(upbeat music) >> Welcome back to The Cubes coverage of WIDS 2023 the eighth Annual Women in Data Science Conference which is held at Stanford University. I'm your host, Lisa Martin. I'm really excited to be having some great co-hosts today. I've got Hannah Freytag with me, who is a data journalism master student at Stanford. We have yet another inspiring woman in technology to bring to you today. Kelly Hoang joins us, data scientist at Gilead. It's so great to have you, Kelly. >> Hi, thank you for having me today. I'm super excited to be here and share my journey with you guys. >> Let's talk about that journey. You recently got your PhD in information sciences, congratulations. >> Thank you. Yes, I just graduated, I completed my PhD in information sciences from University of Illinois Urbana-Champaign. And right now I moved to Bay Area and started my career as a data scientist at Gilead. >> And you're in better climate. Well, we do get snow here. >> Kelly: That's true. >> We proved that the last... And data science can show us all the climate change that's going on here. >> That's true. That's the topic of the data fund this year, right? To understand the changes in the climate. >> Yeah. Talk a little bit about your background. You were mentioning before we went live that you come from a whole family of STEM students. So you had that kind of in your DNA. >> Well, I consider myself maybe I was a lucky case. I did grew up in a family in the STEM environment. My dad actually was a professor in computer science. So I remember when I was at a very young age, I already see like datas, all of these computer science concepts. So grew up to be a data scientist is always something like in my mind. >> You aspired to be. >> Yes. >> I love that. >> So I consider myself in a lucky place in that way. But also, like during this journey to become a data scientist you need to navigate yourself too, right? Like you have this roots, like this foundation but then you still need to kind of like figure out yourself what is it? Is it really the career that you want to pursue? But I'm happy that I'm end up here today and where I am right now. >> Oh, we're happy to have you. >> Yeah. So you' re with Gilead now after you're completing your PhD. And were you always interested in the intersection of data science and health, or is that something you explored throughout your studies? >> Oh, that's an excellent question. So I did have background in computer science but I only really get into biomedical domain when I did my PhD at school. So my research during my PhD was natural language processing, NLP and machine learning and their applications in biomedical domains. And then when I graduated, I got my first job in Gilead Science. Is super, super close and super relevant to what my research at school. And at Gilead, I am working in the advanced analytics department, and our focus is to bring artificial intelligence and machine learning into supporting clinical decision making. And really the ultimate goal is how to use AI to accelerate the precision medicine. So yes, it's something very like... I'm very lucky to get the first job that which is very close to my research at school. >> That's outstanding. You know, when we talk about AI, we can't not talk about ethics, bias. >> Kelly: Right. >> We know there's (crosstalk) Yes. >> Kelly: In healthcare. >> Exactly. Exactly. Equities in healthcare, equities in so many things. Talk a little bit about what excites you about AI, what you're doing at Gilead to really influence... I mean this, we're talking about something that's influencing life and death situations. >> Kelly: Right. >> How are you using AI in a way that is really maximizing the opportunities that AI can bring and maximizing the value in the data, but helping to dial down some of the challenges that come with AI? >> Yep. So as you may know already with the digitalization of medical records, this is nowaday, we have a tremendous opportunities to fulfill the dream of precision medicine. And what I mean by precision medicines, means now the treatments for people can be really tailored to individual patients depending on their own like characteristic or demographic or whatever. And nature language processing and machine learning, and AI in general really play a key role in that innovation, right? Because like there's a vast amount of information of patients and patient journeys or patient treatment is conducted and recorded in text. So that's why our group was established. Actually our department, advanced analytic department in Gilead is pretty new. We established our department last year. >> Oh wow. >> But really our mission is to bring AI into this field because we see the opportunity now. We have a vast amount of data about patient about their treatments, how we can mine these data how we can understand and tailor the treatment to individuals. And give everyone better care. >> I love that you brought up precision medicine. You know, I always think, if I kind of abstract everything, technology, data, connectivity, we have this expectation in our consumer lives. We can get anything we want. Not only can we get anything we want but we expect whoever we're engaging with, whether it's Amazon or Uber or Netflix to know enough about me to get me that precise next step. I don't think about precision medicine but you bring up such a great point. We expect these tailored experiences in our personal lives. Why not expect that in medicine as well? And have a tailored treatment plan based on whatever you have, based on data, your genetics, and being able to use NLP, machine learning and AI to drive that is really exciting. >> Yeah. You recap it very well, but then you also bring up a good point about the challenges to bring AI into this field right? Definitely this is an emerging field, but also very challenging because we talk about human health. We are doing the work that have direct impact to human health. So everything need to be... Whatever model, machine learning model that you are building, developing you need to be precise. It need to be evaluated properly before like using as a product, apply into the real practice. So it's not like recommendation systems for shopping or anything like that. We're talking about our actual health. So yes, it's challenging that way. >> Yeah. With that, you already answered one of the next questions I had because like medical data and health data is very sensitive. And how you at Gilead, you know, try to protect this data to protect like the human beings, you know, who are the data in the end. >> The security aspect is critical. You bring up a great point about sensitive data. We think of healthcare as sensitive data. Or PII if you're doing a bank transaction. We have to be so careful with that. Where is security, data security, in your everyday work practices within data science? Is it... I imagine it's a fundamental piece. >> Yes, for sure. We at Gilead, for sure, in data science organization we have like intensive trainings for employees about data privacy and security, how you use the data. But then also at the same time, when we work directly with dataset, it's not that we have like direct information about patient at like very granular level. Everything is need to be kind of like anonymized at some points to protect patient privacy. So we do have rules, policies to follow to put that in place in our organization. >> Very much needed. So some of the conversations we heard, were you able to hear the keynote this morning? >> Yes. I did. I attended. Like I listened to all of them. >> Isn't it fantastic? >> Yes, yes. Especially hearing these women from different backgrounds, at different level of their professional life, sharing their journeys. It's really inspiring. >> And Hannah, and I've been talking about, a lot of those journeys look like this. >> I know >> You just kind of go... It's very... Yours is linear, but you're kind of the exception. >> Yeah, this is why I consider my case as I was lucky to grow up in STEM environment. But then again, back to my point at the beginning, sometimes you need to navigate yourself too. Like I did mention about, I did my pa... Sorry, my bachelor degree in Vietnam, in STEM and in computer science. And that time, there's only five girls in a class of 100 students. So I was not the smartest person in the room. And I kept my minority in that areas, right? So at some point I asked myself like, "Huh, I don't know. Is this really my careers." It seems that others, like male people or students, they did better than me. But then you kind of like, I always have this passion of datas. So you just like navigate yourself, keep pushing yourself over those journey. And like being where I am right now. >> And look what you've accomplished. >> Thank you. >> Yeah. That's very inspiring. And yeah, you mentioned how you were in the classroom and you were only one of the few women in the room. And what inspired or motivated you to keep going, even though sometimes you were at these points where you're like, "Okay, is this the right thing?" "Is this the right thing for me?" What motivated you to keep going? >> Well, I think personally for me, as a data scientist or for woman working in data science in general, I always try to find a good story from data. Like it's not, when you have a data set, well it's important for you to come up with methodologies, what are you going to do with the dataset? But I think it's even more important to kind of like getting the context of the dataset. Like think about it like what is the story behind this dataset? What is the thing that you can get out of it and what is the meaning behind? How can we use it to help use it in a useful way. To have in some certain use case. So I always have that like curiosity and encouragement in myself. Like every time someone handed me a data set, I always think about that. So it's helped me to like build up this kind of like passion for me. And then yeah. And then become a data scientist. >> So you had that internal drive. I think it's in your DNA as well. When you were one of five. You were 5% women in your computer science undergrad in Vietnam. Yet as Hannah was asking you, you found a lot of motivation from within. You embrace that, which is so key. When we look at some of the statistics, speaking of data, of women in technical roles. We've seen it hover around 25% the last few years, probably five to 10. I was reading some data from anitab.org over the weekend, and it shows that it's now, in 2022, the number of women in technical roles rose slightly, but it rose, 27.6%. So we're seeing the needle move slowly. But one of the challenges that still remains is attrition. Women who are leaving the role. You've got your PhD. You have a 10 month old, you've got more than one child. What would you advise to women who might be at that crossroads of not knowing should I continue my career in climbing the ladder, or do I just go be with my family or do something else? What's your advice to them in terms of staying the path? >> I think it's really down to that you need to follow your passion. Like in any kind of job, not only like in data science right? If you want to be a baker, or you want to be a chef, or you want to be a software engineer. It's really like you need to ask yourself is it something that you're really passionate about? Because if you really passionate about something, regardless how difficult it is, like regardless like you have so many kids to take care of, you have the whole family to take care of. You have this and that. You still can find your time to spend on it. So it's really like let yourself drive your own passion. Drive the way where you leading to. I guess that's my advice. >> Kind of like following your own North Star, right? Is what you're suggesting. >> Yeah. >> What role have mentors played in your career path, to where you are now? Have you had mentors on the way or people who inspired you? >> Well, I did. I certainly met quite a lot of women who inspired me during my journey. But right now, at this moment, one person, particular person that I just popped into my mind is my current manager. She's also data scientist. She's originally from Caribbean and then came to the US, did her PhDs too, and now led a group, all women. So believe it or not, I am in a group of all women working in data science. So she's really like someone inspire me a lot, like someone I look up to in this career. >> I love that. You went from being one of five females in a class of 100, to now having a PhD in information sciences, and being on an all female data science team. That's pretty cool. >> It's great. Yeah, it's great. And then you see how fascinating that, how things shift right? And now today we are here in a conference that all are women in data science. >> Yeah. >> It's extraordinary. >> So this year we're fortunate to have WIDS coincide this year with the actual International Women's Day, March 8th which is so exciting. Which is always around this time of year, but it's great to have it on the day. The theme of this International Women's Day this year is embrace equity. When you think of that theme, and your career path, and what you're doing now, and who inspires you, how can companies like Gilead benefit from embracing equity? What are your thoughts on that as a theme? >> So I feel like I'm very lucky to get my first job at Gilead. Not only because the work that we are doing here very close to my research at school, but also because of the working environment at Gilead. Inclusion actually is one of the five core values of Gilead. >> Nice. >> So by that, we means we try to create and creating a working environment that all of the differences are valued. Like regardless your background, your gender. So at Gilead, we have women at Gilead which is a global network of female employees, that help us to strengthen our inclusion culture, and also to influence our voices into the company cultural company policy and practice. So yeah, I'm very lucky to work in the environment nowadays. >> It's impressive to not only hear that you're on an all female data science team, but what Gilead is doing and the actions they're taking. It's one thing, we've talked about this Hannah, for companies, and regardless of industry, to say we're going to have 50% women in our workforce by 2030, 2035, 2040. It's a whole other ballgame for companies like Gilead to actually be putting pen to paper. To actually be creating a strategy that they're executing on. That's awesome. And it must feel good to be a part of a company who's really adapting its culture to be more inclusive, because there's so much value that comes from inclusivity, thought diversity, that ultimately will help Gilead produce better products and services. >> Yeah. Yes. Yeah. Actually this here is the first year Gilead is a sponsor of the WIDS Conference. And we are so excited to establish this relationship, and looking forward to like having more collaboration with WIDS in the future. >> Excellent. Kelly we've had such a pleasure having you on the program. Thank you for sharing your linear path. You are definitely a unicorn. We appreciate your insights and your advice to those who might be navigating similar situations. Thank you for being on theCUBE today. >> Thank you so much for having me. >> Oh, it was our pleasure. For our guests, and Hannah Freytag this is Lisa Martin from theCUBE. Coming to you from WIDS 2023, the eighth annual conference. Stick around. Our final guest joins us in just a minute.

(upbeat music) >> Welcome back friends. It's theCUBE LIVE in Las Vegas at the Venetian Expo, covering the first full day of AWS re:Invent 2022. I'm Lisa Martin, and I have the privilege of working much of this week with Dave Vellante. >> Hey. Yeah, it's good to be with you Lisa. >> It's always good to be with you. Dave, this show is, I can't say enough about the energy. It just keeps multiplying as I've been out on the show floor for a few minutes here and there. We've been having great conversations about cloud migration, digital transformation, business transformation. You name it, we're talking about it. >> Yeah, and I got to say the soccer Christians are really happy. (Lisa laughing) >> Right? Because the USA made it through. So that's a lot of additional excitement. >> That's true. >> People were crowded around the TVs at lunchtime. >> They were, they were. >> So yeah, but back to data. >> Back to data. We have a couple of guests here. We're going to be talking a lot with customer challenges, how they're helping to overcome them. Please welcome Kevin Zawodzinski, VP of Sales Engineering at COMMVAULT. >> Thank you. >> And Paul Meighan, Director of Product Management at AWS. Guys, it's great to have you on the program. Thank you for joining us. >> Thanks for having us. >> Thanks for having us. >> Isn't it great to be back in person? >> Paul: It really is. >> Kevin: Hell, yeah. >> You cannot replicate this on virtual, you just can't. It's nice to see how excited people are to be back. There's been a ton of buzz on our program today about Adam's keynote this morning. Amazing. A lot of synergies with the direction, Paul, that AWS is going in and where we're seeing its ecosystem as well. Paul, first question for you. Talk about, you know, in the customer environment, we know AWS is very customer obsessed. Some of the main challenges customers are facing today is they really continue this business transformation, this digital transformation, and they move to cloud native apps. What are some of those challenges and how do you help them eradicate those? >> Well, I can tell you that the biggest contribution that we make is really by focusing on the fundamentals when it comes to running storage at scale, right? So Amazon S3 is unique, distributed architecture, you know, it really does deliver on those fundamentals of durability, availability, performance, security and it does it at virtually unlimited scale, right? I mean, you guys have talked to a lot of storage folks in the industry and anyone who's run an estate at scale knows that doing that and executing on those fundamentals day after day is just super hard, right? And so we come to work every day, we focus on the fundamentals, and that focus allows customers to spend their time thinking about innovation instead of on how to keep their data durably stored. >> Well, and you guys both came out of the storage world. >> Right. >> Yeah, yeah. >> It was a box world, (Kevin laughs) and it ain't no more. >> Kevin: That's right, absolutely. >> It's a service and a service of scale. >> Kevin: Yeah. So architecture matters, right? >> Yeah. >> Yeah. >> Paul, talk a little bit about, speaking of innovation, talk about the evolution of S3. It's been around for a while now. Everyone knows it, loves it, but how has AWS architected it to really help meet customers where they are? >> Paul: Right. >> Because we know, again, there's that customer first focus. You write the press release down the road, you then follow that. How is it evolving? >> Well, I can tell you that architecture matters a lot and the architecture of Amazon S3 is pretty unique, right? I think, you know, the most important thing to understand about the architecture of S3 is that it is truly a regional service. So we're laid out across a minimum of 3 Availability Zones, or AZs, which are physically separated and isolated and have a distance of miles between them to protect against local events like floods and fires and power interruption, stuff like that. And so when you give us an object, we distribute that data across that minimum of 3 Availability Zones and then within multiple devices within each AZ, right? And so what that means is that when you store data with us, your data is on storage that's able to tolerate the failure of multiple devices with no impact to the integrity of your data, which is super powerful. And then again, super hard to do when you're trying to roll your own. So that's sort of a, like an overview of the architecture. In terms of how we think about our roadmap, you know, 90% of our roadmap comes directly from what customers tell us matters, and that's a tenant of how we think about customer obsession at AWS and it really is how we drive a roadmap. >> Right, so speaking of customers Kevin, what are customers asking you guys- >> Yeah. >> for, how does it relate to what you're doing with S3? >> Yeah, it's a wonderful question and one that is actually really appropriate for us being at re:Invent, right? So we got, last three years we've had customers here with us on stage talking about it. First of all, 3 years ago we did a virtual session, unfortunately, but glad to be back as you mentioned, with Coca-Cola and theirs was about scale and scope and really about how can we protect hundreds of thousands of objects, petabyte to data, in a simple and secure way, right. Then last year we actually met with a ACT, Inc. as well and co-presented with them and really talked about how we could protect modern workloads and their modern workloads around whether it was Aurora or as well as EKS and how they continue to evolve as well. And, last but not least it's going to be, this year we're talking with Illinois State University as well about how they're going to continue to grow, adapt and really leverage AWS and ourselves to further their support of their teachers and their staff. So that is really helping us quite a bit to continue to move forward. And the things we're doing, again, with our customer base it's really around, focused on what's important to them, right? Customer obsession, how are we working with that? How are we making sure that we're listening to them? Again, working with AWS to understand how can we evolve together and really ultimately their journeys. As you heard, even with those 3 examples they're all very different, right? And that's the point, is that everybody's at a different point in the journey. They're at a different place from a modernization perspective. So we're helping them evolve, as they're helping us evolve as well, and transform with AWS. >> So very mature COMMVAULT stack, the S3 bucket and all the other capabilities. Paul, you just talked about coming together- >> Right. >> Dave: for your customers. >> Yeah, yeah, absolutely. And just, you know, we were talking the other day, Paul and I were talking the other day, it's been, you know, we've worked with AWS, with integration since 2009, right? So a long time, right? I mean, for some that may not seem like a long time ago, but it is, right? It's, you know, over a decade of time and we've really advanced that integration considerably as well. >> What are some of the things that, I don't know if you had a chance to see the keynote this morning? >> Yeah, a little bit. >> What are some of the things that there was, and in fact this is funny, funny data point for you on data. One of my previous guests told me that Adam Selipsky spent exactly 52 minutes talking about data this morning. 52 minutes. >> Okay. >> That there's a data point. But talk about some of the things that he talked about, the direction AWS is going in, obviously new era in the last year. Talk about what you heard and how you think that will evolve the COMMVAULT-AWS relationship. >> Yeah, I think part of that is about flexibility, as Paul mentioned too, architecture matters, right? So as we evolve and some of the things that we pride ourselves on is that we developed our systems and our software and everything else to not worry about what do I have to build to today but how do I continue to evolve with my customer base? And that's what AWS does, right? And continues to do. So that's really how we would see the data environment. It's really about that integration. As they grow, as they add more features we're going to add more features as well. And we're right there with them, right? So there's a lot of things that we also talk about, Paul and I talk about, around, you know, how do we, like Graviton3 was brought up today around some of the innovations around that. We're supporting that with Auto Scale right now, right? So we're right there releasing, right when AWS releasing, co-developing things when necessary as well. >> So let's talk about security a little bit. First of all, what is COMMVAULT, right? You're not a security company but you're an adjacency to security. It's sort of, we're rethinking security. >> Kevin: Yep. >> including data protection, not a bolt-on anymore. You guys both have a background in that world and I'm sure that resonates. >> Yeah. >> So what is the security play here? What role does COMMVAULT play? I think we know pretty well what role AWS plays, but love to hear, Paul, your thoughts as well on security. >> Yeah, I'll start I guess. >> Go on Paul. >> Okay. Yeah, so on the security side of things, there's a quite a few things. So again, on the development side of things, we do things like file anomaly detection, so seeing patterns in data. We talked a lot about analytics as well in the keynote this morning. We look at what is happening in the customer environment, if there's something odd or out of place that's happening, we can detect that and we'll notify people. And we've seen that, we have case studies about that. Other things we do are simple, simple but elegant. Is with our security dashboard. So we'll use our security dashboard to show best practices. Are they using Multi-Factor Authentication? Are you viewing password complexity? You know, things like that. And allows people to understand from a security landscape perspective, how do we layer in protection with their other systems around security. We don't profess to be the security company, or a security company, but we help, you know, obviously add in those additional layers. >> And obviously you're securing, you know, the S3 piece of it. >> Mmmhmm. >> You know, from your standpoint because building it in. >> That's right. And we can tell you that for us, security is job zero. And anyone at AWS will tell you that, and not only that but it will always be our top priority. Right from the infrastructure on down. We're very focused on our shared responsibility model where we handle security from the hypervisor, or host operating system level, down to the physical security of the facilities in which our services run and then it's our customer's responsibility to build secure applications, right. >> Yeah. And you talk about Graviton earlier, Nitro comes into play and how you're, sort of, fencing off, you know, the various components of the system from the operating system, the VMs, and then that is designed in and that's a new evolution that it comes as part of the package. >> Yeah, absolutely. >> Absolutely. >> Paul, talk a little bit about, you know, security, talking about that we had so many conversations this year alone about the threat landscape and how it's dramatically changing, it's top of mind for everybody. Huge rise in ransomware attacks. Ransomware is now, when are we going to get hit? How often? What's the damage going to be? Rather than, are we going to get hit? It's, unfortunately it's progressed in that direction. How does ensuring data security impact how you're planning the roadmap at AWS and how are partners involved in shaping that? >> Right, so like I said, you know, 90% of our roadmap comes from what customers tell us matters, right? And clearly this is an issue that matters very much to customers right now, right? And so, you know, we're certainly hearing that from customers, and COMMVAULT, and partners like COMMVAULT have a big role to play in helping customers to secure and protect their applications, right? And that's why it's so critical that we come together here at re:Invent and we have a bunch of time here at the show with the COMMVAULT technical folks to talk through what they're hearing from customers and what we're hearing. And we have a number of regular touch points throughout the year as well, right? And so what COMMVAULT gets from the relationship is, sort of, early access and feedback into our features and roadmap. And what we get out of it really is that feedback from that large number of customers who interface with Amazon S3 through COMMVAULT. Who are using S3 as a backup target behind COMMVAULT, right? And so, you know, that partnership really allows us to get close to those customers and understand what really matters to them. >> Are you doing joint engineering, or is it more just, hey here you go COMMVAULT, here's the tools available, go, go build. Can you address that? >> Yeah, no, absolutely. There's definitely joint engineering like even things around, you know, data migration and movement of data, we integrate really well and we talk a lot about, hey, what are you, like as Paul mentioned, what are you seeing out there? We actually, I just left a conversation about an hour ago where we're talking about, you know, where are we seeing placement of data and how does that matter to, do you put it on, you know, instant access, or do you put it on Glacier, you know, what should be the best practices? And we tell them, again, some of the telemetry data that we have around what do we see customers doing, what's the patterns of data? And then we feed that back in and we use that to create joint solutions as well. >> You know, I wonder if we could talk about cloud, you know, optimization of cloud costs for a minute. That's obviously a big discussion point in the hallways with customers. And on your earnings call you guys talked about specifically some customers and they specifically mentioned, for example, pushing storage to lower cost tiers. So you brought up Glacier just then. What are you seeing in the field in that regard? How are customers taking advantage of that? And where does COMMVAULT play in, sort of, helping make that decision? >> You want to take part one or you want me to take it? >> I can take part one. I can tell you that, you know, we're very focused on helping customers optimize costs, however necessary, right? And, you know, we introduced intelligent hearing here at the show in 2019 and since launch it's helped customers to reduce costs by over $750 million, right? So that's a real commitment to optimizing costs on behalf of customers. We also launched, you know, later in 2020, Glacier Deep Archive, which is the lowest cost storage in the cloud. So it's an important piece of the puzzle, is to provide those storage options that can allow customers to match the workloads that are, that need to be on folder storage to the appropriate store. >> Yeah, and so, you know, S3 is not this, you know, backup and recovery system, not an archiving system and, you know, in terms of, but you have that intelligence in your platform. 'Cause when I heard that from the earnings call I was like, okay, how do customers then go about deciding what they can, you know, when it's all good times, like yeah, who cares? You know, just go, go, go. But when you got to tighten the belt, how do you guys? >> Yeah, and that goes back to understanding the data pattern. So some of that is we have intelligence and artificial intelligence and everything else and machine learning within our, so we can detect those patterns, right? We understand the patterns, we learn from that and we help customers right size, right. So ultimately we do see a blend, right? As Paul mentioned, we see, you know, hey I'm not going to put everything on Glacier necessarily upfront. Maybe they are, it all depends on their workloads and patterns. So we use the data that we collect from the different customers that we have to share those best practices out and create, you know, the right templates, so to speak, in ways for people to apply it. >> Guys, great joint, you talked about the joint engineering, joint go to market, obviously a very strong synergistic partnership between the two. A lot of excitement. This is only day one, I can only imagine what's going to be coming the next couple of days. But I have one final question for you, but I have same question for both of you. You had the chance to create your own bumper sticker, so you get a shiny new car and for some reason you want to put a bumper sticker on it. About COMMVAULT, what would it say? >> Yeah, so for me I would say comprehensive, yet simple, right? So ultimately about giving you all the bells and whistles but if you want to be very simple we can help you in every shape and form. >> Paul, what's your bumper sticker say about AWS? >> I would say that AWS starts with the customer and works backwards from there. >> Great one. >> Excellent. Guys- >> Kevin: Well done. >> it's been a pleasure to have you on the program. Thank you- >> Kevin: Thank you. >> for sharing what's going on, the updates on the AWS-COMMVAULT partnership and what's in it for customers. We appreciate it. >> Dave: Thanks you guys. >> Thanks a lot. >> Thank you. >> All right. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>>Mhm >>Welcome to the cube where we're celebrating the EC 2/15 birthday anniversary. My name is Dave Volonte and we're joined right now by Sandy carter, Vice President of AWS. Welcome Sandy, it's great to see you again, >>David. So great to see you too. Thanks for having me on the show today. >>Very welcome. We were last physically together. I think it was reinvent 2019. Hopefully I'll see you before 2022. But first happy birthday to EC two. I mean, it's hard to imagine back in 2006, the degree to which EC two would impact our industry. Sandy, >>I totally agree. You know, I joined a W S about 4.5 years ago in EC two and it's, it's even amazing to see what's just happened in the last 4.5 years. So I'm with you. Nobody really expected the momentum, but EC two has really shone brightly in value to our customers. >>You know, we've done the public sector summit, you know, many times. It's a great event. Things are a little different in public sector as you well know. So talk about the public sector momentum with EC two and that journey. What have you seen? >>Yeah, so it's a great question day. So I had to go back in the time vault. You know, public sector was founded in 2010 and we were actually founded by the amazon process writing a paper setting up a two pizza team, which happened to be six people. And that journey really started with a lot of our public sector customers thinking that we don't know about the cloud. So we might want to do a pilot or just look at non mission critical workloads now public sector and I know you know this day but public sector is more than just government, it has education, not for profit healthcare and now space. But everybody at that time was very skeptical. So we had to really work hard to migrate some workloads over. And one of our very first non mission critical workloads was the U. S. Navy. Um and what they did was the Navy Media Services actually moved images over to EC two. Now today that seems like oh that's pretty easy. But back then that was a big monumental reference. Um and we had to spend a lot of time on training and education to win the hearts and souls of our customers. So back then we had half of the floor and Herndon Washington, we just had a few people and that room really became a training room. We trained our reps, we trained our customers um research drive. A lot of our early adopters accounts like Nasa and jpl. And um then when cloud first came out and governments that started with the U. S. A. And we announced Govcloud, you know, things really picked up, we had migration of significant workloads. So if you think back to that S. A. P. And just moving media over um with the Navy, the Navy and S. A. P. Migrated their largest S A P E R P solution to the cloud in that time as well. Um, then we started international. Our journey continued with the UK International was UK and us was us. Then we added a P. J. And latin America and Canada. And then of course the partner team which you know, is very close to my heart. Partners today are about 73% of our overall public sector business. And it started out with some interesting small pro program SVS being very crucial to that, accelerating adoption. And then of course now the journey has continued with Covid. That has really accelerated that movement to the cloud. And we're seeing, you know, use of ec two to really help us drive by the cute power needed for A I N. M. L. And taking all that data in from IOT and computing that data. And are they are. Um, and we're really seeing that journey just continue and we see no end in sight. >>So if we can stay in the infancy and sort of the adolescent years of public sector, I mean, remember, I mean as analysts, we were really excited about, you know, the the the introduction of of of of EC two. But but there was a lot of skepticism in whatever industry, financial services, healthcare concerns about security, I presume it was similar in public sector, but I'm interested in how you you dealt with those challenges, how you you listen to folks, you know, how did you drive that leadership to where it is today? >>Yeah, you're right. The the first questions were what is the cloud? Doesn't amazon sell books? What is this clown thing? Um, what is easy to, what is easy to stand for and then what the heck is an instance? You know, way back when there was one instance, it didn't even have a name. And today of course we have over 400 instant types with different names for each one. Um and the big challenges you asked about challenges, the big challenges that we had to face. Dave were first and foremost, how do we educate? Um we had to educate our employees and then we had to educate our customers. So we created these really innovative hands on training programmes, white boarding um, sessions that we needed. They were wildly popular. So we really have to do that and then also prove security as you know. So you asked how we listen to our customers and of course we followed the amazon way we work backwards from where we were. So at that time, customers needed education. And so we started there um, data was really important. We needed to make customer or data for government more available as well. So for instance, we first started hosting the Census Bureau for instance. Um and that was all on EC two. So we had lots of early adopters and I think the early adopters around EC two really helped us to remember. I said that the UK was our international office for a while. So we had NIH we had a genomes project and the UK Ministry of Justice as well. And we had to prove security out. We had to prove how this drove a structured GovCloud and then we had to also prove it out with our partners with things like helping them get fed ramped or other certifications. I'll for that sort of thing as well. And so we really lead in those early days through that education and training. Um we lead with pilots to show the potential of the possible and we lead with that security setting those security standards and those compliance certifications, always listening to the customer, always listening to the partner, knowing how important the partners we're going to be. So for example, recovery dot gov was the first government wide system that moved to the cloud. Um the recovery transparency board was first overseeing that Recovery act spending, which included stimulus tracking website. I don't know if you remember that, but they hosted the recovery dot gov On amazon.com using EC two. And that site quickly made information available to a million visitors per hour and at that time, that was amazing. And the cost savings were significant. We also launched Govcloud. You'd asked about GovCloud earlier and that federal cloud computing strategy when the U. S. Government came out with cloud first and they had to consider what is really going to compel these federal agencies to consider cloud. They had Public-sector customers had 70 requirements for security and safety of the data that we came out with Govcloud to open up all those great opportunities. And I think Dave we continue to leave because we are customer obsessed uh you know, still supporting more security standards and compliance sort than any other provider. Um You know, now we lead with data not just data for census or images for the US Navy, but we've got now data in space and ground station and data at scale with customers like Finra who's now doing 100 billion financial transactions. Not just that one million from the early days. So it has been a heck of a ride for public sector and I love the way that the public sector team really used and leveraged the leadership principles. Re invent and simplify dive deep. Be obsessed with the customers start where they are. Um and make sure that you're always always always listening to what they need. >>You know, it's interesting just observing public sector. It's not uncommon, especially because of the certifications that some of the services, you know come out after they come out for the commercial sector. And I remember years ago when I was at I. D. C. I was kind of the steward of the public sector business. And that was a time when everybody was trying to focus in public sector on commercial off the shelf software. That was the big thing. And they want to understand, they wanted to look at commercial use cases and how they could apply them to government. And when I dug in a little bit and met with generals and like eight different agencies, I was struck by how many really smart people and the things that they were doing. And I said at the time, you know, a lot of my commercial clients could learn a lot from you. And so the reason I bring that up is because I saw the same thing with Govcloud because there was a lot of skepticism in various industries, particularly regulated industries, financial services, healthcare. And then when Govcloud hit and the CIA deal hit, people said, whoa CIA, they're like the most security conscious industry or organization in the world. And so I feel as though in a way public sector led that that breakthrough. So I'm wondering when you think about EC two today and the momentum that it has in the government, Are there similar things that you see? Where's the momentum today in public sector? >>You are right on target day? I mean that CIA was a monumental moment and that momentum with ever increasing adoption to the cloud has continued in public sector. In fact today, public sector is one of our fastest growing areas. So we've got um, you know, thousands of startups or multiple countries that were helping out today to really ignite that innovation. We have over 4000 government agencies, 9000 education agencies. Um 2000 public sector partners from all over the globe. 24,000 not for profit organizations. And what I see is the way that they're using EC two um is is leading the pack now, especially after Covid, you know, many of these folks accelerated their journey because of Covid. They got to the cloud faster and now they are doing some really things that no one else is doing like sending an outpost postbox into space or leveraging, you know robots and health care for sure. So that momentum continues today and I love that you were the champion of that you know way back when even when you were with I. D. C. >>So I want to ask you, you sort of touched on some interesting use cases, what are some of the more unusual ones and maybe breakthrough use cases that you see? >>Oh so yeah we have a couple. So one is um I mentioned it earlier but there is a robot now that is powered by IOT and EC two and the robot helps to take temperature and and readings for folks that are entering the hospital in latin America really helped during Covid, one of my favorites. It actually blew the socks off of verne or two and you know that's hard to do is a space startup called lunar outpost and they are synthesizing oxygen on mars now that's, that's driven by Ec two. That's crazy. Right? Um, we see state governments like new york, they've got this vision zero traffic and they're leveraging that to prevent accidents all through new york city. I used to live in new york city. So this is really needed. Um, and it continues like with education, we see university of Illinois and Splunk one of our partners, they created a boarding pass for students to get back to school. So I have a daughter in college. Um, and you know, it's really hard for her to prove that she's had the vaccine or that she's tested negative on the covid test. They came out with a past of this little boarding pass, just like you used to get on an airplane to get into different classes and labs and then a couple of my favorites and you guys actually filmed the Cherokee nation. So the Cherokee nation, the chief of the Cherokee nation was on our silicon um show and silicon angles show and the cube featured them And as the chief talked about how he preserves the Cherokee language. And if you remember the Cherokee language has been used to help out the US in many different ways and Presidio. One of our partners helped to create a game, a super cool game that links in with unity To help teach that next generation the language while they're playing a game and then last but not least axle three d out of the UK. Um, they're using easy to, to save lives. They've created a three D imaging process for people getting ready to get kidney transplants and they have just enhanced that taken the time frame down for months. Now today's that they can actually articulate whether the kidney transplant will work. And when I talked to roger their Ceo, they're doing R. O. L return on life's not return on investment. So those are just some of the unusual and breakthrough use cases that we see powered by E. C. To >>Sandy. I'll give you the last word. Your final closing comments. >>Well, my final closing comments are happy birthday to ec two celebrating 15 years. What a game changer and value added. It has been the early days of Ec two. Of course we're about education like what is the cloud? Why is a bookseller doing it. But um, easy to really help to create a new hub of value Now. We've got customers moving so fast with modernization using a I. M and M. L. Containers survivalists. Um, and all of these things are really changing the game and leveling it up as we increased that business connection. So I think the future is really bright. We've only just begun. We've only just begun with EC two and we've only just begun with public sector. You know, our next great moments are still left to come. >>Well, Sandy, thanks so much. Always Great to see you. Really appreciate your time. >>Thank you so much. Dave. I really appreciate it. And happy birthday again to E. C. To keep >>It right there were celebrating Ec 2's 15th birthday right back. >>Mhm.

>> So nice to meet you. And I'm Tetsuhiko Teshima from German branch of MEI Laboratories. I'm working at the Technische Universitat Munchen to conduct wet experiment like using chemical and biological samples. So it's great honor and pleasure for me to have a chance to share with you some topics about miniaturized biointerfaces that I have been working on over the last six or seven years, I guess. So before starting, please let me introduce myself and my background. So I started to work in this company since this March, but until the last year, I was working in NTT Basic Research Laboratories that is located in Kanagawa, Japan. And I have work on basic nanoscience research. But when going back to the further, I was originally a student studying biology especially infectious microbiology. And then I learned about the miniaturize fluidic system to manipulate single cells and MEMS technologies that is kind of a fabrication process for semiconductor devices. So, this background motivate me to start interdisciplinary work, especially about biomedical engineering at NTT Corporation. So in recent year, wearable electrodes have been developed to continuously monitor the vital data, including the heart rate, ECG, or EMG waveforms for rapid diagnosis and early stage treatment of disease. So conventionally, rigid metals or metal-plated fibers have been widely used as the electrodes but they lack flexibility and biocompatibilities, which results in the noise in obtaining data and the patient allergic reaction during the long time years. So at NTT, we are working on the research and development of the conductive composite materials. So, due to its high flexibility and hydrophilicity and biocompatibilities, so this electrodes can successfully record ECG without any rashes and itches to the skin. So now these wearable electrodes cores toy are commercially available and funds are applied for not only the medical care and rehabilitation for the patients, but also for example, remote monitoring system of the workers, integration with these sportswear and entertainment show. But this product is originated from the basic scientific findings especially on the conductive polymers, PEDOT:PSS and silk fibers. So there was some mainly conducted by two key scientists clinician doctor Tsukada, and chemist doctor, Nakashima. In order to realize this product, they try so many prototypes. And make so many effort to obtain the pharmaceutical probables for medical usage. So through this experience, we are going back to the original material science and research and making non-toxic interfaces with cells and tissues in order to seek new kind of development. So, as a next challenge, I have focused on the electrodes that work inside the bodies. So we have the tissues and organs with electrical signals like heart and brain. So if implanted electrodes can work on these tissues, this help us to increase the variety of the vital data like EEG. And also it can directly treat the targeted tissues as a surgical, too, like CRT pacing. So in this case, these biointerfaces should be populated in very humid environment and in non-toxic manner. They also should be transformed into soft, three dimensional structures, in order to fit the shape of cells and tissues because they have very complicated 3D structures. So I decided to develop the basic electrode component that meets all of these requirements that is biocompatible for example, like 3D film-electrodes. So what I tried at first is to create a non-toxic, very soft and flexible film-electrodes using the materials that are using the heatable electrodes that is silk bundles and PEDOT:PSS. So, firstly, I dissolve the silk bundle to extract a specific protein and process into a palette shape using MEMS technologies, one of my main skill. So by adding the conductive polymers, >> PEDOT: PSS little by little, the palettes will gradually become blue but maintain the high optical transparency. Through this experiment, I discover a very unique materials scientific aspect of silk fibroin. So when PEDOT:PSS got added, the molecular structure and the confirmation of silk protein dramatically change from alpha helix to the beta sheet, and I focused this structure change, leads to the increase in conductivity compared with the PEDOT:PSS pristine films. By using the lithographic fabrication process, the films can be process into very tiny shape, with same deviation as single cell Lego. So this electrode is made of the silk fibroin, the, are very cell friendly protein. So the suspender cells prefer to adhere to their surface. So after attaching the cells on a surface, I can manipulate the cells while maintain the adhesive properties and electrically simulate the cells for the cool, very weak electrical signals from the cells. So in this step we created a non-toxic, transparent, and very flexible films and film-based electrodes. But please note that the, they are 2D and they're not 3D. So in the next step, I try to investigated how to transform these same 2D film to 3D shape. So here, among two polymers I used, so I replace the PEDOT:PSS with different type of polymers, there is parylene, like this. So when the parylene is adhering to the silk fibroin layers so, the gradient of the mechanical stiffness is formed in the synchronous directions as shown here. And this gradient causes the driving force of same film folding, like this. So this is a, this is a movie of the self-folding bilayer films. And you can see these rectangular patterns spontaneously transform into the cylindrical shapes. So just before folding, I suspended the cells on top of the films that is derived from the heart muscles. So the folding films, so here can gently rub the cells inside the tubes and you can incubate them safely more than for two weeks in order to reconstitute the self-beating, fiber-shaped muscle tissues, as shown here. So also this reconstituted tissues can be manipulated like building blocks by picking up and dissolving using glass capillaries. So I believe this techniques has a potential to facilitate high-order self-assembly like artificial neural networks or tissue engineering. So I realized to transform the two different film to 3D shape. So I use this method to transform into 3D electrodes. So in the final step, instead of the silk fibroin, I focus on using extremely thin electrodes materials that is called graphene. So as I explained as extremely thin, so it consist of the only single layer of carbon atom. So since they has just a single atom thickness, it has very high optical transparency and flexibility. So when the graphene was transform to the parylene surface I found this bilayer was tightly bonded due to the strong molecular interactions and the graphene itself straight on the parylene surface and this cell film becomes three dimensional electrodes, like tubeless structures. So as you can see in this movie, like this. So just after releasing them from the service lead, I instantly undergoes a phase transition and collapse. So since, this hexagonal molecular structure of graphene is distorted due to the folding process, so electrical characteristics dramatically change from firstly metallic to the semiconductor like non-linear shape, shown here. Or interestingly, the curvature and direction of the cell folding can be well controls with number of graphene, this and it's crystalline directions. So when a merged layers graphene were transfer, the curvature radius become smaller and smaller. And when the crystal, crystal, sorry, single crystalline graphene was loaded on the surface of parylene, this bilayer was folded in one fixed same direction, especially along the arms here siding. So by simply transferring the single carbon atom layer to the parylene surface, so we achieved the self-assembly of 3D transparent electrodes. In order to demonstrate biocompatibility of this graphene electrodes, we apply for the interface with neurons. So as there was a self-folding of silk fibroin, so we suspended the neurons are encapsulated in the self-folded graphene tubes, like this. So I made it a very tiny holes on the films. So the encapsulated neurons can uptake the nutrition and oxygen through this pore. So I culture the neurons for, without any damage, to the cells, and they exhibit cell-cell contact for tissue-like structures and they elongate their nuclei and axon to the outside through this pore. Therefore, the embedded neurons properly exhibit cell-cell interaction and drive intrinsic morphologies and function, which shows achievement of biocompatibility of the graphene electrodes. So in summary, we have been working on producing tiny 3D electrodes, step-by-step, using only four materials. For example, by mixing conductive polymer, >> PEDOT: PSS with silk fibroin, I made transparent and flexible 2D electrodes. By making a bilayer with silk fibroin with parylene, I demonstrated the self-assembly from 2D film to 3D shape. Finally, by transferring the graphene to paralyene, we could assembly tiny 3D electrodes. So in the future, we will continue to work on making bioelectrodes from the material science and biological viewpoints. However, these two approaches are not sufficient for the research or the bioelectronics. And we especially needed the technology of electrochemical assessment of fabricated electrodes and the method to lead up of obtain vital data and manipulation and analysis of obtain data. Therefore, I belong to both of the TUM and NTT research, in order to achieve the four system. So when I look over the world R&D of the bioelectronics, especially implantable electronics are very active, regardless of the university and industry. So firstly, John Rogers' group in University of Illinois, in United States, started to advocate about the implantable, flexible bioelectronics, more than 10 years ago. So now the research on, about it, is rapidly growing all over the world, not only US, but the Asia and Europe. So, the industrial community also tend to participate in this field. So I really hope to contributed to the scientific achievement and the creation of industry from the German basis, by making the most of my experience and cooperation with Japan and American side. So finally, I like to introduce my colleagues in TUM. So they are loved members and he, he is supervisor, Professor Bernhard Wolfrum, especially of the electrochemistry and electrochemical engineering process for biomedical application. So I'm so happy to work with this wonderful team and also appreciated the daily support of the members in NTT research in United States. Finally, let me just conclude by acknowledging my supervisor, mentors, Professor Wolfrum, Director Tomoike, and Dr. Alexander. And also the member from NTT who always support me, especially Mr. Kikuchi, Dr. Nakashima, Tsukada fellow, Director Goto, Dr. Yamamoto, and Director Sogawa. Finally, let me thanks Professor Offenhausser from Julich, for his kind assistance and introduction to this wonderful collaboration schemes. So, that's all. And I hope this presentation was useful to you. Thank you very much.

>>from Las Vegas. It's the Q covering. Next work. 2019 America's Do You buy Juniper Networks? Come back already. Jeffrey here with the Cube were in Las Vegas at Caesar's at the Juniper. Next work event. About 1000 people kind of going over a lot of new cool things. 400 gigs. Who knew that was coming out of new information for me? But that's not what we're here today. We're here for the fourth installment of around the Cube unpacking. I were happy to have all the winners of the three previous rounds here at the same place. We don't have to do it over the phone s so we're happy to have him. Let's jump into it. So winner of Round one was Bob Friday. He is the VP and CTO at Missed the Juniper Company. Bob, Great to see you. Good to be back. Absolutely. All the way from Seattle. Sharna Parky. She's a VP applied scientist at Tech CEO could see Sharna and, uh, from Google. We know a lot of a I happen to Google. Rajan's chef. He is the V p ay ay >>product management on Google. Welcome. Thank you, Christy. Here >>All right, so let's jump into it. So just warm everybody up and we'll start with you. Bob, What are some When you're talking to someone at a cocktail party Friday night talking to your mom And they say, What is a I What >>do you >>give him? A Zen examples of where a eyes of packing our lives today? >>Well, I think we all know the examples of the south driving car, you know? Aye, aye. Starting to help our health care industry being diagnosed cancer for me. Personally, I had kind of a weird experience last week at a retail technology event where basically had these new digital mirrors doing facial recognition. Right? And basically, you start to have little mirrors were gonna be a skeevy start guessing. Hey, you have a beard, you have some glasses, and they start calling >>me old. So this is kind >>of very personal. I have a something for >>you, Camille, but eh? I go walking >>down a mall with a bunch of mirrors, calling me old. >>That's a little Illinois. Did it bring you out like a cane or a walker? You know, you start getting some advertising's >>that were like Okay, you guys, this is a little bit over the top. >>Alright, Charlotte, what about you? What's your favorite example? Share with people? >>Yeah, E think one of my favorite examples of a I is, um, kind of accessible in on your phone where the photos you take on an iPhone. The photos you put in Google photos, they're automatically detecting the faces and their labeling them for you. They're like, Here's selfies. Here's your family. Here's your Children. And you know, that's the most successful one of the ones that I think people don't really think about a lot or things like getting loan applications right. We actually have a I deciding whether or not we get loans. And that one is is probably the most interesting one to be right now. >>Roger. So I think the father's example is probably my favorite as well. And what's interesting to me is that really a I is actually not about the Yeah, it's about the user experience that you can create as a result of a I. What's cool about Google photos is that and my entire family uses Google photos and they don't even know actually that the underlying in some of the most powerful a I in the world. But what they know is they confined every picture of our kids on the beach whenever they whenever they want to. Or, you know, we had a great example where we were with our kids. Every time they like something in the store, we take a picture of it, Um, and we can look up toy and actually find everything that they've taken picture. >>It's interesting because I think most people don't even know the power that they have. Because if you search for beach in your Google photos or you search for, uh, I was looking for an old bug picture from my high school there it came right up until you kind of explore. You know, it's pretty tricky, Raja, you know, I think a lot of conversation about A They always focus the general purpose general purpose, general purpose machines and robots and computers. But people don't really talk about the applied A that's happening all around. Why do you think that? >>So it's a good question. There's there's a lot more talk about kind of general purpose, but the reality of where this has an impact right now is, though, are those specific use cases. And so, for example, things like personalizing customer interaction or, ah, spotting trends that did that you wouldn't have spotted for turning unstructured data like documents into structure data. That's where a eyes actually having an impact right now. And I think it really boils down to getting to the right use cases where a I right? >>Sharon, I want ask you. You know, there's a lot of conversation. Always has A I replace people or is it an augmentation for people? And we had Gary Kasparov on a couple years ago, and he talked about, you know, it was the combination if he plus the computer made the best chess player, but that quickly went away. Now the computer is actually better than Garry Kasparov. Plus the computer. How should people think about a I as an augmentation tool versus a replacement tool? And is it just gonna be specific to the application? And how do you kind of think about those? >>Yeah, I would say >>that any application where you're making life and death decisions where you're making financial decisions that disadvantage people anything where you know you've got u A. V s and you're deciding whether or not to actually dropped the bomb like you need a human in the loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and a I doesn't know that, uh, you know, if it's always seen sheep in a field that when the sheep aren't there, that that isn't a sheep like it doesn't have that kind of recognition to it. So anything were we making decisions about parole or financial? Anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. >>Great. So shift gears. The team are Jeff Saunders. Okay, team, your mind may have been the liquid on my bell, so I'll be more active on the bell. Sorry about that. Everyone's even. We're starting a zero again, so I want to shift gears and talk about data sets. Um Bob, you're up on stage. Demo ing some some of your technology, the Miss Technology and really, you know, it's interesting combination of data sets A I and its current form needs a lot of data again. Kind of the classic Chihuahua on blue buried and photos. You got to run a lot of them through. How do you think about data sets? In terms of having the right data in a complete data set to drive an algorithm >>E. I think we all know data sets with one The tipping points for a I to become more real right along with cloud computing storage. But data is really one of the key points of making a I really write my example on stage was wine, right? Great wine starts a great grape street. Aye, aye. Starts a great data for us personally. L s t M is an example in our networking space where we have data for the last three months from our customers and rule using the last 30 days really trained these l s t m algorithms to really get that tsunami detection the point where we don't have false positives. >>How much of the training is done. Once you once you've gone through the data a couple times in a just versus when you first started, you're not really sure how it's gonna shake out in the algorithm. >>Yeah. So in our case right now, right, training happens every night. So every night, we're basically retraining those models, basically, to be able to predict if there's gonna be an anomaly or network, you know? And this is really an example. Where you looking all these other cat image thinks this is where these neural networks there really were one of the transformational things that really moved a I into the reality calling. And it's starting to impact all our different energy. Whether it's text imaging in the networking world is an example where even a I and deep learnings ruling starting to impact our networking customers. >>Sure, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of chihuahuas and blackberries, and I want to apply some machine intelligence to the problem. >>I mean, so you need to have the right data set. You know, Big is a relative term on, and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares, and then you're trying to detect some anomaly, right? If you train and I what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there, so you actually need to over sample. You have to create a population of data that allows you to detect images you can't say, Um oh, >>I'm going to reflect in my data set the percentage of black women >>in Seattle, which is something below 6% and say it's fair. It's not right. You have to be able thio over sample things that you need, and in some ways you can get this through surveys. You can get it through, um, actually going to different sources. But you have to boot, strap it in some way, and then you have to refresh it, because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time, and so you may have been able to find the one yesterday. But today it's a completely different ball game >>project to you, which comes first, the chicken or the egg. You start with the data, and I say this is a ripe opportunity to apply some. Aye, aye. Or do you have some May I objectives that you want to achieve? And I got to go out and find the >>data. So I actually think what starts where it starts is the business problem you're trying to solve. And then from there, you need to have the right data. What's interesting about this is that you can actually have starting points. And so, for example, there's techniques around transfer, learning where you're able to take an an algorithm that's already been trained on a bunch of data and training a little bit further with with your data on DSO, we've seen that such that people that may have, for example, only 100 images of something, but they could use a model that's trained on millions of images and only use those 100 thio create something that's actually quite accurate. >>So that's a great segue. Wait, give me a ring on now. And it's a great Segway into talking about applying on one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Is air you risking all kinds of interesting problems by taking that and applying it here, especially in light of when people are gonna go to outweigh the marketplace, is because I've got a date. A scientist. I couldn't go get one in the marketplace and apply to my data. How should people be careful not to make >>a bad decision based on that? So I think it really depends. And it depends on the type of machine learning that you're doing and what type of data you're talking about. So, for example, with images, they're they're they're well known techniques to be able to do this, but with other things, there aren't really and so it really depends. But then the other inter, the other really important thing is that no matter what at the end, you need to test and generate based on your based on your data sets and on based on sample data to see if it's accurate or not, and then that's gonna guide everything. Ultimately, >>Sharon has got to go to you. You brought up something in the preliminary rounds and about open A I and kind of this. We can't have this black box where stuff goes into the algorithm. That stuff comes out and we're not sure what the result was. Sounds really important. Is that Is that even plausible? Is it feasible? This is crazy statistics, Crazy math. You talked about the business objective that someone's trying to achieve. I go to the data scientist. Here's my data. You're telling this is the output. How kind of where's the line between the Lehman and the business person and the hard core data science to bring together the knowledge of Here's what's making the algorithm say this. >>Yeah, there's a lot of names for this, whether it's explainable. Aye, aye. Or interpret a belay. I are opening the black box. Things like that. Um, the algorithms that you use determine whether or not they're inspect herbal. Um, and the deeper your neural network gets, the harder it is to inspect, actually. Right. So, to your point, every time you take an aye aye and you use it in a different scenario than what it was built for. For example, um, there is a police precinct in New York that had a facial recognition software, and, uh, victim said, Oh, it looked like this actor. This person looked like Bill Cosby or something like that, and you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So the Russians point yes, like it. You can transfer learning to other a eyes, but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Um, even if you're a, I is explainable, and somebody tries to use it in a way that it was never intended to be used. The risk is much higher >>now. I think maybe I had, You know, if you look at Marvis kind of what we're building for the networking community Ah, good examples. When Marvis tries to do estimate your throughput right, your Internet throughput. That's what we usually call decision tree algorithm. And that's a very interpretive algorithm. and we predict low throughput. We know how we got to that answer, right? We know what features God, is there? No. But when we're doing something like a NAMI detection, that's a neural network. That black box it tells us yes, there's a problem. There's some anomaly, but that doesn't know what caused the anomaly. But that's a case where we actually used neural networks, actually find the anomie, and then we're using something else to find the root cause, eh? So it really depends on the use case and where the night you're going to use an interpreter of model or a neural network which is more of a black box model. T tell her you've got a cat or you've got a problem >>somewhere. So, Bob, that's really interested. So can you not unpacking? Neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the >>separate kind of process too. Get to the root cause. >>Yeah, assigned is always hard to say. Never. But inherently s neural networks are very complicated. Saito set of weights, right? It's basically usually a supervised training model, and we're feeding a bunch of data and trying to train it to detect a certain features, sir, an output. But that is where they're powerful, right? And that's why they basically doing such good, Because they are mimicking the brain, right? That neural network is a very complex thing. Can't like your brain, right? We really don't understand how your brain works right now when you have a problem, it's really trialling there. We try to figure out >>right going right. So I want to stay with you, bought for a minute. So what about when you change what you're optimizing? Four? So you just said you're optimizing for throughput of the network. You're looking for problems. Now, let's just say it's, uh, into the end of the quarter. Some other reason we're not. You're changing your changing what you're optimizing for, Can you? You have to write separate algorithm. Can you have dynamic movement inside that algorithm? How do you approach a problem? Because you're not always optimizing for the same things, depending on the market conditions. >>Yeah, I mean, I think a good example, you know, again, with Marvis is really with what we call reinforcement. Learning right in reinforcement. Learning is a model we use for, like, radio resource management. And there were really trying to optimize for the user experience in trying to balance the reward, the models trying to reward whether or not we have a good balance between the network and the user. Right, that reward could be changed. So that algorithm is basically reinforcement. You can finally change hell that Algren works by changing the reward you give the algorithm >>great. Um, Rajan back to you. A couple of huge things that have come into into play in the marketplace and get your take one is open source, you know, kind of. What's the impact of open source generally on the availability, desire and more applications and then to cloud and soon to be edge? You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of >>a I Yeah, I think open source is really important because I think one thing that's interesting about a I is that it's a very nascent field and the more that there's open source, the more that people could build on top of each other and be able to utilize what what others others have done. And it's similar to how we've seen open source impact operating systems, the Internet, things like things like that with Cloud. I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to t create these thes networks. And so the capacity for data and the capacity for compute is much higher. Edge is gonna be a very important thing, especially going into next few years. You're seeing Maur things incorporated on the edge and one exciting development is around Federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make EJ even more powerful. >>But it's got to be so dynamic, right? Because the fundamental problem used to always be the move, the computer, the data or the date of the computer. Well, now you've got on these edge devices. You've got Tanya data right sensor data all kinds of machining data. You've got potentially nasty hostile conditions. You're not in a nice, pristine data center where the environmental conditions are in the connective ity issues. So when you think about that problem yet, there's still great information. There you got latent issues. Some I might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break the break up? The problem to give you a step up? Well, we see a lot >>of customers do is they do a lot of training on the cloud, but then inference on the on the edge. And so that way they're able to create the model that they want. But then they get fast response time by moving the model to the edge. The other thing is that, like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud. But the other way to do is filter. And to try to figure out what data you want to send to the clouds that you can create the next days. >>Shawna, back to you let's shift gears into ethics. This pesky, pesky issue that's not not a technological issue at all, but right. We see it often, especially in tech. Just cause you should just cause you can doesn't mean that you should. Um so and this is not a stem issue, right? There's a lot of different things that happened. So how should people be thinking about ethics? How should they incorporate ethics? Um, how should they make sure that they've got kind of a, you know, a standard kind of overlooking kind of what they're doing? The decisions are being made. >>Yeah, One of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study, and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change of behavior. We need to ask questions like, What is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem, right? And this this can be true of the intention, goal gap or the intention action up. We can see when we regulated for cigarettes. What? We can't just make it look cool without telling them what the cigarettes are doing to them, right so we can apply the same principles moving forward. And they're pretty accessible without having to know. Oh, this philosopher and that philosopher in this ethicist said these things, it can be pretty human. The challenge with this is that most people building these algorithms are not. They're not trained in this way of thinking, and especially when you're working at a start up right, you don't have access to massive teams of people to guide you down this journey, so you need to build it in from the beginning, and you need to be open and based upon principles. Um, and it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem you need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to still that product for a wider audience, but it seems like >>they were on such a razor sharp edge. Right with Coca Cola wants you to buy Coca Cola and they show ads for Coca Cola, and they appeal to your let's all sing together on the hillside and be one right. But it feels like with a I that that is now you can cheat. Right now you can use behavioral biases that are hardwired into my brain is a biological creature against me. And so where is where is the fine line between just trying to get you to buy Coke? Which somewhat argues Probably Justus Bad is Jule cause you get diabetes and all these other issues, but that's acceptable. But cigarettes are not. And now we're seeing this stuff on Facebook with, you know, they're coming out. So >>we know that this is that and Coke isn't just selling Coke anymore. They're also selling vitamin water so they're they're play isn't to have a single product that you can purchase, but it is to have a suite of products that if you weren't that coke, you can buy it. But if you want that vitamin water you can have that >>shouldn't get vitamin water and a smile that only comes with the coat. Five. You want to jump in? >>I think we're going to see ethics really break into two different discussions, right? I mean, ethics is already, like human behavior that you're already doing right, doing bad behavior, like discriminatory hiring, training, that behavior. And today I is gonna be wrong. It's wrong in the human world is gonna be wrong in the eye world. I think the other component to this ethics discussion is really round privacy and data. It's like that mirror example, right? No. Who gave that mirror the right to basically tell me I'm old and actually do something with that data right now. Is that my data? Or is that the mirrors data that basically recognized me and basically did something with it? Right. You know, that's the Facebook. For example. When I get the email, tell me, look at that picture and someone's take me in the pictures Like, where was that? Where did that come from? Right? >>What? I'm curious about to fall upon that as social norms change. We talked about it a little bit for we turn the cameras on, right? It used to be okay. Toe have no black people drinking out of a fountain or coming in the side door of a restaurant. Not that long ago, right in the 60. So if someone had built an algorithm, then that would have incorporated probably that social norm. But social norms change. So how should we, you know, kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, That's no longer acceptable. We need to tweak this. I >>would have said in that example, that was wrong. 50 years ago. >>Okay, it was wrong. But if you ask somebody in Alabama, you know, at the University of Alabama, Matt Department who have been born Red born, bred in that culture as well, they probably would have not necessarily agreed. But so generally, though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying four things that are no longer the right thing to do? >>Well, I think I mean, as I said, I think you know what? What we know is wrong, you know is gonna be wrong in the eye world. I think the more subtle thing is when we start relying on these Aye. Aye. To make decisions like no shit in my car, hit the pedestrian or save my life. You know, those are tough decisions to let a machine take off or your balls decision. Right when we start letting the machines Or is it okay for Marvis to give this D I ps preference over other people, right? You know, those type of decisions are kind of the ethical decision, you know, whether right or wrong, the human world, I think the same thing will apply in the eye world. I do think it will start to see more regulation. Just like we see regulation happen in our hiring. No, that regulation is going to be applied into our A I >>right solutions. We're gonna come back to regulation a minute. But, Roger, I want to follow up with you in your earlier session. You you made an interesting comment. You said, you know, 10% is clearly, you know, good. 10% is clearly bad, but it's a soft, squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people, you know, kind of make judgments in this this big gray area in the middle? >>Yeah, and I think that is the toughest part. And so the approach that we've taken is to set us set out a set of AI ai principles on DDE. What we did is actually wrote down seven things that we will that we think I should do and four things that we should not do that we will not do. And we now have to actually look at everything that we're doing against those Aye aye principles. And so part of that is coming up with that governance process because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what what you should do and so that governments process is something we're doing. But I think it's something that every company is going to need to do. >>Sharon, I want to come back to you, so we'll shift gears to talk a little bit about about law. We've all seen Zuckerberg, unfortunately for him has been, you know, stuck in these congressional hearings over and over and over again. A little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. You know, he stumbled into some really big Harry nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen, unfortunately, a lot of those hearings is a lot of our elected officials are way, way, way behind there, still printing their e mails, right? So what is the role of the law? How should we think about it? What shall we What should we invite from fromthe law to help sort some of this stuff out? >>I think as an individual, right, I would like for each company not to make up their own set of principles. I would like to have a shared set of principles that were following the challenge. Right, is that with between governments, that's impossible. China is never gonna come up with same regulations that we will. They have a different privacy standards than we D'oh. Um, but we are seeing locally like the state of Washington has created a future of work task force. And they're coming into the private sector and asking companies like text you and like Google and Microsoft to actually advise them on what should we be regulating? We don't know. We're not the technologists, but they know how to regulate. And they know how to move policies through the government. What will find us if we don't advise regulators on what we should be regulating? They're going to regulate it in some way, just like they regulated the tobacco industry. Just like they regulated. Sort of, um, monopolies that tech is big enough. Now there is enough money in it now that it will be regularly. So we need to start advising them on what we should regulate because just like Mark, he said. While everyone else was doing it, my competitors were doing it. So if you >>don't want me to do it, make us all stop. What >>can I do? A negative bell and that would not for you, but for Mark's responsibly. That's crazy. So So bob old man at the mall. It's actually a little bit more codified right, There's GDP are which came through May of last year and now the newness to California Extra Gatorade, California Consumer Protection Act, which goes into effect January 1. And you know it's interesting is that the hardest part of the implementation of that I think I haven't implemented it is the right to be for gotten because, as we all know, computers, air, really good recording information and cloud. It's recorded everywhere. There's no there there. So when these types of regulations, how does that impact? Aye, aye, because if I've got an algorithm built on a data set in in person, you know, item number 472 decides they want to be forgotten How that too I deal with that. >>Well, I mean, I think with Facebook, I can see that as I think. I suspect Mark knows what's right and wrong. He's just kicking ball down tires like >>I want you guys. >>It's your problem, you know. Please tell me what to do. I see a ice kind of like any other new technology, you know, it could be abused and used in the wrong waste. I think legally we have a constitution that protects our rights. And I think we're going to see the lawyers treat a I just like any other constitutional things and people who are building products using a I just like me build medical products or other products and actually harmful people. You're gonna have to make sure that you're a I product does not harm people. You're a product does not include no promote discriminatory results. So I >>think we're going >>to see our constitutional thing is going applied A I just like we've seen other technologies work. >>And it's gonna create jobs because of that, right? Because >>it will be a whole new set of lawyers >>the holdings of lawyers and testers, even because otherwise of an individual company is saying. But we tested. It >>works. Trust us. Like, how are you gonna get the independent third party verification of that? So we're gonna start to see a whole terrorist proliferation of that type of fields that never had to exist before. >>Yeah, one of my favorite doctor room. A child. Grief from a center. If you don't follow her on Twitter Follower. She's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And Rahman up on the keynote this morning, talked about missed and and really, this kind of shifting workload of fixing things into an autonomous set up where the system now is, is finding problems, diagnosing problems, fixing problems up to, I think, he said, even generating return authorizations for broken gear, which is amazing. But autonomy opens up all kinds of crazy, scary things. Robert Gates, we interviewed said, You know, the only guns that are that are autonomous in the entire U. S. Military are the ones on the border of North Korea. Every single other one has to run through a person when you think about autonomy and when you can actually grant this this a I the autonomy of the agency toe act. What are some of the things to think about in the word of the things to keep from just doing something bad, really, really fast and efficiently? >>Yeah. I mean, I think that what we discussed, right? I mean, I think Pakal purposes we're far, you know, there is a tipping point. I think eventually we will get to the CP 30 Terminator day where we actually build something is on par with the human. But for the purposes right now, we're really looking at tools that we're going to help businesses, doctors, self driving cars and those tools are gonna be used by our customers to basically allow them to do more productive things with their time. You know, whether it's doctor that's using a tool to actually use a I to predict help bank better predictions. They're still gonna be a human involved, you know, And what Romney talked about this morning and networking is really allowing our I T customers focus more on their business problems where they don't have to spend their time finding bad hard were bad software and making better experiences for the people. They're actually trying to serve >>right, trying to get your take on on autonomy because because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own. But >>there's there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read. There's a book that just came out. Oh, what's the title? You look like a thing. And I love you. It was a book named by an A I, um if you want to learn a lot about a I, um and you don't know much about it, Get it? It's really funny. Um, so in there there is in China. Ah, factory where the Aye Aye. Is optimizing um, output of cockroaches now they just They want more cockroaches now. Why do they want that? They want to grind them up and put them in a lotion. It's one of their secret ingredients now. It depends on what parameters you allow that I to change, right? If you decide Thio let the way I flood the container, and then the cockroaches get out through the vents and then they get to the kitchen to get food, and then they reproduce the parameters in which you let them be autonomous. Over is the challenge. So when we're working with very narrow Ai ai, when use hell the Aye. Aye. You can change these three things and you can't just change anything. Then it's a lot easier to make that autonomous decision. Um and then the last part of it is that you want to know what is the results of a negative outcome, right? There was the result of a positive outcome. And are those results something that we can take actually? >>Right, Right. Roger, don't give you the last word on the time. Because kind of the next order of step is where that machines actually write their own algorithms, right? They start to write their own code, so they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in the space, you have huge data set. You got great technology. Got tensorflow. When will the machines start writing their own A their own out rhythms? Well, and actually >>it's already starting there that, you know, for example, we have we have a product called Google Cloud. Ottawa. Mel Village basically takes in a data set, and then we find the best model to be able to match that data set. And so things like that that that are there already, but it's still very nascent. There's a lot more than that that can happen. And I think ultimately with with how it's used I think part of it is you have to start. Always look at the downside of automation. And what is what is the downside of a bad decision, whether it's the wrong algorithm that you create or a bad decision in that model? And so if the downside is really big, that's where you need to start to apply Human in the loop. And so, for example, in medicine. Hey, I could do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need tohave have that place in many situations to make sure that it's being applied well. >>But is that just today? Or is that tomorrow? Because, you know, with with exponential growth and and as fast as these things are growing, will there be a day where you don't necessarily need maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and, you know, kind of pros and cons of treatment options that are more emotional than necessarily mechanical, because it seems like eventually that the doctor has a role. But it isn't necessarily in accurately diagnosing a problem. >>I think >>I think for some things, absolutely over time the algorithms will get better and better, and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where, where that is. So, for example, self driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is gonna be much lower than what's there with humans today, and so that will get there. But it will take time. >>And there was a day when will be illegal for you to drive. You have manslaughter, right? >>I I believe absolutely there will be in and and I don't think it's that far off. Actually, >>wait for the day when I have my car take me up to Northern California with me. Sleepy. I've only lived that long. >>That's right. And work while you're while you're sleeping, right? Well, I want to thank everybody Aton for being on this panel. This has been super fun and these air really big issues. So I want to give you the final word will just give everyone kind of a final say and I just want to throw out their Mars law. People talk about Moore's law all the time. But tomorrow's law, which Gardner stolen made into the hype cycle, you know, is that we tend to overestimate in the short term, which is why you get the hype cycle and we turn. Tend to underestimate, in the long term the impacts of technology. So I just want it is you look forward in the future won't put a year number on it, you know, kind of. How do you see this rolling out? What do you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. >>Yeah, you know, for me and, you know, the day of the terminus Heathrow. I don't know if it's 100 years or 1000 years. That day is coming. We will eventually build something that's in part of the human. I think the mission about the book, you know, you look like a thing and I love >>you. >>Type of thing that was written by someone who tried to train a I to basically pick up lines. Right? Cheesy pickup lines. Yeah, I'm not for sure. I'm gonna trust a I to help me in my pickup lines yet. You know I love you. Look at your thing. I love you. I don't know if they work. >>Yeah, but who would? Who would have guessed online dating is is what it is if you had asked, you know, 15 years ago. But I >>think yes, I think overall, yes, we will see the Terminator Cp through It was probably not in our lifetime, but it is in the future somewhere. A. I is definitely gonna be on par with the Internet cell phone, radio. It's gonna be a technology that's gonna be accelerating if you look where technology's been over last. Is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah, we're just on this curve of technology accelerations. This in the >>exponential curves China. >>Yeah, I think the thing I'm most excited about for a I right now is the addition of creativity to a lot of our jobs. So ah, lot of we build an augmented writing product. And what we do is we look at the words that have happened in the world and their outcomes. And we tell you what words have impacted people in the past. Now, with that information, when you augment humans in that way, they get to be more creative. They get to use language that have never been used before. To communicate an idea. You can do this with any field you can do with composition of music. You can if you can have access as an individual, thio the data of a bunch of cultures the way that we evolved can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building Thio Give a I to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the Internet to build a model on a data set. And I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not, um and so it's It's an interesting time because we're in that middle area. Um, and >>it's getting loud, all right, Roger will throw with you before we have to cut out, or we're not gonna be able to hear anything. So I actually start every presentation out with a picture of the Mosaic browser, because what's interesting is I think that's where >>a eyes today compared to kind of weather when the Internet was around 1994 >>were just starting to see how a I can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the company's I talked to the first question is, Why should I be using this? And what benefit does it give me? Why 70% ask you why? Yeah, and and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long >>run, and we underestimate the longer I think that every company out there and every product will be fundamentally transformed by eye over the course of the next decade, and it's actually gonna have a bigger impact on the Internet itself. And so that's really what we have to look forward to. >>All right again. Thank you everybody for participating. There was a ton of fun. Hope you had fun. And I look at the score sheet here. We've got Bob coming in and the bronze at 15 points. Rajan, it's 17 in our gold medal winner for the silver Bell. Is Sharna at 20 points. Again. Thank you. Uh, thank you so much and look forward to our next conversation. Thank Jeffrey Ake signing out from Caesar's Juniper. Next word unpacking. I Thanks for watching.

okay we're back at vmworld twenty twelve i'm john fairy with SiliconANGLE calm this is the cube this is our flagship telecast we go out to the events extract a signal from the noise and share that with you i'm joe and stu miniman my co-host with this segment and martine casado the co-founder of nicera you guys are ranking number one on our trending tool that we built under networking because it moved up to the top of the list because of vmworld company had spent a billion dollars for you guys jaishree from Arista called you guys the Instagram of networking kind of tongue-in-cheek on the huge buyout but hey congratulations great wired story today SR with you guys we've done about the talent you have and you brought over the vmworld and you're the top story here so congratulations thank you welcome to the cube thank you so take us through the logic and your motion around past year okay up until the buyout what a roller coaster so just share with us personally from Europe as an entrepreneur what was it like what highlights of what happened well I guess I've been very focused on changing networking right so for me it's been largely a technical ride and since we started the company five years ago we've been focusing on developing core technology and we did that for the first three years and then the last year to us was primarily about execution and customer engagement and so you know we've spent a lot of time proving the technology getting into production doing the support and fixing out that model and so it turned out is a very natural transition point when the acquisition happened because we had gotten traction we had starting to realize how difficult it is to address a market as large as this within a small start-up and so it was very welcome to come join a much larger company where we can kind of provide this as a much box so you guys have some big backers obviously you know they're all it's all well documented in the valley but every entrepreneur has that moments like wait a minute is this what I wanted is this tea but the dollars was so good and vmware's asti growing company what clicked for you what made you go is this the right thing take us through that decision you know absolutely so I mean like to me business guides behavior and at the end of the day the goal is is how do you change networking and have a very very firm belief that the access layer to that network is moving from within the network towards the edge and so we wanted to develop technologies that can use this position to re-implement networking and software and so once you get the core technology done once you prove it out with large customers once you prove out the market the question is is kind of what is the best way to have the biggest impact and I think in some respects you can look at vmware is one of the largest networking companies in the in the world just based on port count right the number of virtual ports that they control is as large as any large networking vendor so this is the opportunity of a lifetime to change in industry so like I've been doing this now you know sdn since those doing my PhD at stanford for so going on 10 years now and this is the the opportunity of a lifetime to actually have broad broad like planet scale impact well congratulations certainly you disrupted the market not only in the validation of the acquisition but as you guys were moving out and talking about some of the deployments you guys were doing it just came out of left field for most people but in the inside baseball sure people knew it was going on in terms of like how you guys are disrupting so so congratulations thank you here I want to talk about is also the messaging here at vmworld very solid around suffered to find datacenter sure and that really kind of brings you into a whole nother beyond networking so you know we've been covering converged infrastructure that's looking a look upon you know around house storage servers and networking so its bigger now than just networking right so now you're taking it to a whole nother leg of the journey so connect the dots out there for the folks between software virtualization and software-defined networking to this to the data center help them understand what is going to happen under that next leg of the journey yeah of course so we're all familiar with compute virtualization right I mean this is how vmware initially changed the world where the time it takes to provision a workload when from weeks to literally minutes like two minutes however I t isn't about single workloads ideas about applications and all the network services that those applications require for example firewalling or security or or monitoring debugging and so even though we reduce the time it took to provisional workload from weeks two minutes you still took days to do everything else that was required so if we take a broad scope if we take a broad look at a thai tea we still realize it still takes days to provision new applications and to provision new workloads and so the only way to get past this the next step that we want to take is to virtualize every aspect of infrastructure and so there's three of those there's there's compute which is virtualized their storage which we're making good progress on and there's network and network really is a pivot piece right it's the one piece that touches everything right it is between the compute in the storage it is between the different types of compute and so if you look at large data centers even cloud data centers the long pole in the tent and provisioning is the network so we must must virtualize that so the goal is the software-defined data center that's like everything's in software everything's totally dynamic you create it on demand you can move it its liquid it's like water it'll go anywhere but in order for this dream to be realized we've got to get the network out of the way and that's what the sierra does we've been talking about going to go and Wikibon we've just kicked up a whole kind of research section on what we're calling data infrastructure and really highlighting this modern era right and we kind of use a lot of sports analogies but you know a modern era meaning the new way not the old way right so you're a classic example of disruption in a new way so talk about the enablement that you see happening from a from a marker play standpoint just you know open your mind and share the crowds and vision around what you will enable with this because networking is has to be dynamic it has that makes total sense you guys have done it what's going to happen next in your mind's eye in terms of what the possibilities are yeah yeah absolutely so I think ultimately this is where we want to get to we want to build a platform that will provide that will recreate you know every Network Service and functionality in a virtualized manner in software from the edge and that means that there can be any service available anywhere over any type of hardware at any scale that's needed and it can be done all at virtualization timeframe so this is like you do an API call you get a virtual network abstraction you add a firewall to it you you configure ackles to it and so all of network configuration all of network services all of network operations become soft state it becomes like a VM image and it's available anywhere that you want it to and so that is the first step so I believe these transformations and systems and this happened many times in the past happen in two steps the first one is you virtualize and when you virtualize you offer the same thing but in a more flexible manner like when you virtualize compute you offered an x86 cpu but you did it in software after you virtualize you can actually change the operational paradigm like when you when they created compute virtualization they didn't immediately get to migration or snapshot or rewind all these other kind of operational benefits these came later so the first step is any networking anywhere you want at any scale automatically and then the second step is like drastically changing the operational paradigm so you can do things like better security so you can rewind configuration state I mean things that we can't even think about today because now we have this ultimate point of indirection that's virtualized this virtualized layer and who's the candidate for these developers just admins net admins all the above is it going to be software programmatic I mean how does that it takes DevOps right to a level of functionality that is just mind-boggling so yeah who's the new personnel yeah it was like who's life does this impact think what happens called a CI easy out there well I mean it's a good question whose life does this impact I mean I mean immediately anybody that's building out a data center like a cloud architect is going to have this this primitive that that they can use to architect better system just like you gave them a virtual machine they use that as a primitive for building better data centers now we're giving them virtual networks as a primitive build virtual data centers so the cloud architects job gets easier application developers don't have to worry about the basics of you know the way networks work our network configuration operations will have a lot more flexibility and the virtual layer of where they can move things around as far as the physical networking layer the problem actually becomes quite a bit simpler but you still have to focus the on the problem of building a physical network so for example when server virtualization came around you didn't like reduce the need for servers you needed more servers and just like the same thing will happen with with network virtualization which is you'll still need physical networks and they're going to probably have to be better physical networks but the problem now is more of how do you build a physical network with high capacity that can support any workload and less about doing all the operational stuff you do today how does an impact we just had chris hoffman from juniper who's now a worker he's been a big security buff a great guest for us but we just were just riffing on the security problems right so give us your perspective on how this new canvas of software-defined virtualization is gonna impact security paradise yeah so I mean I think there are a couple of answers i actually think ultimately the security model is improved honestly so yeah the original work was done with the intelligence community actually the the original funding for nasira came from the intelligence community my background I used to work for the intelligence agencies and when you move everything to software we already have a fundamental security paradigm which is crust consolidation in the hypervisor right and with network virtualization you follow the same paradigm which is you you entrust the hypervisor to enforce things like isolation enforce the security but now you've got a strongly authenticated endpoint there you're not guessing about things but but it requires the security community to evolve with the virtualization community so I think that there's much more of a socialization hurdle more of a social hurdle than a technical hurdle like all of the technology is there to do good security in the cloud I think getting the traditional vendors to evolve their tools into of all they're thinking it's much more difficult so I've got one more thing to add I actually think there's an opportunity to do security in entirely new ways ones that again can transform the industry so for example with virtualization you've got deep semantics into the workloads I mean you're in the hypervisor you can look inside the VMS you know who's using them know what applications they're using guy you could even know what the documents are being sent or or read or passed around and because you have this information at the edge if you virtualize the network as well you can pass this context into the network so now instead of like looking at packets and kind of trying to guess what application there is by looking at traffic you can actually get past like the ground truth information from the hypervisor so I think we have the potential so it's like drastically improved security that's Martine if you look at the networking industry there's lots of companies that have tried to change it in the past when you talk about innovation standards have a lot of times slow things down yep you know there's the legacy thought set you know great respect for ccie s but you know they have their install base in their way of doing things so you know there's there's so many pieces that make up networking and even the first time I saw your solution there's multiple standards and open you know groups working on this so you know how do you guys tease through and work through all of these issues yeah so clearly a very complex and multifarious question so I'm going to I'm going to attack one piece of it and we can go from there one of the primary benefits of actual virtualization like actual virtualization is that what you end up with should look like what you started with right so like if you're fundamentally changing an operational paradigm you're probably not doing virtualization so for example in a network virtualization solution the physical network is still a physical Network and it needs to be managed like a physical network with physical networking tools and in order to be fully virtualized the virtual abstraction I give you if I give you a virtual network that should also look like the networks that you've kind of grown to love as a child right they should have all the counters all the debugging the ability to interpose services right and so from from that standpoint you're still preserving the interfaces that people are used to it says there's more of them so like for example when I talk to a network operator today they're like oh this is confusing I've got virtualization I say actually instead of having one network that's really complicated you've got em and simple networks now you've got a very simple physical Network and if you got any virtual networks and they all all of the same interfaces that you use to manage it however there's one catch and that one catch is is there's an additional bit of information which is how do you map this virtual world to the physical world which happened in compute virtualization as well so like everybody understood a virtual machine everybody understood the physical machine but people weren't entirely sure how you debug the mapping between the two and that's incumbent as US is software providers and solution providers to provide that to provide the ability to to map from this kind of you know like platonic virtual reality down to this kind of gritty physical reality okay so from a standard standpoint you I mean you guys helped invent OpenFlow you guys created the open V switch you're heavily involved in OpenStack Andy there's been a lot of buzz since the acquisition about you know the involvement in OpenStack and yeah yeah kind of God how many people today everything in what's your thoughts on it yeah so let me also teach a tease apart you know two things before I get to that one so in networking standards are really important and like in the way standards work he's got a bunch of people that kind of go and talk about things and they design things they agree on them that's actually quite different than open source right and like their different processes different communities different rules of engagement so let me focus on the open source first then we'll go back to the standards thank you because I perfect just to give you a little bit foreshadowing like I hope the world goes open source not open Stan so can we do to it so but we'll get there right so as far as open source yes so I wrote the first version of open flow I mean it came out of my thesis right the first three employees of nicera created the first craft of open flow and it was it was just something that we wanted to use to control switches right i mean we wrote the first reference implementation the first open flow controller you know we seeded the stanford stuff of course i'm a consulting a faculty at stanford so i was involved there we also are the primary developers behind open V switch it's in the linux kernel you know we've probably put you know many millions of dollars in developing that it's used by competitors and partners alike that's used in many clouds and then we've heavily participated in an OpenStack in particular you know where the Delete on quantum which is the networking portion of OpenStack we've done a lot of development bear so as far as the merger is concerned the acquisitions concerned none of that will change we're fully committed to open V switch to OpenStack will continue and even escalate our contribution there quick quick note on OpenStack i was told that something for folks have actually entered some code into the OpenStack of storage just kind of curious about that so and we touched many areas of OpenStack and again the the networking piece touches everything and you know we do a lot of the development on quantum and we run actually nasira internally randa an openstack cloud for internal dev cloud and we've got thousands of VMs on it that we use it and so we're heavily we're like heavy users and contributors to both OpenStack and linux I mean if you look in Linux we've actually fixed a lot of the veal and issues in the kernel right so like and we're very very involved in open source but we're involved as users right like we don't sell you know linux we don't sell OpenStack but we do believe for to have a vibrant ecosystem is nice to have these tools out there and as we use the tools we fix them and we contribute it back okay what about multi hypervisor environments because that was one of the things that really impressed me about like the open D switch is it really doesn able kind of that that multi hypervisor even more than kind of heterogeneous switches it's the multi hypervisor piece yeah that's right so if you kind of zoom away like I think we've had like a fairly myopic focus in the industry on servers over the last 10 years and it's like if you zoom away from the server to a data center you end up in this realm of heterogeneous technologies multiple cloud management systems multiple hypervisors and so when we came up with our our initial strategy of building a network virtualization layer we knew networks touch everything we must support all of those technologies and so it was like a fundamental tenant of the technology that we might support all hypervisors and physical hardware switches as well because there are workloads that are not july's and so you know open V switch itself which is the V switch that we use it's in sports in kvm bare metal linux it's been ported to bsd it's been ported to other operating systems it's been ported to top-of-rack hardware switches so we can use all of them to do to do network virtualization so mark can I want to ask you about the sufferer define partnering strategy from a technical perspective obviously we're really big believers in open source as well they love that we'd love to think it's great and it's now a business model in the industry so it's great to see all that work as vmware now with you guys in the family there go to other unifying clouds so they took a multiple clouds at this point so you know what would you bring to the table from hyper Microsoft hyper-v environment and other big vendors HP Dell yeah Microsoft what can you bring to the table in working with those guys or are you outgoing are you talking to them and and if you were having those conversations what does what would those conversations be well so the product itself that we're developing and we we do bring to market now we will continue is a network virtualization platform that's multi hypervisor right and so the goal is to have something that you can deploy into any cloud environment regardless of what CMS are running and regardless of what of what hypervisors they're using now we have many many partners whether their system integrators with the solution partners and so you know we don't have any religion on on the type of technologies in play we want to provide the best virtual networking solution in the industry and that's really our primary our primary focus let me ask you about it Trent some trends in the in the tech community in in academia and the research areas obviously at this example just randomly low-level virtual machines that kind of those kinds of shifts are happening could you talk about just what you're tracking right now that your get your eye on in terms of what's going on at some of the top university obviously low-level virtual machines at the University of Illinois and in Chicago so what other areas can you share with us that you monitoring listen this is a great question to ask a nap academic and I'm going to totally disappoint you in that I you know I i I'm on a lot of pcs and I follow a lot of research I mean you know I submit papers you know all the time and like I've mostly lost faith in the academic process on the research side lately which i haven't relevant so in terms of trends no but that's exactly the point I think that there's enough vision to last for a century and like now it's time to do work and if it were up to me we would all be taking these ideas that we've come up with over the last 10 years there's very few new ones in my opinion and we'd be executing like crazy and so well again while i'm on the pcs and while i do review the papers i do submit the papers i think we should all focus on like changing infrastructure into software executing like hell and changing the world that way and so and I don't have a really bad attitude about this especially as abuse or but it's a bad attitude okay we say it we hit it all hang out so final question for me and if she wants to get one more in and don't you can't say the acquisition as the answer what is the biggest surprise that that that you fell out of your chair over the past 24 months around you in the industry in your entrepreneurial venture here now at VMware and it could be like a surprise and this trend didn't happen that happened that you know these are the things that happened it could be good or bad what's the biggest surprise that caught you off guard this year that's 24 months yeah it's a good question I think the one that actually been a little the most shocking is how how difficult is being just very honest is how difficult to manage perception in the industry and if you look at kind of social media and you look at a lot of the buzz in the rags so much of it is generated by non disinterested parties so invested parties and so I think it's possible to be a perfectly good citizen and then get paint in a very negative light or be a very negative citizen and be painted in a very good light and it's been counterintuitive to me how you manage this effectively like almost a dynamic feedback system so for example this year has been an enormous contributor to open source I think we've contributed more than anybody in our space by you know factor of 10 or more we contributed most of the core technologies and often people like well but it's a proprietary solution on the other hand there sometimes we're like okay this is a closer source product people like we should use this here because it's the open solution and so well I think that definitely felt on both sides you know being both open source and close or sometimes it's worked for us and for the wrong reasons sometimes it's not worked for us for the right reasons and so that dynamic has been the least intuitive to me so I'm not sure I fell off my chair but definitely it's been the most surprising yeah and you know and that's what we're trying to solve a SiliconANGLE as we say we're agile media and ultimately with social media the whole media business is changing so we know one of the things that we care about here so that's why we have the qubits we just this is raw data we want to share be provocative be edgy is too it's a data-driven world and we believe the media business is absolutely screwed up beyond all recognition so so because of just lack of fact-checking just old techniques aren't working and but it's the same game right so it's just so things circulate things get branded and we've seen a time and time again I've seen great people show up as like almost painted as criminals yeah so it's just a sad state of reporting and media so would agree with you there okay John so if I if I can have that one last question your machine you know the networking industries is a big community and when you talk about kind of the jobs that people are doing today what's your recommendation to folks out there in the networking industry what should what should they start to you know we'd or you know start playing with to kind of understand where things are going down the line honestly I don't want to say a cliche but I actually really believe this one I think I think networking networks are evolving to become proper systems and proper systems in an end-to-end manner meaning that goes a very well-defined hardware a software layer they all work together and I think the data center is is becoming a large computer and I think the most important thing is to view the industry and that lens meaning you know I would get as much information as I could on how guys like Google or Amazon or Facebook build their data centers and you realize that if you do a cross-section of these things like the Capital savings the operational savings the flexibility of the software like that's changing the world and if it's not changing the world directly by changing infrastructure it's changing the world to the surfaces they deliver and understanding that model in your bones I think is the beacon going forward so if it were me the first thing I do is I really understand why they make those decisions what the benefits are and I would use that to guide my learning going forward okay Martinez out of this co-founder of this year now at do you have a title at VMware yet or do you I mean did i do I don't know my head honcho of the Sierra am where Thanks coming inside the cube really preciate it we right back with our next guest we're going to wrap up try to wrap up the day as they start to bon jovi soundcheck here at V emerald 2012 this is SiliconANGLE calm and Wikibon doors continues coverage at vmworld great thank you