(upbeat techno music) >> From Sand Hill Road, in the heart of Silicon Valley, it's theCUBE. Presenting the People First Network, insights from entrepreneurs and tech leaders. >> Hello, everyone, I'm John Furrier the co-host on theCUBE, founder of SiliconANGLE Media. We are here at Sand Hill Road, at Mayfield for the 50th anniversary celebration and content series called The People First Network. This is a co-developed program. We're going to bring thought leaders, inspirational entrepreneurs and tech executives to talk about their experience and their journey around a people first society. This is the focus of entrepreneurship these days. I'm here with Navin Chaddha who's the managing director of Mayfield. Navin, you're kicking off the program. Tell us, why the program? Why People First Network? Is this a cultural thing? Is this part of a program? What's the rationale? What's the message? >> Yeah, first of all I want to thank, John, you and your team and theCUBE for co-hosting the People First Network with us. It's been a real delight working with you. Shifting to people first, Mayfield has had a long standing philosophy that people build companies and it's not the other way around. We believe in betting on great people because even if their initial idea doesn't pan out, they'll quickly pivot to find the right market opportunity. Similarly we believe when the times get tough it's our responsibility to stand behind people and the purpose of this People First Network is people like me were extremely lucky to have mentors along the way, when I was an entrepreneur and now as a venture capitalist, who are helping me achieve my dreams. Mayfield and me want to give back to other entrepreneurs, by bringing in people who are luminaries in their own fields to share their learnings with other entrepreneurs. >> This is a really great opportunity and I want to thank you guys for helping us put this together with you guys. It's a great co-creation. The observation that we're seeing in Silicon Valley and certainly in talking to some of the guests we've already interviewed and that will be coming up on the program, is the spirit of community and the culture of innovation is around the ecosystem of Silicon Valley. This has been the bedrock. >> Mm-hmm. >> Of Silicon Valley, Mayfield, one of the earliest if not the first handful of venture firms. >> Mm-hmm. >> Hanging around Stanford, doing entrepreneurship, this is a people culture in Silicon Valley and this is now going global. >> Mm-hmm. >> So great opportunity. What can we expect to see from some of the interviews? What are you looking for and what's the hope? >> Yeah, so I think what you're going to see from the interviews is, we are trying to bring around 20 plus people, and they'll be many John on the interview besides you. So there will be John Chambers, ex-chairman and CEO of Cisco. There'll be John Zimmer, president and co founder of Lyft. And there also will be John Hennessy who will be our first interview, with him, from Stanford University. And jokes apart, there'll be like 20 plus other people who will be part of this network. So I think what you're going to see is, goings always don't go great. There's a lot of learnings that happen when things don't work out. And our hope is, when these luminaries from their professions, share their learnings the entrepreneurs will benefit from it. As we all know, being an entrepreneur is hard. But sometimes, and many times, actually it's also a lonely road and our belief is, and I strongly personally also believe in it, that great entrepreneurs believe in continuous learning and are continuously adapting themselves to succeed. So our hope is, this People First Network serves as a learning opportunity from entrepreneurs to learn from great leaders. >> You said a few things I really admire about Mayfield and I want to get your reaction because I think is a fundamental for society. Building durable companies is about the long game because people fail and people succeed but they always move on. >> Mm-hmm. >> They move on to another opportunity. They move on to another pursuit. >> Mm-hmm. >> And this pay it forward culture has been a key thing for Silicon Valley. >> It absolutely has been. >> What's the inspiration behind it, from your perspective? You mentioned your experiences. Tell us a story and experience you've had? >> Yeah, so I would say, first of all, right, since we strongly believe people make products and products don't make people, we believe venture capital and entrepreneurship is about like running a marathon, it's not a sprint. So if you take a longterm view, have a strong vision and mission which is supported with great beliefs and values? You can do wonders. And our whole aim, not only as Mayfield but other venture capitalists, is to build iconic companies which are built to last which beyond creating jobs and economic wealth, can give back to the society and make the world a better place to work, live and play. >> You know one of the things that we are passionate about at theCUBE, and on SiliconANGLE Media is standing by our community. >> Mm-hmm. >> Because people do move around and I think one of the things that is key in venture capital now, than ever before is not looking for the quick hit. >> Mm-hmm. >> It's standing by your companies in good times and in bad. >> Mm-hmm. >> Because this is about people and you don't know how things might turn out, how a company might end up in a different place. We've heard some of your entrepreneurs talk about that, that the outcome was not how they envisioned it when they started. >> Mm-hmm. >> This is a key mindset for a business. >> It absolutely is, right? Let's look at a few examples. One of our most successful companies is Lyft. When we backed it at Series A, it was called Zimride. They weren't doing what they were doing, but the company had a strong vision and mission of changing the way people transport and given that, they were A plus people, as I mentioned earlier. The initial idea wasn't going to be a massive opportunity. They quickly pivoted to go after the right market opportunity. And hence, again and again, right? Like to me, it's all about the people. >> Navigating those boards is sometimes challenging and we hope that this content will help people, inspire people, help them discover their passion, discover people that they might want to work with. We really appreciate your support and thank you for contributing your network and your brand and your team in supporting our mission. >> Yeah, it's been an absolute pleasure and we hope the viewers and especially entrepreneurs can learn from the journeys of many iconic people who have built great things in their careers. >> Were here at Sand Hill Road, at Mayfield's venture capital headquarters in sunny Silicon Valley, California, Stanford, California, Palo Alto California, all one big melting pot of innovation. I'm here with John Hennessy, who's the Stanford President Emeritus, also the director of the Knight Hennessy Scholarship. Thanks for joining me today for this conversation. >> Delighted to be here, John. >> So I wanted to get your thoughts on the history of the valley. Obviously, Mayfield, celebrating their 50th anniversary and Mayfield was one of those early venture capital firms that kind of hung around the barbershop, looking for a haircut. Stanford University was that place. Early on this was the innovation spark that created the valley. A lot of other early VCs as well, but not that many in the early days and now 50 years later, so much has changed. What's your thoughts on the arc of entrepreneurship around Stanford, around Silicon Valley? >> Well, you're right, it's been an explosive force. I mean, I think there were a few companies out here on Sand Hill Road at that time. Now nearly the number of venture firms there are today. But I think the biggest change has been the kinds of technologies we build. You know, in those days, we built technologies that were primarily for other engineers or perhaps they were tandem computers being built for business interest. Now we build technologies that change people's lives, every single day and the impact on the world is so much larger than it was and these companies have grown incredibly fast. I mean, you look at the growth rate? We had the stars of the earlier compared to the Googles and Facebooks of today, it's small growth rates, so those are big changes. >> I'm excited to talk with you, because you're one of the only people that I can think of that has seen so many different waves of innovation. You've been involved in many of them yourself, one of the co-founders of MIPS, chairman of the board of Alphabet, which is Google, Google's holding company, the large holdings they have and just Stanford in general has been, you know, now with CAL, kind of the catalyst for a lot of the change. What's interesting is, you know, the Hewlett-Packards, the birthplace of Silicon Valley, that durable company view. >> Mm-hmm. >> Of how to build a company and the people that are involved is really a, still, essential part of it. Certainly happening faster, differently. When you look at the waves of innovation, is there anything that you could look at and say, hey, this is the consistent pattern that we see emerging of these waves? Is it a classic formula of engineers getting together trying to solve problems? Is it the Stanford drop out PH.d program? Is there a playbook? Is there a pattern that you see in the entrepreneurship over the years? >> You know, I think there are these waves that are often induced by big technology changes, right? The beginning of the personal computer. The beginning of the internet. The world wide web, social media. The other observation is that it's very hard to predict what the next one will be. (laughing) If it was easier to predict, there would be one big company, rather than lots of companies riding each one of these waves. The other thing I think that's fascinating about them is these waves don't create just one company. They create a whole new microcosm of companies around that technology which exploit it and bring it to the people and change people's lives with it. >> And another thing is interesting about that point is that even the failures have DNA. You see people, big venture backed company, I think Go is a great example, you think about those kinds of companies. The early work on mobile computing, the early work on processors that you were involved in MIPS. >> Mm-hmm. >> They become successful and/or may/may not have the outcomes but the people move on to other companies to either start companies. This is a nice flywheel, this is one of the things that Silicon Valley has enjoyed over the years. >> Yeah, and just look at the history of RISC technology that I was involved in. We initially thought it would take over the general purpose computing industry and I think Intel responded in an incredible way and eventually reduced the advantage. Now here we are 30 years later and 95%/98% of the processors in the world are RISC because of the rise of mobile, internet of things, dramatically changing where the processors were. >> Yeah. >> They're not on the desktop anymore, they're scattered around in very different ways. >> It's interesting, I was having a conversation with Andy Kessler, who used to be an analyst back at the time for Morgan Stanley. He then became an investor. And he was talking about, with me, the DRAM days when the Japanese were dumping DRAMs and then that was low margin business, and then Intel said, "Hey, no problem. "We'll let go of the DRAM business." but they created Pentium and then the micro processor. >> Right. >> That spawned a whole nother wave, so you see the global economy today, you see China, you see people manufacturing things at very low cost, Apple does work out there. What's your view and reaction to the global landscape? Because certainly things are changed a bit but it seems to be some of the same? What's your thoughts on the global landscape and the impact of entrepreneurs? >> It certainly is global. I mean, I think in two ways. First of all, supply chains have become completely global. Look at how many companies in the valley rely on TSMC as their primary source of silicon? It's a giant engine for the valley. But we also see, increasingly, even in young companies a kind of global, distributed engineering scheme where they'll have a group in Taiwan, or in China or in India that'll be doing part of the engineering work and they're basically outsourcing some of that and balancing their costs and bringing in other talent that might be very hard to hire right now in the valley or very expensive in the valley. And I think that's exciting to see. >> The future of Silicon Valley is interesting because you have a lot of the fast pace, it seems like ventures have shrink down in terms of the acceleration of the classic building blocks of how to get a company started. You get some funding, engineers build a product, they get a prototype, they get it out. Now it seems to be condensed. You'll see valuations of a billion dollars. Can Silicon Valley survive the current pace given the real estate prices and some of the transportation challenges? What's your view on the future of Silicon Valley? >> Well my view is there is no place like the valley. The interaction between great universities, Stanford and Cal, UCSF if you're interested in biomedical innovation and the companies makes it just a microcosm of innovation and excellence. It's challenges, if it doesn't solve it's problems on housing and transportation, it will eventually cause a second Silicon Valley to rise and challenge it and I think that's really up to us to solve and I think we're going to have to, the great leaders, the great companies in the valley are going to have to take a leadership role working with the local governments to solve that problem. >> On the Silicon Valley vision of replicating it, I've seen many people try, other regions try over the years and over the 20 years, my observation is, they kind of get it right on paper but kind of fail in the execution. It's complicated but it's nuanced in a lot of ways but now we're seeing with remote working and the future of work changing a little bit differently and all kinds of new tech from block chain to, you name it, remote working. >> Right. >> That it might be a perfect storm now to actually have a formula to replicate Silicon Valley. If you were advising folks to say, hey, if you want to replicate Silicon Valley, what would be your advice to people? >> Well you got to start with the weather. (laughing) Always a challenge to replicate that. But then the other pieces, right? Some great universities, an ecosystem that supports risk taking and smart failure. One of the great things about the valley is, you're a young engineer/computer scientist graduating, you come here. You go to a start up company, so what it fails? There's 10 other companies you can get a job with. So there's a sense of this is a really exciting place to be, that kind of innovation. Creating that, replicating that ecosystem, I think and getting all the pieces together is going to be the challenge and I think the area that does that will have a chance at building something that could eventually be a real contestant for the second Silicon Valley. >> And I think the ecosystem and community is the key word. >> And community, absolutely. >> So I'll get your thoughts on your journey. Take us through your journey. MIPS co-founder, life at Stanford, now with the Knights Scholarship Program that you're involved in, the Knight Hennessy Scholarship. What lessons have you learned from each kind of big sequence of your life? Obviously in the start up days. Take us through some of the learnings. >> Yeah. >> Whether it's the scar tissue or the success, you know? >> Well, no, the time I spent starting MIPS and I took a leave for about 18 months full-time from the university, but I stayed involved after that on a part time basis but that 18 months was an intensive learning experience because I was an engineer. I knew a lot about the technology we're building, I didn't know anything about starting a company. And I had to go through all kinds of things, you know? Determining who to hire for CEO. Whether or not the CEO would be able to scale with the company. We had to do a layoff when we almost ran out of cash and that was a grueling experience but I learned how to get through that and that was a lesson when I came back to return to the university, to really use those lessons from the valley, they were invaluable. I also became a much better teacher, because here I had actually built something in industry and after all, most of our students are going to build things, they're not going to become future academics. So I went back and reengaged with the university and started taking on a variety of leadership roles there. Which was a wonderful experience. I never thought I'd be university president, not in a million years would I have told you that was, and it wasn't my goal. It was sort of the proverbial frog in the pot of water and the temperature keeps going up and then you're cooking before you know it. >> Well one of the things you did I thought was interesting during your time in the 90's as the head of the computer science department is a lot of that Stanford innovation started to come out with the internet and you had Yahoo, you had Google, you had PH.ds and you guys were okay with people dropping out, coming back in. >> Yeah. >> So you had this culture of building? >> Yup. >> Tell us some of the stories there, I mean Yahoo was a server under the desk and the web exploded. >> Yeah, it was a server under the desk. In fact, Dave and Jerry's office was in a trailer and you go into their room and they'd have pizza boxes and Coke cans stacked around because Yahoo use was exploding and they were trying to build this portal out to serve this growing community of users. Their machine was called Akebono because they were both big sumo wrestling fans. Then eventually, the university had to say, "You guys need to move this off campus "because it's generating 3/4 of the internet traffic "at the university and we can't afford it." (laughing) So they moved off campus and of course figured out how to use advertising as a monetization model. And that changed a lot of things on the internet because that made it possible for Google to come along years later. Redo search in a way that lots of us thought, there's nothing left to do in search, there's just not a lot there. But Larry and Sergey came up with a much better search algorithm. >> Talk about the culture that you guys fostered there because this, I think, is notable, in my mind, as well as some of the things I want to get into about the interdisciplinary. But at that time, you guys fostered a culture of creating and taking things out and there was an investment group of folks around Stanford. Was it a policy? Was it more laid back? >> No, I think-- >> Take us through some of the cultural issues. >> It was a notion of what really matters in the world. How do you get impact? Because in the end that's what the university really wants to do. Some people will do impact by publishing a paper or a book but some technologies, the real impact will occur when you take it out into the real world. And that was a vision that a lot of us had, dating back to Hewlett-Packard, of course but Jim Clark at Silicon Graphics, the Cisco work, MIPS and then, of course, Yahoo and Google years later. That was something that was supported by both the leadership of the university and that made it much easier for people to go out and take their work and take it out to the world. >> Well thank you for doing that, because I think the impact has been amazing and had transcended a lot of society today. You're seeing some challenges now with society. Now we have our own problems. (laughing) The impact has been massive but now lives are being changed. You're seeing technology better lives so it's changing the educational system. It's also changing how people are doing work. Talk about your current role right now with the Knight Hennessy Scholarship. What is that structured like and how are you shaping that? What's the vision? >> Well our vision, I became concerned as I was getting ready to leave the president's office that we, as a human society, were failing to develop the kinds of leaders that we needed. It seemed to me it was true in government. It was true in the corporate world. It was even true in some parts of the nonprofit world. And we needed to step back and say, how do we generate a new community of young leaders who are going to go out, determined to do the right thing, who see their role as service to society? And their success aligned with the success of others? We put together a small program. We put together a vision of this. I got support from the trustees. I went to ask my good friend Phil Knight, talked to him about it, and I said, "Phil I have this great idea," and I explained it to him and he said, "That's terrific." So I said, "Phil I need 400 million dollars." (laughing) A month later he said, "Yes," and we were off and running. Now we've got 50 truly extraordinary scholars from around the world, 21 different birth countries. Really, some of them have already started nonprofits that are making a big difference in their home communities. Others will do it in the future. >> What are some of the things they're working on? And how did you guys roll this out? Because, obviously, getting the funding's key but now you got to execute. What are some of the things that you went through? How did you recruit? How did you deploy? How did you get it up and running? >> We recruited by going out to universities around the world, and meeting with them and, of course, using social media as well. If you want get 21 year and 22 year olds to apply? Go to social media. So that gave us a feed on some students and then we thought a lot, our goal is to educate people who will be leaders in all walks of life. So we have MBAs, we have MDs, we have PH.ds, we have JDs. >> Yeah. >> A broad cohort of people, build a community. Build a community that will last far beyond their time at Stanford so they have a connection to a community of like minded individuals long after they graduate and then try to build their leadership skills. Bringing in people who they can meet with and hear from. George Schultz is coming in on Thursday night to talk about his journey through government service in four different cabinet positions and how did he address some of the challenges that he encountered. Build up their speaking skills and their ability to collaborate with others. And hopefully, these are great people. >> Yeah. >> We just hope to push their trajectory a little higher. >> One of the things I want you is that when Steve Jobs gave his commencement speech at Stanford, which is up on YouTube, it's got zillions and zillions of views, before he passed away, that has become kind of a famous call to arms for a lot of young people. A lot of parents, I have four kids and the question always comes up, how do I get into Stanford? But the question I want to ask you is more of, as you have the program, and you look for these future leaders, what advice would you give? Because we're seeing a lot of people saying, hey you know people build their resume, they say what they think people want to hear to get into a school, you know Steve Job's point said, "Follow your passion, don't live other people's dogma" these are some of the themes that he shared during that famous commencement speech in Stanford. Your advice for the next generation of leaders? How should they develop their skills? What are some of the things that they can acquire? Steve Jobs was famous to say in interviews, "What have you built?" >> Yeah. >> "Tell me something that you've built." It's kind of a qualifying question. So this brings up the question of, how should young people develop? How should they think about, not just applying and getting in but being a candidate for some of these programs? >> Well I think the first thing is you really want to challenge yourself. You really want to engage your intellectual passions. Find something you really like to do. Find something that you're also good at because that's the thing that'll get you out of bed on weekends early, and you'll go do it. I mean, if you asked me about my career? And asked me about my number one hobby for most of my career? It was my career. I loved being a professor. I loved research, I love teaching. That made it very easy to do it with energy and excitement and passion. You know there's a great quote in Steve Job's commencement speech where he says, "I look in the mirror every morning "and if too many days in a row I find out "I don't like what I'm going to do that day, "it's time for a change." Well I think it's that commitment to something. It's that belief in something that's bigger than yourself, that's about a journey that you're going to go on with others in that leadership role. >> I want to get your thoughts on the future for young people and society and business. It's very people centric now. You're seeing a lot of the younger generation look for mission driven ventures, they want to make a difference. But there's a lot of skills out there that are not yet born, yet. There's jobs that haven't been invented yet. Who handles autonomous vehicles? What's the policy? These are societal and technology questions. What are some of things that you see that are important to focus on for some of these new skills? There's a zillion new cyber security jobs open, for instance. >> Right. I mean there's thousands and thousands of openings for people that don't have those skills. >> Well I think we're going to need two different types of people. The traditional techno experts that we've always had but we're also going to need people that have a deep understanding of technology but are deeply committed to understanding it's impact on people. One of the problems we're going to have with the rise of artificial intelligence is we're going to have job displacements. In the longterm, I'm a believer that the number of opportunities created will exceed those that get destroyed but there'll be a lot of jobs that are deskilled or actually eliminated. How are we going to help educate that cohort of people and minimize the disruption of this technology? Because that disruption is really people's live that you're playing with. >> It's interesting, the old expression of ATMs will kill the bank branch but yet, now there's more bank branches than ever before. >> Than ever before, right? >> So, I think you're right on that, I think there'll be new opportunities. Entrepreneurship certainly is changing and I want to get your thoughts. This is the number one question I get from young entrepreneurs is, how should I raise money? How should I leverage money investors and my board? As you build your early foundational successes whether you're an engineer or a team, putting that E team together, entrepreneurial team is critical and that's just not people around the table of the venture. >> Correct. >> It's the support service providers and advisors and board of directors. How should they leverage their investors and board? How should they leverage that resource and not make it contentious, make it positive? >> Make is positive, right? So the best boards are collaborative with the management team, they work together to try to move the company forward. With so many angels now investing in these young companies there's an opportunity to bring in experience from somebody who's already had a successful entrepreneurial venture and looking for really deciding who do you want your investor to be? And it's not just about who gives you the highest valuation. It's also about who'll be there when things get tough? When the cash squeeze occurs and you're about to run out of money and you're really in a difficult situation? Who will help you build out the rest of your management team? Lots of young entrepreneurs, they're excited about their technology. >> Yeah. >> They don't have any management experience. (laughing) They need help. >> Yeah. >> They need help building that team and finding the right people for the company to be successful. >> I want to get thoughts on Mayfield. The 50th anniversary, obviously, they've been around longer than me, I'm going to be 53 this year. I remember when I first pitched Yogan DeGaulle in 1990, my first venture, he passed, but, Mayfield's been around for a while. I mean, Mayfield was the name of the town around here? >> Right. >> And has a lot of history. How do you see the relationship with the ventures and Stanford evolving? Are they still solid? They're doing well? Is it evolved? There's a new program going on? I see much more integration. What's the future of venture? >> Well I think the university's still a source of many ideas, obviously the notion of entrepreneurship has spread much more broadly than the university. And lots of creative start ups are spun out of existing companies or a group of young entrepreneurs that were in Google or Facebook early and now decide they want to go do their own thing. That's certainly happens but I think that ongoing innovation cycle is still alive. It's still dependent on the venture community and their experience having built companies. Particularly when you're talking about first time entrepreneurs. >> Yeah. >> Who really don't have a lot of depth. >> My final question I want to ask you is obviously one relating, pure to my heart, is computer science. I got my degree in the 80's during the systems revolution. Fun time, a lots changed. Women in computer science, the surface area of what computer science is. >> Mm-hmm. >> It was interesting, there was a story in Bloomberg that was debunked but people were debating if the super micros was being hacked by a chip in the system. >> Right. >> And more people don't even know what computer architecture is, I was like, hey now, the drivers might able to inject malware. So you need computer architecture, a book you've written. >> Mm-hmm. >> Academically, to programming so the range of computer science has changed. The diversity has changed. What's your thoughts on the current computer science curriculums? The global programs? Where's it going and what's your perspective on that? >> So I think computer science has changed dramatically. When I was a graduate student, you could arguably take a full set of breadth courses across the discipline. Maybe only one course in AI or one course in data base if you were a hardware or systems person but you could do everything. I could go to basically any Ph.d defense and understand what was going on. No more, the field has just exploded. And the impact? I mean you have people who do bio computation, for example, and you have to understand a lot of biology in order to understand how computer science applies to that. So that's the excitement. The excitement of having computer science have this broad impact. The other thing that's exciting is to see more women, more people of color, coming into the field, really injecting new energy and new perspective into the field and I think that will stand the discipline well in the future. >> And open source has been growing. I mean if you think about what it's like now to write software, all this goodness coming in with open source, it just adds over the top. >> Yeah. >> More goodness. >> I think today a, even a young undergraduate, writing in Python, using all these open libraries, could write more code in two weeks than I could have written in a year when I was graduate student. >> If we were 21 together, sitting here you and I, today, we're 21 years old, what would we do? What would you do? >> Well I think the opportunity created by the rise of machine learning and artificial intelligence is just unrivaled. This is a technology which we have invested in for 50 or 60 years, that was disappointing us for 50 or 60 years, in terms of not meeting it's projections and then, all of a sudden, turning point. It was a radical breakthrough and we're still at the very beginning of that radical breakthrough so I think it's going to be a really exciting time. >> Diane Green had a great quote at her last Google Cloud conference. She said, "It's like butter, everything's great with it." (laughing) AI is the-- >> Yeah, it's great with it. And of course, it can be overstated but I think there really is a fundamental breakthrough in terms of how we use the technology. Driven, of course, by the amount of data available for training these neural networks and far more computational resources than we ever thought we'd have. >> John it's been a great pleasure. Thanks for spending the time with us here for our People First interview, appreciate it. >> My pleasure, John. >> I'm John Furrier with theCUBE, we are here in Sand Hill Road for the People First program, thanks for watching. (upbeat techno music)

>> [Narrator] Live from Boston Massachusetts. This is the Cube, covering Sparks Summit East 2017. Brought to you by Databricks. Now here are your hosts, Dave Alante and George Gilbert. >> Back to you Boston everybody. This is the Cube and we're here live at Spark Summit East, #SparkSummit. Ziya Ma is here. She's the Vice President of Big Data at Intel. Ziya, thanks for coming to the Cube. >> Thanks for having me. >> You're welcome. So software is our topic. Software at Intel. You know people don't necessarily associate Intel with always with software but what's the story there? >> So actually there are many things that we do for software. Since I manage the Big Data engineering organization so I'll just say a little bit more about what we do for Big Data. >> [Dave] Great. >> So you know Intel do all the processors, all the hardware. But when our customers are using the hardware, they like to get the best performance out of Intel hardware. So this is for the Big Data space. We optimize the Big Data solution stack, including Spark and Hadoop on top of Intel hardware. And make sure that we leverage the latest instructions set so that the customers get the most performance out of the newest released Intel hardware. And also we collaborated very extensively with the open source community for Big Data ecosystem advancement. For example we're a leading contributor to Apache Spark ecosystem. We're also a top contributor to Apache Hadoop ecosystem. And lately we're getting into the machine learning and deep learning and the AI space, especially integrating those capabilities into the Big Data eTcosystem. >> So I have to ask you a question to just sort of strategically, if we go back several years, you look at during the Unix days, you had a number of players developing hardware, microprocessors, there were risk-based systems, remember MIPS and of course IBM had one and Sun, et cetera, et cetera. Some of those live on but very, very small portion of the market. So Intel has dominated the general purpose market. So as Big Data became more mainstream, was there a discussion okay, we have to develop specialized processors, which I know Intel can do as well, or did you say, okay, we can actually optimize through software. Was that how you got here? Or am I understanding that? >> We believe definitely software optimization, optimizing through software is one thing that we do. That's why Intel actually have, you may not know this, Intel has one of the largest software divisions that focus on enabling and optimizing the solutions in Intel hardware. And of course we also have very aggressive product roadmap for advancing continuously our hardware products. And actually, you mentioned a general purpose computing. CPU today, in the Big Data market, still has more than 95% of the market. So that's still the biggest portion of the Big Data market. And will continue our advancement in that area. And obviously as the Ai and machine learning, deep learning use cases getting added into the Big Data domain and we are expanding our product portfolio into some other Silicon products. >> And of course that was kind of the big bet of, we want to bet on Intel. And I guess, I guess-- >> You should still do. >> And still do. And I guess, at the time, Seagate or other disk mounts. Now flash comes in. And of course now Spark with memory, it's really changing the game, isn't it? What does that mean for you and the software group? >> Right, so what do we... Actually, still we focus on the optimi-- Obviously at the hardware level, like Intel now, is not just offering the computing capability. We also offer very powerful network capability. We offer very good memory solutions, memory hardware. Like we keep talking about this non-volatile memory technologies. So for Big Data, we're trying to leverage all those newest hardware. And we're already working with many of our customers to help them, to improve their Big Data memory solution, the e-memory, analytics type of capability on Intel hardware, give them the most optimum performance and most secure result using Intel hardware. So that's definitely one thing that we continue to do. That's going to be our still our top priority. But we don't just limit our work to optimization. Because giving user the best experience, giving user the complete experience on Intel platform is our ultimate goal. So we work with our customers from financial services company. We work with folks from manufacturing. From transportation. And from other IOT internet of things segment. And to make sure that we give them the easiest Big Data analytics experience on Intel hardware. So when they are running those solutions they don't have to worry too much about how to make their application work with Intel hardware, and how to make it more performant with Intel hardware. Because that's the Intel software solution that's going to bridge the gap. We do that part of the job. And so that it will make our customers experience easier and more complete. >> You serve as the accelerant to the marketplace. Go ahead George. >> [Ziya] That's right. >> So Intel's big ML as the news product, as of the last month of so, open source solution. Tell us how there are other deep learning frameworks that aren't as fully integrated with Spark yet and where BigML fits in since we're at a Spark conference. How it backfills some functionality and how it really takes advantage of Intel hardware. >> George, just like you said, BigDL, we just open sourced a month ago. It's a deep learning framework that we organically built onto of Apache Spark. And it has quite some differences from the other mainstream deep learning frameworks like Caffe, Tensorflow, Torch and Tianu are you name it. The reason that we decide to work on this project was again, through our experience, working with our analytics, especially Big Data analytic customers, as they build their AI solutions or AI modules within their analytics application, it's funny, it's getting more and more difficult to build and integrate AI capability into their existing Big Data analytics ecosystem. They had to set up a different cluster and build a different set of AI capabilities using, let's say, one of the deep learning frameworks. And later they have to overcome a lot of challenges, for example, moving the model and data between the two different clusters and then make sure that AI result is getting integrated into the existing analytics platform or analytics application. So that was the primary driver. How do we make our customers experience easier? Do they have to leave their existing infrastructure and build a separate AI module? And can we do something organic on top of the existing Big Data platform, let's say Apache Spark? Can we just do something like that? So that the user can just leverage the existing infrastructure and make it a naturally integral part of the overall analytics ecosystem that they already have. So this was the primary driver. And also the other benefit that we see by integrating this BigDL framework naturally was the Big Data platform, is that it enables efficient scale-out and fault tolerance and elasticity and dynamic resource management. And those are the benefits that's on naturally brought by Big Data platform. And today, actually, just with this short period of time, we have already tested that BigDL can scale easily to tens or hundreds of nodes. So the scalability is also quite good. And another benefit with solution like BigDL, especially because it eliminates the need of setting a separate cluster and moving the model between different hardware clusters, you save your total cost of ownership. You can just leverage your existing infrastructure. There is no need to buy additional set of hardware and build another environment just for training the model. So that's another benefit that we see. And performance-wise, again we also tested BigDL with Caffe, Torch and TensorFlow. So the performance of BigDL on single node Xeon is orders of magnitude faster than out of box at open source Caffe, TensorFlow or Torch. So it definitely it's going to be very promising. >> Without the heavy lifting. >> And useful solution, yeah. >> Okay, can you talk about some of the use cases that you expect to see from your partners and your customers. >> Actually very good question. You know we already started a few engagement with some of the interested customers. The first customer is from Stuart Industry. Where improving the accuracy for steel-surface defect recognition is very important to it's quality control. So we worked with this customer in the last few months and built end-to-end image recognition pipeline using BigDL and Spark. And the customer just through phase one work, already improved it's defect recognition accuracy to 90%. And they're seeing a very yield improvement with steel production. >> And it used to by human? >> It used to be done by human, yes. >> And you said, what was the degree of improvement? >> 90, nine, zero. So now the accuracy is up to 90%. And another use case and financial services actually, is another use case, especially for fraud detection. So this customer, again I'm not at the customer's request, they're very sensitive the financial industry, they're very sensitive with releasing their name. So the customer, we're seeing is fraud risks were increasing tremendously. With it's wide range of products, services and customer interaction channels. So the implemented end-to-end deep learning solution using BigDL and Spark. And again, through phase one work, they are seeing the fraud detection rate improved 40 times, four, zero times. Through phase one work. We think there were more improvement that we can do because this is just a collaboration in the last few month. And we'll continue this collaboration with this customer. And we expect more use cases from other business segments. But that are the two that's already have BigDL running in production today. >> Well so the first, that's amazing. Essentially replacing the human, have to interact and be much more accurate. The fraud detection, is interesting because fraud detection has come a long way in the last 10 years as you know. Used to take six months, if they found fraud. And now it's minutes, seconds but there's a lot of false positives still. So do you see this technology helping address that problem? >> Yeah, we actually that's continuously improving the prediction accuracy is one of the goals. This is another reason why we need to bring AI and Big Data together. Because you need to train your model. You need to train your AI capabilities with more and more training data. So that you get much more improved training accuracy. Actually this is the biggest way of improving your training accuracy. So you need a huge infrastructure, a big data platform so that you can host and well manage your training data sets. And so that it can feed into your deep learning solution or module for continuously improving your training accuracy. So yes. >> This is a really key point it seems like. I would like to unpack that a little bit. So when we talk to customers and application vendors, it's that training feedback loop that gets the models smarter and smarter. So if you had one cluster for training that was with another framework, and then Spark was your... Rest of your analytics. How would training with feedback data work when you had two separate environments? >> You know that's one of the drivers why we're creating BigDL. Because, we tried to port, we did not come to BigDL at the very beginning. We tried to port the existing deep learning frameworks like Caffe and Tensorflow onto Spark. And you also probably saw some research papers folks. There's other teams that out there that's also trying to port Caffe, Tensorflow and other deep learning framework that's out there onto Spark. Because you have that need. You need to bring the two capabilities together. But the problem is that those systems were developed in a very traditional way. With Big Data, not yet in consideration, when those frameworks were created, were innovated. But now the need for converging the two becomes more and more clear, and more necessary. And that's we way, when we port it over, we said gosh, this is so difficult. First it's very challenging to integrate the two. And secondly the experience, after you've moved it over, is awkward. You're literally using Spark as a dispatcher. The integration is not coherent. It's like they're superficially integrated. So this is where we said, we got to do something different. We can not just superficially integrate two systems together. Can we do something organic on top of the Big Data platform, on top of Apache Spark? So that the integration between the training system, between the feature engineering, between data management can &be more consistent, can be more integrated. So that's exactly the driver for this work. >> That's huge. Seamless integration is one of the most overused phrases in the technology business. Superficial integration is maybe a better description for a lot of those so-called seamless integrations. You're claiming here that it's seamless integration. We're out of time but last word Intel and Spark Summit. What do you guys got going here? What's the vibe like? >> So actually tomorrow I have a keynote. I'm going to talk a little bit more about what we're doing with BigDL. Actually this is one of the big things that we're doing. And of course, in order for BigDL, system like BigDL or even other deep learning frameworks, to get optimum performance on Intel hardware, there's another item that we're highlighting at MKL, Intel optimized Math Kernel Library. It has a lot of common math routines. That's optimized for Intel processor using the latest instruction set. And that's already, today, integrated into the BigDL ecosystem.z6 So that's another thing that we're highlighting. And another thing is that those are just software. And at hardware level, during November, Intel's AI day, our executives from BK, Diane Bryant and Doug Fisher. They also highlighted the Nirvana product portfolio that's coming out. That will give you different hardware choices for AI. You can look at FPGA, Xeon Fi, Xeon and our new Nirvana based Silicon like Crestlake. And those are some good silicon products that you can expect in the future. Intel, taking us to Nirvana, touching every part of the ecosystem. Like you said, 95% share and in all parts of the business. Yeah, thanks very much for coming the Cube. >> Thank you, thank you for having me. >> You're welcome. Alright keep it right there. George and I will be back with our next guest. This is Spark Summit, #SparkSummit. We're the Cube. We'll be right back.