, I'm thinking about FEMA, disaster response, maybe a core of engineers, you know, bridge construction, road construction, coastline management. Are all those kind of applications that we see on the dot gov side? >> There are all things that you see that can be done on the dot gov side, but we're doing them all in the commercial environment. The USC's region for AWS, and I think that's actually a really important distinction, and it's something that I think more and more of the government agencies are starting to see. We do a lot of work for one particular government agency and have for years. But 99 point something percent of our imagery is commercial unclassified, and it's available for the purposes that our customers use it for, but they're also available for all those other customers I've talked about. And more and more of what we do, we are doing on the completely open but secure commercial environment because it's ubiquitous for our customers. Not all of our customers do that type of work. They don't need to comply with those rigid standards. It's generally where all AWS products that are released are released to, with the other environments lagging, and they probably don't want me saying that on TV, but I just did. And it's cheaper, you know, we're a commercial company that does public sector work. We have to make a profit, and the best way to do that is to put your environment in a place where if you're going to repeat an operation, like pulling an image of Glacier and build it into something that is consumable by either a human or an algorithm and put it back. If you're going to do something like that a million times, you want to do it really inexpensively. And so, that's where... (crosstalking) >> Lower prices, make things fast, that's Jeff Hayes' ethos, shipping products, that these books in the old days. Now, they're shipping code and making lower-latency systems. So, you guys are a big customer. What are the big implementation features that you have with AWS, and then, the second part of the question is, are you worried about locking. At some point, you're so big, the hours are going to be so massive, you're going to be paying so much cash, should you build your own, that's the big debate. Do you go private cloud, do you stay in the public? Thoughts on those two options? >> Well, we have both. Right now, we're running a 15-year-old system, which is where we create the imagery that comes off the satellites, and it goes into a tape archive. Last year, Reinvent... >> John F: Tape's supposed to be dead! >> Tape will die someday! It's going to die really soon, but, at the Reinvent Conference last year, AWS rolled out a semi truck. Well, the real semi truck was in our parking lot getting loaded with all those tapes, and it's sad... >> John F: Did you actually use the semi? >> We were the first customer ever, I believe, of the Snowmobile. And so, it takes a lot of time and effort to move 12,000 LTO 5 tapes loaded onto a semi and send it off. You know, that represents every image ever taken by DG in the history of our company, and it's now in AWS. So, to your second part of your question, we're pretty committed now. >> John F: Are you okay with that? >> Well, we're okay with that for a couple of reasons. One is, I'm not constraining the business. AWS is cheaper. It will be even cheaper for us as we learn how to pull all the levers and turn all the dials in this environment. But, you know, you think about that, we ran a particular job last year for a customer that consumes 750,000 compute hours in 22 days. We couldn't have done that in our data center. We would have said no. And so, I would... >> I know, I can't do, you can't do it. >> We can't do it! Or, we can do it, come back, the answer will be here in six months. So, time is of the essence in situations like that, so we're comfortable with it for our business. We're also comfortable with it because, increasingly, that's where our customers already are. We are creating something in our current environment and shipping it to Amazon anyway. >> We're going to start a movie about you, with Jim Carrey, Yes Man. (laughter) You're going to say yes to everything now with Amazon. Okay, but this is a good point. Joking aside, this is interesting because we have this debate all the time, when is the cloud prohibitive. In this case, your business model, based on that fact that variables spend that you turn up your Compute is based upon cadence of the business. >> That's exactly right. You know, the thing that's really changed for the business with this model is historically, IT has been a call center, and moving into Amazon, I manage our storage, and I pay for our storage because it's a shared asset. It's something that is for the common good. The business units and different product managers in our business now have the dial for what they spend on the Compute and everything else. So, if they want to go to market really rapidly, they can. If they want to spin it up rapidly, they can. If they want to turn it down, they can. And it's not a fixed investment. So, it allows the business philosophy that we've never had before. >> Jay, I know we're getting tight on time, but I do want to ask you one question, and I did not know that you were the first Snowmobile customers, so, that's good trivia to have on theCube and great to have you. So, while we got you here, being the first customer of AWS Snowmobile when they rolled out at Amazon Reinvent, we covered on SiliconAngle. Why did you jump on that and how was your experience been, share some color onto that whole process. >> Okay, it's been an iterative learning process for both us and for Amazon. We were sitting on all this imagery. We knew we wanted to get in AWS. We started using the Snowballs almost a year and a half ago. But moving 100 petabytes, 80 terabytes at a time, it's like using a spoon to move a haystack. So, when Amazon approached us, knowing the challenge we had about moving it all at once, I initially thought they were kidding, and I realized it was Amazon, they don't kid about things like this, and so we jumped on pretty early and worked with them on this. >> John F: So, you've got blown away like, what? >> Just like. >> What's the catch? >> Really, a truck, really? Yeah, but really. So, it's as secure as it could possibly be. We're taking out the Internet and all the different variables in that, including a lot of cost in bandwidth and strengths, and basically parking and next to our data, and, you know, it's basically a big NFS file system, and we loaded data onto it, the constraint for us being, basically that tape library with 10,000 miles of movement on the tape pads. We had to balance between loading the Snowmobile and basically responding to our regular customers. You know, we pulled 4 million images a year off that tape library. And so, loading every single image we've ever created onto the Snowmobile at the same time was a technical challenge on our side more so than Amazon's side. So, we had to find that sweet spot and then just let it run. >> John F: Now, it's operational. >> So, the Snowmobile is gone. AWS has got it. They're adjusting it right now into the West Region, and we're looking forward to being able to just go wild with that data. >> We got Snowmobiles, we got semis, we have satellites, we have it all, right? >> We have it all, yeah. >> It's massive, obviously, but impressed with what you're doing with this. So, congratulations on that front, and thank you again for being with us. >> My pleasure, thanks for having me. >> You bet, we continue our coverage here from Washington, DC, live on theCube. SiliconAngle TV continues right after this. (theCube jingle)

>> Announcer: Live from San Francisco, it's theCUBE, covering DevNet Create 2017, brought to you by Cisco. >> Okay, welcome back, everyone. We're here live in San Francisco for theCUBE's exclusive coverage of Cisco's new inaugural event called DevNet Create, an extension, an augmentation, a community-focused event of their DevNet community, which is a Cisco developer community, now out in the wild. Our next guest is Steven Pousty, lead developer and evangelist at Red Hat, I'm John Furrier, and my co-host Peter Burris. Steven, welcome to theCUBE. >> Thank you, thank you very much. It's exciting to be here. >> Great to have you on. We were just talking before on camera, getting all animated like, "Hey, turn the cameras on. "We got to get this conversation." We're talking about open source and really looking at some of the trends, but more importantly, the impact. >> Steven: Right. >> Also, we've had you guys on many times on theCUBE. We covered Red Hat Summit, Jim Whitehurst. So, abstractions layers in software, open source ecosystems, you have a background in nature. >> Steven: Yeah. I- >> And ecosystems, literally. >> Steven: Yeah. Yeah, yeah. Yeah, actually I have my PhD in ecology. I'm actually a conservation biologist by training, but IT and computer programming pays the bills a lot better than-- >> Hey, anthropologists and ecologists do very well in the tech world, believe it or not. >> Steven: Yeah, I love big data. >> Peter: And philosophers. >> Yeah, and philosophers. Yeah, with all that logic and the ontologies and all that. >> Ontologies and symbiotics. >> Steven: Yep, yep. >> John: Okay, so I got to ask you, obviously Red Hat has been really the poster child for open source companies going public. We've heard since over the past generation, "The Red Hat of blank, The Red Hat of," and that got played. Certainly we downplayed that. People were trying to call Cloudera the Red Hat of Hadoop (mumbles) realizing that that's never going to happen. You were a once in a generational company, but Red Hat was a tier two company back in those days. Now, open source is certainly tier one software across the board, and I think this event at Cisco kind of amplifies that. Look at it, open source has gone a whole nother generation. A lot of young kids coming in. It's tier one software. The business model is open source. Four new companies just went public recently. So, done deal. >> Right, I mean, I think if you look in the technology ecosystem as a whole, if you don't start with open source you either have some incredibly magic sauce that no one else has or you're done. You couldn't even look at the movies... The arch enemy when I was growing up in software was Microsoft of open source, right? If you look at them now with Satya, they've made great strides to be part of the open source ecosystem at a real level, not like just lip service like they used to do sometimes. Like when I interact with some of our Microsoft partners, you can tell that there's a different change and they really believe in that open source-- >> Microsoft used to be known as lip service and vaporware and they used to kind of freeze the market with their monopoly power as some would say, but more recently they've... Back in the old days, Linux was a cancer. Steve Ballmer said, "Linux is the cancer to the industry." >> Steven: And so-- >> John: Now they're doing Linux with .NET. >> And so at the Red Hat Summit just recently I did the Microsoft keynote, I was the Red Hat person on the Microsoft keynote, and we demonstrated .NET Core running in OpenShift on Linux machines, we demonstrated SQL Server running in containers on OpenShift, and then for the end we showed some of the community work, because both of us are involved in Kubernetes. We actually showed a Windows container spinning up IIS being orchestrated from a Linux OpenShift. So, it was actually the Linux server, the Linux OpenShift server, was talking to Windows containers and spinning up Windows containers on the fly. So, I never thought that would've happened. So, it's definitely a sea change. >> And boy was that partly the sea change, we can encapsulate it, is that we used to think in terms of winners and losers in the tech industry, and now it's big winners and less big winners, but the question is how is, I think the realization Microsoft had, is that open source does not demarcate winners from losers. It demarcates, or rather suggests, a new way of thinking about how software gets developed, how software gets integrated and packaged, and ultimately how software gets diffused. So, talk a little bit about this notion of the new world of winners and winners and how this thing moves together, almost in an ecosystem type of way, so that the capabilities overall improve over time, because that's really where we're going is digital business being able to do more for customers. >> Right, and I think that's one of the things that you're seeing coming out from the open source world now is it's becoming less and less about I have this technology versus this is the technology, this open source technology, that we use to help solve your business problems. I gave a talk about this a couple times. There's a concept in ecology called, now I'm blocking on the word, but you probably came across it in school, probably even elementary school. It's the idea that you have bare earth, and then a few plants show up and they start breaking it up, and those plants create a condition where new trees come in, and then it just keeps going and going and going, and then you finally have a rainforest at the end, right? >> Peter: Diversity? >> No, it's-- >> Anyway, we don't want to put you out. >> Yeah, I'm stuck on the word and I can't remember-- >> Here's an ecology question. I saw a Facebook thing where in Yellowstone National Park they introduced four wolves to the ecosystem, and all of a sudden the rivers are no longer wide, they're tighter, there's pools. So four wolves create dynamics. So there's a coexistence, but there's still wolves. >> Right, and so the-- >> John: Who's the wolves in the industry? >> See, that's the thing, it's not that. Just because there are wolves in the industry doesn't mean that they control the entire ecosystem. So I think what I say at the end of this talk is there is no right or wrong about where you are in the ecosystem or in your evolution as an ecosystem, right? There is what is right for your business problem. So, we have this in our, especially in the United States, we have this idea of you're either the winner in this space, you're the cloud solution and you're the winner, or you're not, you're nothing. It's like the Talladega Nights, "If you're not first, you're last!" >> He runs around in his underwear. That's your outcome if you have that strategy. >> Great strategy. >> It was such a good movie. But so the point that I was trying to make in this talk is there's lots of different... So like with bird species, when they need to share a tree, there can be six different species all in the same tree, and what they do is what's called niche differentiation. That means, "Oh, I'm going to specialize "in the tops of the trees "and I'm going to only eat this type of caterpillar." And the one on the bottom says, "I specialize on beetles and I do this." And I think what you're seeing with the open source stuff is all these things can coexist. Like GNOME versus KDE. Everybody was claiming GNOME or KDE was the winner for forever. They're still around for forever. So, what I think with this cloud software as well where everybody is like, "Oh, this is the one winning," or this is the, there's a whole host of places for them all to live, and with open source I think things just live forever. >> John: What's your ecosystem analogy that coexistence is actually a better philosophy looking at the big picture than some dominant wolf or whatever. >> That's right, it's the diversity, it's the mutualism, it's the coevolution, it's the right diversity. Like a desert is actually a beautiful place if you go to it. Like we like to pick on the desert, but if you actually spend time in the desert it's gorgeous. There's nothing wrong with the desert. So, if you're some company who doesn't need Kubernetes and all the other pieces in this huge cloud environment, don't feel like that's something you have to take on. >> Peter: But they are the desert. >> That's right, but they are the desert. But, all my PhD research was in the desert, and I used to hate it, because I started this little rolly polly in the desert, and by the time I left I was like, "Oh, I miss the desert when I don't have it." >> John: The sunrises are beautiful. >> Sunrises are beautiful. You can see forever. If you actually pay attention to the small things... All I'm trying to point out is people live in Kansas, people live in New York, people live all over, and they usually find where they live, unless it's some disgusting dump, they say this is a beautiful-- >> Peter: They find beauty in it. >> Yeah, and I think it shouldn't necessarily be everybody has to get to the same place and use all the same technology. There's technology reasons for everything. >> So, I want to pick up on that concept. So the industry used to be pretty much structured around asset specificity. This asset does this for you. As we move more to a software orientation that notion of asset specificity starts to blend away. I think that's one of the seminal features of digital business and digital business transformation is the reduction of asset specificity, but it does mean that increasingly we need to focus on what I'll call value specificity, that we're moving away from the asset being the dominant determinant of structure and how you do things to the value that's being generated and the value that's being presented in any number of different fashions, and that becomes what dictates or describes who you are, what you do, both as an individual, also as a company, as well as a piece of software data. So talk a bit about kind of this notion of niche specialization being more tied to the value that you create as opposed to the asset that you bring. >> That's right, and we're seeing this a lot with our customers, who... You know, OpenShift is based off of Kubernetes and Docker and all that stuff, and containers, and so what we're seeing is a lot of companies come to us and say, "Well, I want to use OpenShift for this. "I want to use OpenShift for that." It's no more that we go to customers and say, "Here's OpenShift and you will use it "for purposes X, Y, and Z." What it is is well, that IT group might say well I've got three different business groups that I have to produce stuff for them that they can use. And they'll say, "Can I use Kubernetes for this? "Can I use, oh, I can't? "Well, then I'll get something else for this, or can we adapt-- >> Or complement it. >> Yeah, it's about creating value for the business unit, and it's becoming more and more that now. I think it's an evolution that we've seen, again, this evolution of stuff with the shadow IT and all that stuff. It became less about you're some sort of specialized high priest with this special asset that only you know how to control, I know how to do GIS software, I know how to do big data, no, what value do you produce for me? I don't care that you can buy these kinds of servers and provision them. If I can't use them, what does that do for me, right? So I think we see that at Red Hat a lot where we were the enterprise Linux company, and I think our leaders have done a really good job of saying, "Yeah, that's a good place "where the puck is right now, "but that's not where the puck is staying. "It's moving towards value, "it's moving towards integrated solutions." Go ahead. >> Let me extend this a little bit. So one of the things that we've observed within (mumbles) SiliconAngle, and we've talked to some other people today specifically about this, was the idea that open source has done a really good job of looking at a thing, a convention, that's well defined and well established and then building an open source variant of it. Open source has not been as successful, for example, in the big data world, where the use case or the definition of where we're going is amorphous. Instead, a lot of open source development ends up looking at each other saying, "Well, I'll fix your problem and you'll fix my problem, kind of. Nothing wrong with that, but the vision of where the industry is going to go. How are different companies, what will be open source leadership at redefining where this industry goes so that the open source developers can both be free to do what they need to do, create value as they need to, but at the same time, share a common understanding of where this ends up? >> So I think this goes back to what you were talking about with value, right? So I think what ends up... I'll use the example of big data. So I did a lot of statistical analysis for my PhD, and back then you used SAS or S-PLUS, both proprietary solutions. I think what has caused some of the explosion in big data is that you had these data scientists, the statisticians, intermingling, fertilizing with the computer science people who were handling these other really big problems. So what comes out of that, this is that margin thing again, right? You have statistics and-- >> Peter: Diversity and interesting things happen in the margin. >> At the margin. So what you have is these two groups come together, and suddenly you have the computer science people saying, "Oh, well I know a lot about algorithms "and I'm going to help you figure out "how to get value of what... "You're trying to solve this statistical algorithm, "I'm going to help you build distributed software that does that and that's where we get that happening. >> So the collaboration at the edge, the fringe, the lunatic fringe, or whatever you want to call it, the margin, is where the innovation is. >> I think that's where the innovation is because that helps avoid the navel gazing, right? Like, "Oh, I'm looking at what you exactly built, "and I'm going to build a slight variation on it." Well no, I actually need some, when you bring other disciplines in they say, "Well, this is the problem I'm going to solve," and the computer science person or the other side will say, "Well, that sounds "kind of like this thing, but let's try," and then suddenly new ideas come up and new ways to handle things. So I think, again, switching to value rather than what technology am I going to build is what's going to actually drive like, we need something to handle our big data. That's what's going to drive the vision. So you see in the big data world you see Spark, you see Zeppelin, you see all these different things competing, but what they're all doing is trying to drive how do I analyze big data efficiently? So you get some competing solutions. Then over time I think that's the vision that they're driving. >> I got to ask you, so like naval gazers is one dimension, but also there's the rearranging the deck chairs, like someone says, "Let's move things around "and magic will happen." Well you're pushing a whole nother concept, which I think is legit, which is as you put people together it might be uncomfortable, but then innovation can come out of it. Okay, so here's the ways. Computer and science and cloud computing, all that great stuff is happening, compute, storage, algorithm, etc., data, now society. So now society has issues, because what's the societal impact? These are first generation problems that we're facing, which side of the street does the cards drive on? Who gets hit first? They have to make these decisions. You see all these new issues, from even younger kids, cyber bullying, online behavior, across the board, societal impact. We are those margins. >> So I think for me tools... I thought about this a lot, right, because in the college I was kind of a tools person, and I think tools are value neutral. Any tool can be used for good or for bad. So, what we're doing right now in the open source world is develop, and in IT in general, is developing new tools, and what usually ends up happening is society develops norms after the tools have been created. In some ways, I think... I some ways, I kind of... It's a hard one. This is a much longer discussion and probably would involve some sort of alcoholic liquid or something to draw it out. >> It's a double edged sword, or tool, depending on how you look at it. We got to see it first before you can problem solve it. >> But the problem is-- >> You can't problem solve vapor. >> That's right, but on the other hand, sometimes you can see if you stopped and aren't so enamored with the latest and greatest tool without thinking about like, "Oh, well what are actually the implications of it?" I was going to say, I think the Europeans do a little bit of a better job of putting a little bit of foresight into tools when they come out saying, "Hold on, let's take a look at this." >> John: At the impact? >> Yeah, at the impact. >> So let me add one more thing to the conversation, because I think you're spot on, that the tools may be value neutral, but the impact, the transaction cost, of doing certain types of work in a different ways, and some work, and work is not necessarily value neutral. We may look at some tools and say, "That work is not good. "This tool reduces the transaction cost "of performing that work faster "or more completely than that work, "so that tool is going to have a less positive impact--" >> Impact on society as a whole >> "Than some other tool." And I think we can start introducing that kind of an analysis into it. >> I think so. I think that was... I live in this area, like I'm in Santa Cruz, so when I want to I say I'm not in the Valley, but when I want to I say I am in the Valley, I think the Valley is particularly enamored with the toys, or the tools, that it produces, and how technology will solve all our problems, and technology is great, and it is inherently good, and I like to say, "No, it's a tool, "and so a tool could be used for good or for bad." Like one example is ride sharing. Everybody was like, "Oh, this is the best! "This is awesome!" One of the things I thought of, my father is an immigrant, so I'm first generation on my father's side, and he wasn't a taxi driver, but I know how hard it is for first generation immigrants if you don't speak the language really well. So what used to happen with those ride shares is you had to have the capital to acquire a car before you could actually do ride sharing. So what you were basically doing was disenfranchising people who didn't have the capital from actually having this as a source of income when they came to the country. So, I was very conflicted about it to start with. Now, I'm less conflicted. I actually don't think ride share, given the economics I've seen actually play out I actually think ride sharing is not as big of a market and as game changing as everybody was making it. It was just some funny economics. >> Well Steven, certainly the conversation is very awesome. We should have you at the studio in Palo Alto next time you're in the Valley. >> Sounds great. >> You have plenty of tools and shiny new toys. >> Go by the Baylands and then go birding together at the Baylands, or maybe some fishing. >> Let's bring theCube over to Santa Cruz for a couple days. >> We should go down. >> That's great. >> Chill in Santa Cruz. Surf those waves, cloud, data, society. >> There you go. >> theCube on the boardwalk. >> Final question for you. Cisco is trying to push the margin with this event. It's a new event. It's an extension. It's outside their comfort zone. They had some projects that were kind of dismissed, interclouding, other things, this is a statement. Your thoughts on this show, because they have DevNet, why DevNet Create? Your thoughts. >> I think DevNet Create is a great opportunity for Cisco. I've been to the Cisco, is it Cisco Live, the huge gazillion people event? And there's a lot of energy around that, but that's mostly like network engineers and people who were bread and butter Cisco people. I really like that Cisco, that blurring between software and hardware means that Cisco really should be pushing people more in the, "We're going to help you create really interesting solutions." The more they make that easy for the developers... I think some developers are hardware hackers and love it. I am not one of those, and there's a lot of us who are not, and the more you make it easy for me to use software to create really interesting hardware things, the better it is for us. >> It's a classic case, the data scientists meets the algorithm guy. >> Steven: Exactly. >> So they're trying to bring these margins together where it might be awkward at first, but magic can happen. >> If I got to sit with some hardware people and like, "You need to make it so that I can write in Python "and do a whole bunch of neat networking and stuff "so at my house I can keep track "of how many birds are coming to my bird feeder "because I want to do this really cool experiment, "make that easy for me." >> By the way, you got camera, so you got bird recognition software. >> Steven: Exactly, exactly. >> A new feature on AWS. >> Yeah, I've seen demos of that. It's incredible what they can actually pull out now. >> Steven Pousty, Lead Developer at Red Hat, thanks for coming on theCube. Great conversation. >> Thank you very much. >> We'll have to continue it in Palo Alto. More live coverage here at Cisco Systems' DevNet Create. It's their inaugural event for developers. It's where IoT and app developers meet infrastructure, application infrastructure (mumbles). I'm John Furrier, Peter Burris with theCube. We'll be right back. Stay with us. (techno music) >> Hi, I'm April Mitchell, and I'm the Senior Director of Strategy & Planning for Cisco DevNet.

>> Announcer: Live, from Boston, Massachusetts, it's The Cube, covering Red Hat Summit 2017. Brought to you by Red Hat. >> Welcome to more of The Cube's coverage of the Red Head Summit 2017, I'm your host, Rebecca Knight. I'm joined today by DeLisa Alexander, she is the Chief People Officer here at Red Hat and then, joining us also, are the women in Open Source Technology winners. We have Jigyasa Grover and we also have Avni Khatri. So congratulations. >> Thank you. >> Thank you. >> I'm looking forward to hearing more about why you were bestowed with this honor but I want to start with you, DeLisa. >> DeLisa: Thank you. >> Why this award? Why did Red Hat feel that highlighting women and what they're doing in Open Source was worthy and we needed to showcase these women? >> Red Hat believes this is incredibly important. We all know that there are not nearly enough females in the technology industry and as the Open Source leader, we felt like we had a responsibility to begin to make a difference in that way. >> So tell us about the process. How do you find these women? How do you then winnow it down to who deserves it? >> So it's community based. It's a power of participation. >> So it's the Open Source way. >> It is the Open Source way. So the nominees come in from whomever would like to make a nomination. We do have a panel of judges that narrow down the nominations so there's five of each, the academic and the community And then we put it out to the community to vote. And so the community selects our award winners. >> Great, okay. So let's start with you, Anvi. So you, you're based here in Cambridge. >> Anvi: I am. >> And you were talking about how you had a five year goal. >> Yes. So, I was working at Yahoo! at the time and my boss at that time had asked us to make one year, five year, and 10 year goals. And in my five year plan, I had listed I wanted to set up computer labs for underserved populations. I wanted to travel, I wanted to see other cultures and I wanted to bring technology to other cultures. And I went to this awesome conference, the Grace Hopper Conference for Women in Computing. >> The Cube has a great partnership and long-term partnership with Grace Hooper. >> Awesome, it's a great conference. I was there and I met ... I reconnected with some folks and I was so inspired by all the women that were there and I came back and I was looking at my goals and I was like, why do I have to wait five years to do this? And I looked online and I saw that someone I had reconnected with, Stormy Peters at Grace Hopper, was running Kids on Computers and so I emailed her and the rest is really history. I found one of my passions in life is to bring technology to people who don't have access to it and doing it with Open Source so that it's accessible to everyone who needs it. >> So tell me about some of the stories, some of the kids that you're working with, and how it is, in fact, changing their lives. I just got back Monday night from a trip to Oaxaca, Mexico for Kids on Computers. We were there for a whole week. But we were setting up computer labs for these local rural communities. Most of them don't have internet. Some of them are now starting to get internet but what we do is we take donated equipment and grant money and Red Hat has also been ... Has awarded Kids on Computers a grant for contributing to some of the labs we set up last week. But we set up two new labs, we took donated equipment and we purchased equipment in country and we worked in the small towns of Antequera and Constitución. Those are actually the school names. We worked in the city of ... It's a suburb of Oaxaca City, Santa Cruz Xoxocotlán and working with them is really enlightening. So, some of the teachers have never used a computer before. Some of the kids have but most of them have not. So just seeing them trying to use a mouse, learning how to do single-click, double-click and going from the point where they haven't used it to the point where they have and where the understand it and getting to the point where one kid is teaching another kid is just really ... Just seeing that makes you feel, like, wow. I've actually made an impact and then, hopefully, by providing accessed technology and also providing access to educational content. So the offline content pieces for schools that don't have internet, working with a partner of Kids on Computers, Internet in a Box, providing offline Wikipedia, Khan Academy, MEDLINE content, offline books, that we give them a pathway to bettering their own lives and bettering the lives of their communities. >> That's really incredible and it will be this really big leveling of the playing field. >> Yes, I hope so. I really hope so and I am hopeful that will come to fruition 'cause I think education is one of the most sustainable ways to improve communities and I think Open Source is an avenue to get them there. >> Thank you. Jigyasa, so you are the academic winner. You are still a college student and with this wonderful award so congratulations. >> Jigyasa: Thank you so much. >> I want to talk to you. So you went to an all-girls high school in India and then got to university in New Delhi and weren't very happy with what you saw when you got to university. Can you tell us a little bit more. >> So I told you what was at the end. What I see is ... I am doing my undergraduation in Computer Science and Technology. In my batch, 80% of them are boys and the rest, girls, and not much interested in pursuing a career in technology, as such. They're pursuing different stuff like arts, designing, or even going for civil services back home. So when I came, I wanted to actually pursue a career in technology and do something apart from cataclysm. Not just books, but do something so that I can apply the concepts somewhere. We were just studying different mordents of software engineering but I wanted to be a part of a team, which actually implements it. So Open Source was the only way because I had internet, I had a good internet connection, I had a laptop and lots of free time. So one day I came across Pharaoh. The name itself fascinated me because it reminded me of Egyptian mummies and all. So that's how I actually got into Pharaoh. I've been contributing to it since three years now and also been apart of different world wide programs like Google Summer of Code and to give back to the community which has helped me so much, starting right from scratch. I tried to meet 13 rich developers and budding programmers through programs, like one of them is Learn IT Girl. So it pairs females, both mentors and mentees, worldwide. So not only do you get to know about technology but you can also know about their culture by being a team and knowing about how it works, how are their working styles and temperaments. Also, I wanted to be a part of something local so that I could interact with them physically so I'm the Director for Delhi Network of Women who Code which has more than 400 plus members back in New Delhi and I organize code labs, teach them, or randomly give pep talks sot that they do not feel bogged down and have enough to look forward to. It's been a pretty exciting journey, as I say. >> It's just beginning. >> And this is the thing is that we are bombarded with headlines about how difficult it is for women in the technology industry because it is such a male-dominated industry. There's a lot of sexism, there's a lot of discrimination, a lot of biases where people just don't put women and technology together. You think of a technologist, you think of an engineer, you think of a guy. So how do you think that these awards, DeLisa, are changing things? What are your hopes and dreams for women in this sector? >> Well, we've come so far in terms of the way we think about supporting women just in our conference alone. And so, I think that when we're really, really successful we won't need this award anymore. But we have a long way to go between now and then. Women like these women are just so inspiring and by sharing their stories and showing what women can do future generations of girls, hopefully, will be inspired to join. Men will understand the contributions that women are making today and it will help really generate the next leaders in Open Source that are women. >> Anvi, five years from now, what do you hope? How many labs do you hope to have opened? What's your grand plan? >> So we have 22 labs right now, which is so exciting, in five countries. >> In how long? >> So, we're eight years old. We were a 501(c)(3) in 2009, so super exciting. So my hope is that ... We are currently focusing in Oaxaca and we just formed a partnership with a local university down there to provide support because, as we know, technology is just one piece of the puzzle. We need the community, we need the support, we need the education pieces along with the technology to really fulfill the project. So my hope is that ... At this point, we've kind of figured out how to deploy one lab at a time and my hope is that now we can do this at scale. That we can work with local universities, governments, and actually get .... Reach out to kids who need it because I think Oaxaca has one of the lowest literacy rates in all of Mexico. This is definitely communities where most of the kids do not go on to high school and definitely most do not go on to college. So if we can make an impact, show the measure, like be able to measure the impact that we're making, longitudinally, I think that then we can grow and we can scale. So, very hopeful. But this is my passion, right. So it's going back to as a woman, how do you find your passion. I think, find what you're passion is and go for it and that makes things so much easier. And I think there's a lot of opportunities for growth and look for people that will support efforts that you're doing, like DeLisa. And Jigyasa, she's mentoring girls already. >> And I think that that's also a great point too. This is the Open Source way because it is about community building and it's about collaboration and that is also, you're doing these things ... The software is a metaphor for what you're doing in life. >> [Jigyasa and Anvi] Yes. >> Jigyasa, what's next for you? So first, graduate from college, that would be >> Yes. (laughing) >> A big priority. But then where do you hope to work? >> Actually, I want to learn lots and travel the world, know more about everything. That's what Jigyasa means. So Jigyasa means curiousity in Hindi and Sanskrit so I hope I live up to my name and the next few years, I just want to keep the learning mode switched on, be curious, and if I want to do something, at least I'll give it a try so that I do not regret that I never gave a try. So always be curious, interact, and give a try. >> Do you want to continue working in technology or do you want to come to the States? Where do you see your career path? My career path, it's like I'm trying to balance everything. I want to learn more theoretically about computer science and technology. Maybe do a Master's degree further and then move on to industry. Also, I am pretty excited about the research work. I've done a couple of them in Europe, Asbarez, and Canada so I want to do something which is a mix of everything so that it keeps me going. >> Do you see ... These are really social initiatives that you're both working on. Do you see that as sort of a real future for Open Source innovation and technology? We know that Open Source is helping companies grow, get more customers, make more money, improve their bottom lines, but we also see it having this big impact on global and social progress. I mean, how untapped is this, where are we in this? Open Source is a way, it's not a technology, it's a way. It's a way of doing things and thinking about the world. Transparency, using the best ideas, innovating rapidly. We have a lot of complex problems to solve, now and in the future. Using the Open Source way, we will solve those problems more rapidly. Whether it's a technology issue or something entirely outside of technology. >> I agree with that completely. Open Source is a mechanism by which we can accomplsih not just technical innovations, but also social innovations. We have to look at it wholistically. We have to look at the ecosystem wholistically. It's not just technology, it's also society, it's also community, education and how do all the puzzle pieces fit together. JeLisa, we talked a little bit about the challenges of recruiting and retaining women in this industry. What is Red Hat doing to get the best and the brightest and the most talented women engineers? Well, we've come a long way. We have a long way to go. The first thing we wanted to do is to create an ecosystem within Red Hat that was very welcoming and inclusive because if you are recruiting people and they come in and they have an experience that isn't positive, they're going to go right out the door. So the most important thing was shoring up our community and creating an environment. So we focused on that, really, in the beginning. Then we started thinking about outreach. Now, the problem is so complex to solve, right. So we started realizing there's not enough people to outreach to. So now our next step has been to start to go deeper into the school systems and start partnering, We have a partnership with BU and also the city of Boston where we supported girls coming from middle school into a lab environment and doing some fun stuff, they get introduced to technology and we're going to keep our eyes on them and we'd like to recreate this type of experience in multiple places so really go deeper in to help create an interest at the middle school age with girls. Because that's what we understand that's when we need to get them interested. >> And that's when research shows confidence falls off and women, young girls, start raising their hands less in class. >> And all that stuff. Yeah, it's such a difficult issue but we hope that we will make a difference by reaching into the pipeline and then certainly retaining. We develop our women, we really focus on that. We want to support them as leaders and so it's the whole pathway. >> And Jigyasa, are you finding that your mentorship is making a difference for the young women you're working with? Young girls? >> It certainly is because even after the program ends I receive messages and emails from girls and boys alike about the program or how they want to build their own product. So, I remember one of the girls from Romania. I mentored her during a program sponsored by Google and all she wanted to build was a website for herself and she's very young. So she used to text me about what technologies she should use and how is it shaping up. Can I test it for her? So I really liked that even after the program ended, she kept up her spirit and is still continuing with it. >> And as DeLisa says, now you got to keep an eye on her and make sure she stays with it and everything. Well, DeLisa, Anvi, Jigyasa, thank you so much for joining us. Congratulations. >> Thank you so much. >> Well-deserved. >> Thank you. >> Thank you. >> This has been Rebecca Knight at the Red Hat Summit in Boston, Massachusetts. We''ll be back with more after this. (electronic beat)

>> Welcome to the Intel AI Lounge. Today, we're very excited to share with you the Precision Medicine panel discussion. I'll be moderating the session. My name is Kay Erin. I'm the general manager of Health and Life Sciences at Intel. And I'm excited to share with you these three panelists that we have here. First is John Madison. He is a chief information medical officer and he is part of Kaiser Permanente. We're very excited to have you here. Thank you, John. >> Thank you. >> We also have Naveen Rao. He is the VP and general manager for the Artificial Intelligence Solutions at Intel. He's also the former CEO of Nervana, which was acquired by Intel. And we also have Bob Rogers, who's the chief data scientist at our AI solutions group. So, why don't we get started with our questions. I'm going to ask each of the panelists to talk, introduce themselves, as well as talk about how they got started with AI. So why don't we start with John? >> Sure, so can you hear me okay in the back? Can you hear? Okay, cool. So, I am a recovering evolutionary biologist and a recovering physician and a recovering geek. And I implemented the health record system for the first and largest region of Kaiser Permanente. And it's pretty obvious that most of the useful data in a health record, in lies in free text. So I started up a natural language processing team to be able to mine free text about a dozen years ago. So we can do things with that that you can't otherwise get out of health information. I'll give you an example. I read an article online from the New England Journal of Medicine about four years ago that said over half of all people who have had their spleen taken out were not properly vaccinated for a common form of pneumonia, and when your spleen's missing, you must have that vaccine or you die a very sudden death with sepsis. In fact, our medical director in Northern California's father died of that exact same scenario. So, when I read the article, I went to my structured data analytics team and to my natural language processing team and said please show me everybody who has had their spleen taken out and hasn't been appropriately vaccinated and we ran through about 20 million records in about three hours with the NLP team, and it took about three weeks with a structured data analytics team. That sounds counterintuitive but it actually happened that way. And it's not a competition for time only. It's a competition for quality and sensitivity and specificity. So we were able to indentify all of our members who had their spleen taken out, who should've had a pneumococcal vaccine. We vaccinated them and there are a number of people alive today who otherwise would've died absent that capability. So people don't really commonly associate natural language processing with machine learning, but in fact, natural language processing relies heavily and is the first really, highly successful example of machine learning. So we've done dozens of similar projects, mining free text data in millions of records very efficiently, very effectively. But it really helped advance the quality of care and reduce the cost of care. It's a natural step forward to go into the world of personalized medicine with the arrival of a 100-dollar genome, which is actually what it costs today to do a full genome sequence. Microbiomics, that is the ecosystem of bacteria that are in every organ of the body actually. And we know now that there is a profound influence of what's in our gut and how we metabolize drugs, what diseases we get. You can tell in a five year old, whether or not they were born by a vaginal delivery or a C-section delivery by virtue of the bacteria in the gut five years later. So if you look at the complexity of the data that exists in the genome, in the microbiome, in the health record with free text and you look at all the other sources of data like this streaming data from my wearable monitor that I'm part of a research study on Precision Medicine out of Stanford, there is a vast amount of disparate data, not to mention all the imaging, that really can collectively produce much more useful information to advance our understanding of science, and to advance our understanding of every individual. And then we can do the mash up of a much broader range of science in health care with a much deeper sense of data from an individual and to do that with structured questions and structured data is very yesterday. The only way we're going to be able to disambiguate those data and be able to operate on those data in concert and generate real useful answers from the broad array of data types and the massive quantity of data, is to let loose machine learning on all of those data substrates. So my team is moving down that pathway and we're very excited about the future prospects for doing that. >> Yeah, great. I think that's actually some of the things I'm very excited about in the future with some of the technologies we're developing. My background, I started actually being fascinated with computation in biological forms when I was nine. Reading and watching sci-fi, I was kind of a big dork which I pretty much still am. I haven't really changed a whole lot. Just basically seeing that machines really aren't all that different from biological entities, right? We are biological machines and kind of understanding how a computer works and how we engineer those things and trying to pull together concepts that learn from biology into that has always been a fascination of mine. As an undergrad, I was in the EE, CS world. Even then, I did some research projects around that. I worked in the industry for about 10 years designing chips, microprocessors, various kinds of ASICs, and then actually went back to school, quit my job, got a Ph.D. in neuroscience, computational neuroscience, to specifically understand what's the state of the art. What do we really understand about the brain? And are there concepts that we can take and bring back? Inspiration's always been we want to... We watch birds fly around. We want to figure out how to make something that flies. We extract those principles, and then build a plane. Don't necessarily want to build a bird. And so Nervana's really was the combination of all those experiences, bringing it together. Trying to push computation in a new a direction. Now, as part of Intel, we can really add a lot of fuel to that fire. I'm super excited to be part of Intel in that the technologies that we were developing can really proliferate and be applied to health care, can be applied to Internet, can be applied to every facet of our lives. And some of the examples that John mentioned are extremely exciting right now and these are things we can do today. And the generality of these solutions are just really going to hit every part of health care. I mean from a personal viewpoint, my whole family are MDs. I'm sort of the black sheep of the family. I don't have an MD. And it's always been kind of funny to me that knowledge is concentrated in a few individuals. Like you have a rare tumor or something like that, you need the guy who knows how to read this MRI. Why? Why is it like that? Can't we encapsulate that knowledge into a computer or into an algorithm, and democratize it. And the reason we couldn't do it is we just didn't know how. And now we're really getting to a point where we know how to do that. And so I want that capability to go to everybody. It'll bring the cost of healthcare down. It'll make all of us healthier. That affects everything about our society. So that's really what's exciting about it to me. >> That's great. So, as you heard, I'm Bob Rogers. I'm chief data scientist for analytics and artificial intelligence solutions at Intel. My mission is to put powerful analytics in the hands of every decision maker and when I think about Precision Medicine, decision makers are not just doctors and surgeons and nurses, but they're also case managers and care coordinators and probably most of all, patients. So the mission is really to put powerful analytics and AI capabilities in the hands of everyone in health care. It's a very complex world and we need tools to help us navigate it. So my background, I started with a Ph.D. in physics and I was computer modeling stuff, falling into super massive black holes. And there's a lot of applications for that in the real world. No, I'm kidding. (laughter) >> John: There will be, I'm sure. Yeah, one of these days. Soon as we have time travel. Okay so, I actually, about 1991, I was working on my post doctoral research, and I heard about neural networks, these things that could compute the way the brain computes. And so, I started doing some research on that. I wrote some papers and actually, it was an interesting story. The problem that we solved that got me really excited about neural networks, which have become deep learning, my office mate would come in. He was this young guy who was about to go off to grad school. He'd come in every morning. "I hate my project." Finally, after two weeks, what's your project? What's the problem? It turns out he had to circle these little fuzzy spots on these images from a telescope. So they were looking for the interesting things in a sky survey, and he had to circle them and write down their coordinates all summer. Anyone want to volunteer to do that? No? Yeah, he was very unhappy. So we took the first two weeks of data that he created doing his work by hand, and we trained an artificial neural network to do his summer project and finished it in about eight hours of computing. (crowd laughs) And so he was like yeah, this is amazing. I'm so happy. And we wrote a paper. I was the first author of course, because I was the senior guy at age 24. And he was second author. His first paper ever. He was very, very excited. So we have to fast forward about 20 years. His name popped up on the Internet. And so it caught my attention. He had just won the Nobel Prize in physics. (laughter) So that's where artificial intelligence will get you. (laughter) So thanks Naveen. Fast forwarding, I also developed some time series forecasting capabilities that allowed me to create a hedge fund that I ran for 12 years. After that, I got into health care, which really is the center of my passion. Applying health care to figuring out how to get all the data from all those siloed sources, put it into the cloud in a secure way, and analyze it so you can actually understand those cases that John was just talking about. How do you know that that person had had a splenectomy and that they needed to get that pneumovax? You need to be able to search all the data, so we used AI, natural language processing, machine learning, to do that and then two years ago, I was lucky enough to join Intel and, in the intervening time, people like Naveen actually thawed the AI winter and we're really in a spring of amazing opportunities with AI, not just in health care but everywhere, but of course, the health care applications are incredibly life saving and empowering so, excited to be here on this stage with you guys. >> I just want to cue off of your comment about the role of physics in AI and health care. So the field of microbiomics that I referred to earlier, bacteria in our gut. There's more bacteria in our gut than there are cells in our body. There's 100 times more DNA in that bacteria than there is in the human genome. And we're now discovering a couple hundred species of bacteria a year that have never been identified under a microscope just by their DNA. So it turns out the person who really catapulted the study and the science of microbiomics forward was an astrophysicist who did his Ph.D. in Steven Hawking's lab on the collision of black holes and then subsequently, put the other team in a virtual reality, and he developed the first super computing center and so how did he get an interest in microbiomics? He has the capacity to do high performance computing and the kind of advanced analytics that are required to look at a 100 times the volume of 3.2 billion base pairs of the human genome that are represented in the bacteria in our gut, and that has unleashed the whole science of microbiomics, which is going to really turn a lot of our assumptions of health and health care upside down. >> That's great, I mean, that's really transformational. So a lot of data. So I just wanted to let the audience know that we want to make this an interactive session, so I'll be asking for questions in a little bit, but I will start off with one question so that you can think about it. So I wanted to ask you, it looks like you've been thinking a lot about AI over the years. And I wanted to understand, even though AI's just really starting in health care, what are some of the new trends or the changes that you've seen in the last few years that'll impact how AI's being used going forward? >> So I'll start off. There was a paper published by a guy by the name of Tegmark at Harvard last summer that, for the first time, explained why neural networks are efficient beyond any mathematical model we predict. And the title of the paper's fun. It's called Deep Learning Versus Cheap Learning. So there were two sort of punchlines of the paper. One is is that the reason that mathematics doesn't explain the efficiency of neural networks is because there's a higher order of mathematics called physics. And the physics of the underlying data structures determined how efficient you could mine those data using machine learning tools. Much more so than any mathematical modeling. And so the second thing that was a reel from that paper is that the substrate of the data that you're operating on and the natural physics of those data have inherent levels of complexity that determine whether or not a 12th layer of neural net will get you where you want to go really fast, because when you do the modeling, for those math geeks in the audience, a factorial. So if there's 12 layers, there's 12 factorial permutations of different ways you could sequence the learning through those data. When you have 140 layers of a neural net, it's a much, much, much bigger number of permutations and so you end up being hardware-bound. And so, what Max Tegmark basically said is you can determine whether to do deep learning or cheap learning based upon the underlying physics of the data substrates you're operating on and have a good insight into how to optimize your hardware and software approach to that problem. >> So another way to put that is that neural networks represent the world in the way the world is sort of built. >> Exactly. >> It's kind of hierarchical. It's funny because, sort of in retrospect, like oh yeah, that kind of makes sense. But when you're thinking about it mathematically, we're like well, anything... The way a neural can represent any mathematical function, therfore, it's fully general. And that's the way we used to look at it, right? So now we're saying, well actually decomposing the world into different types of features that are layered upon each other is actually a much more efficient, compact representation of the world, right? I think this is actually, precisely the point of kind of what you're getting at. What's really exciting now is that what we were doing before was sort of building these bespoke solutions for different kinds of data. NLP, natural language processing. There's a whole field, 25 plus years of people devoted to figuring out features, figuring out what structures make sense in this particular context. Those didn't carry over at all to computer vision. Didn't carry over at all to time series analysis. Now, with neural networks, we've seen it at Nervana, and now part of Intel, solving customers' problems. We apply a very similar set of techniques across all these different types of data domains and solve them. All data in the real world seems to be hierarchical. You can decompose it into this hierarchy. And it works really well. Our brains are actually general structures. As a neuroscientist, you can look at different parts of your brain and there are differences. Something that takes in visual information, versus auditory information is slightly different but they're much more similar than they are different. So there is something invariant, something very common between all of these different modalities and we're starting to learn that. And this is extremely exciting to me trying to understand the biological machine that is a computer, right? We're figurig it out, right? >> One of the really fun things that Ray Chrisfall likes to talk about is, and it falls in the genre of biomimmicry, and how we actually replicate biologic evolution in our technical solutions so if you look at, and we're beginning to understand more and more how real neural nets work in our cerebral cortex. And it's sort of a pyramid structure so that the first pass of a broad base of analytics, it gets constrained to the next pass, gets constrained to the next pass, which is how information is processed in the brain. So we're discovering increasingly that what we've been evolving towards, in term of architectures of neural nets, is approximating the architecture of the human cortex and the more we understand the human cortex, the more insight we get to how to optimize neural nets, so when you think about it, with millions of years of evolution of how the cortex is structured, it shouldn't be a surprise that the optimization protocols, if you will, in our genetic code are profoundly efficient in how they operate. So there's a real role for looking at biologic evolutionary solutions, vis a vis technical solutions, and there's a friend of mine who worked with who worked with George Church at Harvard and actually published a book on biomimmicry and they wrote the book completely in DNA so if all of you have your home DNA decoder, you can actually read the book on your DNA reader, just kidding. >> There's actually a start up I just saw in the-- >> Read-Write DNA, yeah. >> Actually it's a... He writes something. What was it? (response from crowd member) Yeah, they're basically encoding information in DNA as a storage medium. (laughter) The company, right? >> Yeah, that same friend of mine who coauthored that biomimmicry book in DNA also did the estimate of the density of information storage. So a cubic centimeter of DNA can store an hexabyte of data. I mean that's mind blowing. >> Naveen: Highly done soon. >> Yeah that's amazing. Also you hit upon a really important point there, that one of the things that's changed is... Well, there are two major things that have changed in my perception from let's say five to 10 years ago, when we were using machine learning. You could use data to train models and make predictions to understand complex phenomena. But they had limited utility and the challenge was that if I'm trying to build on these things, I had to do a lot of work up front. It was called feature engineering. I had to do a lot of work to figure out what are the key attributes of that data? What are the 10 or 20 or 100 pieces of information that I should pull out of the data to feed to the model, and then the model can turn it into a predictive machine. And so, what's really exciting about the new generation of machine learning technology, and particularly deep learning, is that it can actually learn from example data those features without you having to do any preprogramming. That's why Naveen is saying you can take the same sort of overall approach and apply it to a bunch of different problems. Because you're not having to fine tune those features. So at the end of the day, the two things that have changed to really enable this evolution is access to more data, and I'd be curious to hear from you where you're seeing data come from, what are the strategies around that. So access to data, and I'm talking millions of examples. So 10,000 examples most times isn't going to cut it. But millions of examples will do it. And then, the other piece is the computing capability to actually take millions of examples and optimize this algorithm in a single lifetime. I mean, back in '91, when I started, we literally would have thousands of examples and it would take overnight to run the thing. So now in the world of millions, and you're putting together all of these combinations, the computing has changed a lot. I know you've made some revolutionary advances in that. But I'm curious about the data. Where are you seeing interesting sources of data for analytics? >> So I do some work in the genomics space and there are more viable permutations of the human genome than there are people who have ever walked the face of the earth. And the polygenic determination of a phenotypic expression translation, what are genome does to us in our physical experience in health and disease is determined by many, many genes and the interaction of many, many genes and how they are up and down regulated. And the complexity of disambiguating which 27 genes are affecting your diabetes and how are they up and down regulated by different interventions is going to be different than his. It's going to be different than his. And we already know that there's four or five distinct genetic subtypes of type II diabetes. So physicians still think there's one disease called type II diabetes. There's actually at least four or five genetic variants that have been identified. And so, when you start thinking about disambiguating, particularly when we don't know what 95 percent of DNA does still, what actually is the underlining cause, it will require this massive capability of developing these feature vectors, sometimes intuiting it, if you will, from the data itself. And other times, taking what's known knowledge to develop some of those feature vectors, and be able to really understand the interaction of the genome and the microbiome and the phenotypic data. So the complexity is high and because the variation complexity is high, you do need these massive members. Now I'm going to make a very personal pitch here. So forgive me, but if any of you have any role in policy at all, let me tell you what's happening right now. The Genomic Information Nondiscrimination Act, so called GINA, written by a friend of mine, passed a number of years ago, says that no one can be discriminated against for health insurance based upon their genomic information. That's cool. That should allow all of you to feel comfortable donating your DNA to science right? Wrong. You are 100% unprotected from discrimination for life insurance, long term care and disability. And it's being practiced legally today and there's legislation in the House, in mark up right now to completely undermine the existing GINA legislation and say that whenever there's another applicable statute like HIPAA, that the GINA is irrelevant, that none of the fines and penalties are applicable at all. So we need a ton of data to be able to operate on. We will not be getting a ton of data to operate on until we have the kind of protection we need to tell people, you can trust us. You can give us your data, you will not be subject to discrimination. And that is not the case today. And it's being further undermined. So I want to make a plea to any of you that have any policy influence to go after that because we need this data to help the understanding of human health and disease and we're not going to get it when people look behind the curtain and see that discrimination is occurring today based upon genetic information. >> Well, I don't like the idea of being discriminated against based on my DNA. Especially given how little we actually know. There's so much complexity in how these things unfold in our own bodies, that I think anything that's being done is probably childishly immature and oversimplifying. So it's pretty rough. >> I guess the translation here is that we're all unique. It's not just a Disney movie. (laughter) We really are. And I think one of the strengths that I'm seeing, kind of going back to the original point, of these new techniques is it's going across different data types. It will actually allow us to learn more about the uniqueness of the individual. It's not going to be just from one data source. They were collecting data from many different modalities. We're collecting behavioral data from wearables. We're collecting things from scans, from blood tests, from genome, from many different sources. The ability to integrate those into a unified picture, that's the important thing that we're getting toward now. That's what I think is going to be super exciting here. Think about it, right. I can tell you to visual a coin, right? You can visualize a coin. Not only do you visualize it. You also know what it feels like. You know how heavy it is. You have a mental model of that from many different perspectives. And if I take away one of those senses, you can still identify the coin, right? If I tell you to put your hand in your pocket, and pick out a coin, you probably can do that with 100% reliability. And that's because we have this generalized capability to build a model of something in the world. And that's what we need to do for individuals is actually take all these different data sources and come up with a model for an individual and you can actually then say what drug works best on this. What treatment works best on this? It's going to get better with time. It's not going to be perfect, because this is what a doctor does, right? A doctor who's very experienced, you're a practicing physician right? Back me up here. That's what you're doing. You basically have some categories. You're taking information from the patient when you talk with them, and you're building a mental model. And you apply what you know can work on that patient, right? >> I don't have clinic hours anymore, but I do take care of many friends and family. (laughter) >> You used to, you used to. >> I practiced for many years before I became a full-time geek. >> I thought you were a recovering geek. >> I am. (laughter) I do more policy now. >> He's off the wagon. >> I just want to take a moment and see if there's anyone from the audience who would like to ask, oh. Go ahead. >> We've got a mic here, hang on one second. >> I have tons and tons of questions. (crosstalk) Yes, so first of all, the microbiome and the genome are really complex. You already hit about that. Yet most of the studies we do are small scale and we have difficulty repeating them from study to study. How are we going to reconcile all that and what are some of the technical hurdles to get to the vision that you want? >> So primarily, it's been the cost of sequencing. Up until a year ago, it's $1000, true cost. Now it's $100, true cost. And so that barrier is going to enable fairly pervasive testing. It's not a real competitive market becaue there's one sequencer that is way ahead of everybody else. So the price is not $100 yet. The cost is below $100. So as soon as there's competition to drive the cost down, and hopefully, as soon as we all have the protection we need against discrimination, as I mentioned earlier, then we will have large enough sample sizes. And so, it is our expectation that we will be able to pool data from local sources. I chair the e-health work group at the Global Alliance for Genomics and Health which is working on this very issue. And rather than pooling all the data into a single, common repository, the strategy, and we're developing our five-year plan in a month in London, but the goal is to have a federation of essentially credentialed data enclaves. That's a formal method. HHS already does that so you can get credentialed to search all the data that Medicare has on people that's been deidentified according to HIPPA. So we want to provide the same kind of service with appropriate consent, at an international scale. And there's a lot of nations that are talking very much about data nationality so that you can't export data. So this approach of a federated model to get at data from all the countries is important. The other thing is a block-chain technology is going to be very profoundly useful in this context. So David Haussler of UC Santa Cruz is right now working on a protocol using an open block-chain, public ledger, where you can put out. So for any typical cancer, you may have a half dozen, what are called sematic variance. Cancer is a genetic disease so what has mutated to cause it to behave like a cancer? And if we look at those biologically active sematic variants, publish them on a block chain that's public, so there's not enough data there to reidentify the patient. But if I'm a physician treating a woman with breast cancer, rather than say what's the protocol for treating a 50-year-old woman with this cell type of cancer, I can say show me all the people in the world who have had this cancer at the age of 50, wit these exact six sematic variants. Find the 200 people worldwide with that. Ask them for consent through a secondary mechanism to donate everything about their medical record, pool that information of the core of 200 that exactly resembles the one sitting in front of me, and find out, of the 200 ways they were treated, what got the best results. And so, that's the kind of future where a distributed, federated architecture will allow us to query and obtain a very, very relevant cohort, so we can basically be treating patients like mine, sitting right in front of me. Same thing applies for establishing research cohorts. There's some very exciting stuff at the convergence of big data analytics, machine learning, and block chaining. >> And this is an area that I'm really excited about and I think we're excited about generally at Intel. They actually have something called the Collaborative Cancer Cloud, which is this kind of federated model. We have three different academic research centers. Each of them has a very sizable and valuable collection of genomic data with phenotypic annotations. So you know, pancreatic cancer, colon cancer, et cetera, and we've actually built a secure computing architecture that can allow a person who's given the right permissions by those organizations to ask a specific question of specific data without ever sharing the data. So the idea is my data's really important to me. It's valuable. I want us to be able to do a study that gets the number from the 20 pancreatic cancer patients in my cohort, up to the 80 that we have in the whole group. But I can't do that if I'm going to just spill my data all over the world. And there are HIPAA and compliance reasons for that. There are business reasons for that. So what we've built at Intel is this platform that allows you to do different kinds of queries on this genetic data. And reach out to these different sources without sharing it. And then, the work that I'm really involved in right now and that I'm extremely excited about... This also touches on something that both of you said is it's not sufficient to just get the genome sequences. You also have to have the phenotypic data. You have to know what cancer they've had. You have to know that they've been treated with this drug and they've survived for three months or that they had this side effect. That clinical data also needs to be put together. It's owned by other organizations, right? Other hospitals. So the broader generalization of the Collaborative Cancer Cloud is something we call the data exchange. And it's a misnomer in a sense that we're not actually exchanging data. We're doing analytics on aggregated data sets without sharing it. But it really opens up a world where we can have huge populations and big enough amounts of data to actually train these models and draw the thread in. Of course, that really then hits home for the techniques that Nervana is bringing to the table, and of course-- >> Stanford's one of your academic medical centers? >> Not for that Collaborative Cancer Cloud. >> The reason I mentioned Standford is because the reason I'm wearing this FitBit is because I'm a research subject at Mike Snyder's, the chair of genetics at Stanford, IPOP, intrapersonal omics profile. So I was fully sequenced five years ago and I get four full microbiomes. My gut, my mouth, my nose, my ears. Every three months and I've done that for four years now. And about a pint of blood. And so, to your question of the density of data, so a lot of the problem with applying these techniques to health care data is that it's basically a sparse matrix and there's a lot of discontinuities in what you can find and operate on. So what Mike is doing with the IPOP study is much the same as you described. Creating a highly dense longitudinal set of data that will help us mitigate the sparse matrix problem. (low volume response from audience member) Pardon me. >> What's that? (low volume response) (laughter) >> Right, okay. >> John: Lost the school sample. That's got to be a new one I've heard now. >> Okay, well, thank you so much. That was a great question. So I'm going to repeat this and ask if there's another question. You want to go ahead? >> Hi, thanks. So I'm a journalist and I report a lot on these neural networks, a system that's beter at reading mammograms than your human radiologists. Or a system that's better at predicting which patients in the ICU will get sepsis. These sort of fascinating academic studies that I don't really see being translated very quickly into actual hospitals or clinical practice. Seems like a lot of the problems are regulatory, or liability, or human factors, but how do you get past that and really make this stuff practical? >> I think there's a few things that we can do there and I think the proof points of the technology are really important to start with in this specific space. In other places, sometimes, you can start with other things. But here, there's a real confidence problem when it comes to health care, and for good reason. We have doctors trained for many, many years. School and then residencies and other kinds of training. Because we are really, really conservative with health care. So we need to make sure that technology's well beyond just the paper, right? These papers are proof points. They get people interested. They even fuel entire grant cycles sometimes. And that's what we need to happen. It's just an inherent problem, its' going to take a while. To get those things to a point where it's like well, I really do trust what this is saying. And I really think it's okay to now start integrating that into our standard of care. I think that's where you're seeing it. It's frustrating for all of us, believe me. I mean, like I said, I think personally one of the biggest things, I want to have an impact. Like when I go to my grave, is that we used machine learning to improve health care. We really do feel that way. But it's just not something we can do very quickly and as a business person, I don't actually look at those use cases right away because I know the cycle is just going to be longer. >> So to your point, the FDA, for about four years now, has understood that the process that has been given to them by their board of directors, otherwise known as Congress, is broken. And so they've been very actively seeking new models of regulation and what's really forcing their hand is regulation of devices and software because, in many cases, there are black box aspects of that and there's a black box aspect to machine learning. Historically, Intel and others are making inroads into providing some sort of traceability and transparency into what happens in that black box rather than say, overall we get better results but once in a while we kill somebody. Right? So there is progress being made on that front. And there's a concept that I like to use. Everyone knows Ray Kurzweil's book The Singularity Is Near? Well, I like to think that diadarity is near. And the diadarity is where you have human transparency into what goes on in the black box and so maybe Bob, you want to speak a little bit about... You mentioned that, in a prior discussion, that there's some work going on at Intel there. >> Yeah, absolutely. So we're working with a number of groups to really build tools that allow us... In fact Naveen probably can talk in even more detail than I can, but there are tools that allow us to actually interrogate machine learning and deep learning systems to understand, not only how they respond to a wide variety of situations but also where are there biases? I mean, one of the things that's shocking is that if you look at the clinical studies that our drug safety rules are based on, 50 year old white guys are the peak of that distribution, which I don't see any problem with that, but some of you out there might not like that if you're taking a drug. So yeah, we want to understand what are the biases in the data, right? And so, there's some new technologies. There's actually some very interesting data-generative technologies. And this is something I'm also curious what Naveen has to say about, that you can generate from small sets of observed data, much broader sets of varied data that help probe and fill in your training for some of these systems that are very data dependent. So that takes us to a place where we're going to start to see deep learning systems generating data to train other deep learning systems. And they start to sort of go back and forth and you start to have some very nice ways to, at least, expose the weakness of these underlying technologies. >> And that feeds back to your question about regulatory oversight of this. And there's the fascinating, but little known origin of why very few women are in clinical studies. Thalidomide causes birth defects. So rather than say pregnant women can't be enrolled in drug trials, they said any woman who is at risk of getting pregnant cannot be enrolled. So there was actually a scientific meritorious argument back in the day when they really didn't know what was going to happen post-thalidomide. So it turns out that the adverse, unintended consequence of that decision was we don't have data on women and we know in certain drugs, like Xanax, that the metabolism is so much slower, that the typical dosing of Xanax is women should be less than half of that for men. And a lot of women have had very serious adverse effects by virtue of the fact that they weren't studied. So the point I want to illustrate with that is that regulatory cycles... So people have known for a long time that was like a bad way of doing regulations. It should be changed. It's only recently getting changed in any meaningful way. So regulatory cycles and legislative cycles are incredibly slow. The rate of exponential growth in technology is exponential. And so there's impedance mismatch between the cycle time for regulation cycle time for innovation. And what we need to do... I'm working with the FDA. I've done four workshops with them on this very issue. Is that they recognize that they need to completely revitalize their process. They're very interested in doing it. They're not resisting it. People think, oh, they're bad, the FDA, they're resisting. Trust me, there's nobody on the planet who wants to revise these review processes more than the FDA itself. And so they're looking at models and what I recommended is global cloud sourcing and the FDA could shift from a regulatory role to one of doing two things, assuring the people who do their reviews are competent, and assuring that their conflicts of interest are managed, because if you don't have a conflict of interest in this very interconnected space, you probably don't know enough to be a reviewer. So there has to be a way to manage the conflict of interest and I think those are some of the keypoints that the FDA is wrestling with because there's type one and type two errors. If you underregulate, you end up with another thalidomide and people born without fingers. If you overregulate, you prevent life saving drugs from coming to market. So striking that balance across all these different technologies is extraordinarily difficult. If it were easy, the FDA would've done it four years ago. It's very complicated. >> Jumping on that question, so all three of you are in some ways entrepreneurs, right? Within your organization or started companies. And I think it would be good to talk a little bit about the business opportunity here, where there's a huge ecosystem in health care, different segments, biotech, pharma, insurance payers, etc. Where do you see is the ripe opportunity or industry, ready to really take this on and to make AI the competitive advantage. >> Well, the last question also included why aren't you using the result of the sepsis detection? We do. There were six or seven published ways of doing it. We did our own data, looked at it, we found a way that was superior to all the published methods and we apply that today, so we are actually using that technology to change clinical outcomes. As far as where the opportunities are... So it's interesting. Because if you look at what's going to be here in three years, we're not going to be using those big data analytics models for sepsis that we are deploying today, because we're just going to be getting a tiny aliquot of blood, looking for the DNA or RNA of any potential infection and we won't have to infer that there's a bacterial infection from all these other ancillary, secondary phenomenon. We'll see if the DNA's in the blood. So things are changing so fast that the opportunities that people need to look for are what are generalizable and sustainable kind of wins that are going to lead to a revenue cycle that are justified, a venture capital world investing. So there's a lot of interesting opportunities in the space. But I think some of the biggest opportunities relate to what Bob has talked about in bringing many different disparate data sources together and really looking for things that are not comprehensible in the human brain or in traditional analytic models. >> I think we also got to look a little bit beyond direct care. We're talking about policy and how we set up standards, these kinds of things. That's one area. That's going to drive innovation forward. I completely agree with that. Direct care is one piece. How do we scale out many of the knowledge kinds of things that are embedded into one person's head and get them out to the world, democratize that. Then there's also development. The underlying technology's of medicine, right? Pharmaceuticals. The traditional way that pharmaceuticals is developed is actually kind of funny, right? A lot of it was started just by chance. Penicillin, a very famous story right? It's not that different today unfortunately, right? It's conceptually very similar. Now we've got more science behind it. We talk about domains and interactions, these kinds of things but fundamentally, the problem is what we in computer science called NP hard, it's too difficult to model. You can't solve it analytically. And this is true for all these kinds of natural sorts of problems by the way. And so there's a whole field around this, molecular dynamics and modeling these sorts of things, that are actually being driven forward by these AI techniques. Because it turns out, our brain doesn't do magic. It actually doesn't solve these problems. It approximates them very well. And experience allows you to approximate them better and better. Actually, it goes a little bit to what you were saying before. It's like simulations and forming your own networks and training off each other. There are these emerging dynamics. You can simulate steps of physics. And you come up with a system that's much too complicated to ever solve. Three pool balls on a table is one such system. It seems pretty simple. You know how to model that, but it actual turns out you can't predict where a balls going to be once you inject some energy into that table. So something that simple is already too complex. So neural network techniques actually allow us to start making those tractable. These NP hard problems. And things like molecular dynamics and actually understanding how different medications and genetics will interact with each other is something we're seeing today. And so I think there's a huge opportunity there. We've actually worked with customers in this space. And I'm seeing it. Like Rosch is acquiring a few different companies in space. They really want to drive it forward, using big data to drive drug development. It's kind of counterintuitive. I never would've thought it had I not seen it myself. >> And there's a big related challenge. Because in personalized medicine, there's smaller and smaller cohorts of people who will benefit from a drug that still takes two billion dollars on average to develop. That is unsustainable. So there's an economic imperative of overcoming the cost and the cycle time for drug development. >> I want to take a go at this question a little bit differently, thinking about not so much where are the industry segments that can benefit from AI, but what are the kinds of applications that I think are most impactful. So if this is what a skilled surgeon needs to know at a particular time to care properly for a patient, this is where most, this area here, is where most surgeons are. They are close to the maximum knowledge and ability to assimilate as they can be. So it's possible to build complex AI that can pick up on that one little thing and move them up to here. But it's not a gigantic accelerator, amplifier of their capability. But think about other actors in health care. I mentioned a couple of them earlier. Who do you think the least trained actor in health care is? >> John: Patients. >> Yes, the patients. The patients are really very poorly trained, including me. I'm abysmal at figuring out who to call and where to go. >> Naveen: You know as much the doctor right? (laughing) >> Yeah, that's right. >> My doctor friends always hate that. Know your diagnosis, right? >> Yeah, Dr. Google knows. So the opportunities that I see that are really, really exciting are when you take an AI agent, like sometimes I like to call it contextually intelligent agent, or a CIA, and apply it to a problem where a patient has a complex future ahead of them that they need help navigating. And you use the AI to help them work through. Post operative. You've got PT. You've got drugs. You've got to be looking for side effects. An agent can actually help you navigate. It's like your own personal GPS for health care. So it's giving you the inforamation that you need about you for your care. That's my definition of Precision Medicine. And it can include genomics, of course. But it's much bigger. It's that broader picture and I think that a sort of agent way of thinking about things and filling in the gaps where there's less training and more opportunity, is very exciting. >> Great start up idea right there by the way. >> Oh yes, right. We'll meet you all out back for the next start up. >> I had a conversation with the head of the American Association of Medical Specialties just a couple of days ago. And what she was saying, and I'm aware of this phenomenon, but all of the medical specialists are saying, you're killing us with these stupid board recertification trivia tests that you're giving us. So if you're a cardiologist, you have to remember something that happens in one in 10 million people, right? And they're saying that irrelevant anymore, because we've got advanced decision support coming. We have these kinds of analytics coming. Precisely what you're saying. So it's human augmentation of decision support that is coming at blazing speed towards health care. So in that context, it's much more important that you have a basic foundation, you know how to think, you know how to learn, and you know where to look. So we're going to be human-augmented learning systems much more so than in the past. And so the whole recertification process is being revised right now. (inaudible audience member speaking) Speak up, yeah. (person speaking) >> What makes it fathomable is that you can-- (audience member interjects inaudibly) >> Sure. She was saying that our brain is really complex and large and even our brains don't know how our brains work, so... are there ways to-- >> What hope do we have kind of thing? (laughter) >> It's a metaphysical question. >> It circles all the way down, exactly. It's a great quote. I mean basically, you can decompose every system. Every complicated system can be decomposed into simpler, emergent properties. You lose something perhaps with each of those, but you get enough to actually understand most of the behavior. And that's really how we understand the world. And that's what we've learned in the last few years what neural network techniques can allow us to do. And that's why our brain can understand our brain. (laughing) >> Yeah, I'd recommend reading Chris Farley's last book because he addresses that issue in there very elegantly. >> Yeah we're seeing some really interesting technologies emerging right now where neural network systems are actually connecting other neural network systems in networks. You can see some very compelling behavior because one of the things I like to distinguish AI versus traditional analytics is we used to have question-answering systems. I used to query a database and create a report to find out how many widgets I sold. Then I started using regression or machine learning to classify complex situations from this is one of these and that's one of those. And then as we've moved more recently, we've got these AI-like capabilities like being able to recognize that there's a kitty in the photograph. But if you think about it, if I were to show you a photograph that happened to have a cat in it, and I said, what's the answer, you'd look at me like, what are you talking about? I have to know the question. So where we're cresting with these connected sets of neural systems, and with AI in general, is that the systems are starting to be able to, from the context, understand what the question is. Why would I be asking about this picture? I'm a marketing guy, and I'm curious about what Legos are in the thing or what kind of cat it is. So it's being able to ask a question, and then take these question-answering systems, and actually apply them so that's this ability to understand context and ask questions that we're starting to see emerge from these more complex hierarchical neural systems. >> There's a person dying to ask a question. >> Sorry. You have hit on several different topics that all coalesce together. You mentioned personalized models. You mentioned AI agents that could help you as you're going through a transitionary period. You mentioned data sources, especially across long time periods. Who today has access to enough data to make meaningful progress on that, not just when you're dealing with an issue, but day-to-day improvement of your life and your health? >> Go ahead, great question. >> That was a great question. And I don't think we have a good answer to it. (laughter) I'm sure John does. Well, I think every large healthcare organization and various healthcare consortiums are working very hard to achieve that goal. The problem remains in creating semantic interoperatability. So I spent a lot of my career working on semantic interoperatability. And the problem is that if you don't have well-defined, or self-defined data, and if you don't have well-defined and documented metadata, and you start operating on it, it's real easy to reach false conclusions and I can give you a classic example. It's well known, with hundreds of studies looking at when you give an antibiotic before surgery and how effective it is in preventing a post-op infection. Simple question, right? So most of the literature done prosectively was done in institutions where they had small sample sizes. So if you pool that, you get a little bit more noise, but you get a more confirming answer. What was done at a very large, not my own, but a very large institution... I won't name them for obvious reasons, but they pooled lots of data from lots of different hospitals, where the data definitions and the metadata were different. Two examples. When did they indicate the antibiotic was given? Was it when it was ordered, dispensed from the pharmacy, delivered to the floor, brought to the bedside, put in the IV, or the IV starts flowing? Different hospitals used a different metric of when it started. When did surgery occur? When they were wheeled into the OR, when they were prepped and drapped, when the first incision occurred? All different. And they concluded quite dramatically that it didn't matter when you gave the pre-op antibiotic and whether or not you get a post-op infection. And everybody who was intimate with the prior studies just completely ignored and discounted that study. It was wrong. And it was wrong because of the lack of commonality and the normalization of data definitions and metadata definitions. So because of that, this problem is much more challenging than you would think. If it were so easy as to put all these data together and operate on it, normalize and operate on it, we would've done that a long time ago. It's... Semantic interoperatability remains a big problem and we have a lot of heavy lifting ahead of us. I'm working with the Global Alliance, for example, of Genomics and Health. There's like 30 different major ontologies for how you represent genetic information. And different institutions are using different ones in different ways in different versions over different periods of time. That's a mess. >> Our all those issues applicable when you're talking about a personalized data set versus a population? >> Well, so N of 1 studies and single-subject research is an emerging field of statistics. So there's some really interesting new models like step wedge analytics for doing that on small sample sizes, recruiting people asynchronously. There's single-subject research statistics. You compare yourself with yourself at a different point in time, in a different context. So there are emerging statistics to do that and as long as you use the same sensor, you won't have a problem. But people are changing their remote sensors and you're getting different data. It's measured in different ways with different sensors at different normalization and different calibration. So yes. It even persists in the N of 1 environment. >> Yeah, you have to get started with a large N that you can apply to the N of 1. I'm actually going to attack your question from a different perspective. So who has the data? The millions of examples to train a deep learning system from scratch. It's a very limited set right now. Technology such as the Collaborative Cancer Cloud and The Data Exchange are definitely impacting that and creating larger and larger sets of critical mass. And again, not withstanding the very challenging semantic interoperability questions. But there's another opportunity Kay asked about what's changed recently. One of the things that's changed in deep learning is that we now have modules that have been trained on massive data sets that are actually very smart as certain kinds of problems. So, for instance, you can go online and find deep learning systems that actually can recognize, better than humans, whether there's a cat, dog, motorcycle, house, in a photograph. >> From Intel, open source. >> Yes, from Intel, open source. So here's what happens next. Because most of that deep learning system is very expressive. That combinatorial mixture of features that Naveen was talking about, when you have all these layers, there's a lot of features there. They're actually very general to images, not just finding cats, dogs, trees. So what happens is you can do something called transfer learning, where you take a small or modest data set and actually reoptimize it for your specific problem very, very quickly. And so we're starting to see a place where you can... On one end of the spectrum, we're getting access to the computing capabilities and the data to build these incredibly expressive deep learning systems. And over here on the right, we're able to start using those deep learning systems to solve custom versions of problems. Just last weekend or two weekends ago, in 20 minutes, I was able to take one of those general systems and create one that could recognize all different kinds of flowers. Very subtle distinctions, that I would never be able to know on my own. But I happen to be able to get the data set and literally, it took 20 minutes and I have this vision system that I could now use for a specific problem. I think that's incredibly profound and I think we're going to see this spectrum of wherever you are in your ability to get data and to define problems and to put hardware in place to see really neat customizations and a proliferation of applications of this kind of technology. >> So one other trend I think, I'm very hopeful about it... So this is a hard problem clearly, right? I mean, getting data together, formatting it from many different sources, it's one of these things that's probably never going to happen perfectly. But one trend I think that is extremely hopeful to me is the fact that the cost of gathering data has precipitously dropped. Building that thing is almost free these days. I can write software and put it on 100 million cell phones in an instance. You couldn't do that five years ago even right? And so, the amount of information we can gain from a cell phone today has gone up. We have more sensors. We're bringing online more sensors. People have Apple Watches and they're sending blood data back to the phone, so once we can actually start gathering more data and do it cheaper and cheaper, it actually doesn't matter where the data is. I can write my own app. I can gather that data and I can start driving the correct inferences or useful inferences back to you. So that is a positive trend I think here and personally, I think that's how we're going to solve it, is by gathering from that many different sources cheaply. >> Hi, my name is Pete. I've very much enjoyed the conversation so far but I was hoping perhaps to bring a little bit more focus into Precision Medicine and ask two questions. Number one, how have you applied the AI technologies as you're emerging so rapidly to your natural language processing? I'm particularly interested in, if you look at things like Amazon Echo or Siri, or the other voice recognition systems that are based on AI, they've just become incredibly accurate and I'm interested in specifics about how I might use technology like that in medicine. So where would I find a medical nomenclature and perhaps some reference to a back end that works that way? And the second thing is, what specifically is Intel doing, or making available? You mentioned some open source stuff on cats and dogs and stuff but I'm the doc, so I'm looking at the medical side of that. What are you guys providing that would allow us who are kind of geeks on the software side, as well as being docs, to experiment a little bit more thoroughly with AI technology? Google has a free AI toolkit. Several other people have come out with free AI toolkits in order to accelerate that. There's special hardware now with graphics, and different processors, hitting amazing speeds. And so I was wondering, where do I go in Intel to find some of those tools and perhaps learn a bit about the fantastic work that you guys are already doing at Kaiser? >> Let me take that first part and then we'll be able to talk about the MD part. So in terms of technology, this is what's extremely exciting now about what Intel is focusing on. We're providing those pieces. So you can actually assemble and build the application. How you build that application specific for MDs and the use cases is up to you or the one who's filling out the application. But we're going to power that technology for multiple perspectives. So Intel is already the main force behind The Data Center, right? Cloud computing, all this is already Intel. We're making that extremely amenable to AI and setting the standard for AI in the future, so we can do that from a number of different mechanisms. For somebody who wants to develop an application quickly, we have hosted solutions. Intel Nervana is kind of the brand for these kinds of things. Hosted solutions will get you going very quickly. Once you get to a certain level of scale, where costs start making more sense, things can be bought on premise. We're supplying that. We're also supplying software that makes that transition essentially free. Then taking those solutions that you develop in the cloud, or develop in The Data Center, and actually deploying them on device. You want to write something on your smartphone or PC or whatever. We're actually providing those hooks as well, so we want to make it very easy for developers to take these pieces and actually build solutions out of them quickly so you probably don't even care what hardware it's running on. You're like here's my data set, this is what I want to do. Train it, make it work. Go fast. Make my developers efficient. That's all you care about, right? And that's what we're doing. We're taking it from that point at how do we best do that? We're going to provide those technologies. In the next couple of years, there's going to be a lot of new stuff coming from Intel. >> Do you want to talk about AI Academy as well? >> Yeah, that's a great segway there. In addition to this, we have an entire set of tutorials and other online resources and things we're going to be bringing into the academic world for people to get going quickly. So that's not just enabling them on our tools, but also just general concepts. What is a neural network? How does it work? How does it train? All of these things are available now and we've made a nice, digestible class format that you can actually go and play with. >> Let me give a couple of quick answers in addition to the great answers already. So you're asking why can't we use medical terminology and do what Alexa does? Well, no, you may not be aware of this, but Andrew Ian, who was the AI guy at Google, who was recruited by Google, they have a medical chat bot in China today. I don't speak Chinese. I haven't been able to use it yet. There are two similar initiatives in this country that I know of. There's probably a dozen more in stealth mode. But Lumiata and Health Cap are doing chat bots for health care today, using medical terminology. You have the compound problem of semantic normalization within language, compounded by a cross language. I've done a lot of work with an international organization called Snowmed, which translates medical terminology. So you're aware of that. We can talk offline if you want, because I'm pretty deep into the semantic space. >> Go google Intel Nervana and you'll see all the websites there. It's intel.com/ai or nervanasys.com. >> Okay, great. Well this has been fantastic. I want to, first of all, thank all the people here for coming and asking great questions. I also want to thank our fantastic panelists today. (applause) >> Thanks, everyone. >> Thank you. >> And lastly, I just want to share one bit of information. We will have more discussions on AI next Tuesday at 9:30 AM. Diane Bryant, who is our general manager of Data Centers Group will be here to do a keynote. So I hope you all get to join that. Thanks for coming. (applause) (light electronic music)