>>from around the globe. It's the Cube presenting innovation for good, brought to you by on shape. >>Hello, everyone, and welcome to Innovation for Good Program, hosted by the Cuban. Brought to You by on Shape, which is a PTC company. My name is Dave Valentin. I'm coming to you from our studios outside of Boston. I'll be directing the conversations today. It's a very exciting, all live program. We're gonna look at how product innovation has evolved, where it's going and how engineers, entrepreneurs and educators are applying cutting edge, cutting edge product development techniques and technology to change our world. You know, the pandemic is, of course, profoundly impacted society and altered how individuals and organizations they're gonna be thinking about and approaching the coming decade. Leading technologists, engineers, product developers and educators have responded to the new challenges that we're facing from creating lifesaving products to helping students learn from home toe how to apply the latest product development techniques and solve the world's hardest problems. And in this program, you'll hear from some of the world's leading experts and practitioners on how product development and continuous innovation has evolved, how it's being applied toe positive, positively affect society and importantly where it's going in the coming decades. So let's get started with our first session fueling Tech for good. And with me is John Herstek, who is the president of the Suffers, a service division of PTC, which required on shape just over a year ago, where John was the CEO and co founder. And Dana Grayson is here. She is the co founder and general partner at Construct Capital, a new venture capital firm. Folks, welcome to the program. Thanks so much for coming on. Great >>to be here, Dave. >>All right, John. You're very welcome. Dana. Look, John, let's get into it for first. A belated congratulations on the acquisition of Von Shape. That was an awesome seven year journey for your company. Tell our audience a little bit about the story of on shape, but take us back to Day zero. Why did you and your co founders start on shape? Well, >>actually, start before on shaping the You know, David, I've been in this business for almost 40 years. The business of building software tools for product developers and I had been part of some previous products in the industry and companies that had been in their era. Big changes in this market and about, you know, a little Before founding on shape, we started to see the problems product development teams were having with the traditional tools of that era years ago, and we saw the opportunity presented by Cloud Web and Mobile Technology. And we said, Hey, we could use Cloud Web and Mobile to solve the problems of product developers make their Their business is run better. But we have to build an entirely new system, an entirely new company, to do it. And that's what on shapes about. >>Well, so notwithstanding the challenges of co vid and difficulties this year, how is the first year been as, Ah, division of PTC for you guys? How's business? Anything you can share with us? >>Yeah, our first year of PTC has been awesome. It's been, you know, when you get acquired, Dave, you never You know, you have great optimism, but you never know what life will really be like. It's sort of like getting married or something, you know, until you're really doing it, you don't know. And so I'm happy to say that one year into our acquisition, a TPI TC on shape is thriving. It's worked out better than I could have imagined a year ago. Along always, I mean sales are up. In Q four, our new sales rate grew 80% vs Excuse me, our fiscal Q four Q three. In the calendar year, it grew 80% compared to the year before. Our educational uses skyrocketing with around 400% growth, most recently year to year of students and teachers and co vid. And we've launched a major cloud platform using the core of on shape technology called Atlas. So, um, just tons of exciting things going on a TTC. >>That's awesome. But thank you for sharing some of those metrics. And of course, you're very humble individual. You know, people should know a little bit more about you mentioned, you know, we founded solid works, go founded solid, where I actually found it solid works. You had a great exit in the late nineties. But what I really appreciate is, you know, you're an entrepreneur. You've got a passion for the babies that you helped birth. You stayed with the salt systems for a number of years. The company that quiet, solid works well over a decade. And and, of course, you and I have talked about how you participated in the M I t blackjack team. You know, back in the day a Z I say you're very understated, for somebody was so accomplished. So well, >>that's kind of you. But I tend to I tend Thio always keep my eye more on what's ahead. You know what's next then? And you know, I look back Sure to enjoy it and learn from it about what I can put toe work, making new memories, making new successes. >>I love it. Okay, let's bring Dana into the conversation. Hello, Dana. And you Look, you were fairly early investor in on shape when you were with any A. And I think it was like it was a Siri's B. But it was very right close after the A raise. And and you were and still are a big believer in industrial transformation. So take us back. What did you see about on shape back then? That that excited you? >>Thanks. Thanks for that. Yeah. I was lucky to be a early investment in shape. You know, the things that actually attracted me. Don shape were largely around John and, uh, the team. They're really setting out to do something, as John says humbly, something totally new, but really building off of their background was a large part of it. Um, but, you know, I was really intrigued by the design collaboration side of the product. Um, I would say that's frankly what originally attracted me to it. What kept me in the room, you know, in terms of the industrial world was seeing just if you start with collaboration around design what that does to the overall industrial product lifecycle accelerating manufacturing just, you know, modernizing, manufacturing, just starting with design. So I'm really thankful to the on shape guys, because it was one of the first investments I've made that turned me on to the whole sector. And, wow, just such a great pleasure to work with with John and the whole team there. Now see what they're doing inside PTC, >>and you just launched construct capital this year, right in the middle of a pandemic and which is awesome. I love it. And you're focused on early stage investing. Maybe tell us a little bit about construct capital. What? Your investment thesis is and you know, one of the big waves that you're hoping to ride. >>Sure, it construct it is literally lifting out of any what I was doing there, um uh, you're on shape. I went on to invest in companies such as desktop metal and Tulip, to name a couple of them form labs, another one in and around the manufacturing space. But our thesis it construct is broader than just, you know, manufacturing and industrial. It really incorporates all of what we'd call foundational industries that have let yet to be fully tech enabled or digitized. Manufacturing is a big piece of it. Supply chain, logistics, transportation and mobility or not, or other big pieces of it. And together they really drive, you know, half of the GDP in the US and have been very under invested. And frankly, they haven't attracted really great founders like Iran in droves. And I think that's going to change. We're seeing, um, entrepreneurs coming out of the tech world or staggolee into these industries and then bringing them back into the tech world, which is which is something that needs to happen. So John and team were certainly early pioneers and I think, you know, frankly, obviously, that voting with my feet that the next set, a really strong companies are going to come out of this space over the next decade. >>I think there's a huge opportunity to digitize the sort of traditionally non digital organizations. But Dana, you focused. I think it's it's accurate to say you're focused on even Mawr early stage investing now. And I want to understand why you feel it's important to be early. I mean, it's obviously riskier and reward e er, but what do you look for in companies and and founders like John >>Mhm, Um, you know, I think they're different styles of investing all the way up to public market investing. I've always been early stage investors, so I like to work with founders and teams when they're, you know, just starting out. Um, I happened to also think that we were just really early in the whole digital transformation of this world. You know, John and team have been, you know, back from solid works, etcetera around the space for a long time. But again, the downstream impact of what they're doing really changes the whole industry and and so we're pretty early and in digitally transforming that market. Um, so that's another reason why I wanna invest early now, because I do really firmly believe that the next set of strong companies and strong returns for my own investors will be in the spaces. Um, you know, what I look for in Founders are people that really see the world in a different way. And, you know, sometimes some people think of founders or entrepreneurs is being very risk seeking. You know, if you asked John probably and another successful entrepreneurs, they would call themselves sort of risk averse, because by the time they start the company, they really have isolated all the risk out of it and think that they have given their expertise or what they're seeing their just so compelled to go change something, eh? So I look for that type of attitude experience a Z. You can also tell from John. He's fairly humble. So humility and just focus is also really important. Um, that there's a that's a lot of it. Frankly, >>excellent. Thank you. And John, you got such a rich history in the space in one of you could sort of connect the dots over time. I mean, when you look back, what were the major forces that you saw in the market in in the early days? Uh, particularly days of on shape on how is that evolved? And what are you seeing today? Well, I >>think I touched on it earlier. Actually, could I just reflect on what Dana said about risk taking for just a quick one and say, throughout my life, from blackjack to starting solid works on shape, it's about taking calculated risks. Yes, you try to eliminate the risk sa's much as you can, but I always say, I don't mind taking a risk that I'm aware of, and I've calculated through as best I can. I don't like taking risks that I don't know I'm taking. >>That's right. You like to bet on >>sure things as much sure things, or at least where you feel you. You've done the research and you see them and you know they're there and you know, you, you you keep that in mind in the room, and I think that's great. And Dana did so much for us. Dana, I want to thank you again for all that you did it every step of the way from where we started. Thio, Thio You know your journey with us ended formally but continues informally. Now back to you. Um, Dave, I think question about the opportunity and how it's shaped up. Well, I think I touched on it earlier when I said It's about helping product developers. You know, our customers of the people build the future of manufactured goods. Anything you think of that would be manufacturing factory. You know, the chair you're sitting in machine that made your coffee. You know, the computer you're using that trucks that drive by on the street, all the covert product research, the equipment being used to make vaccines. All that stuff is designed by someone, and our job is given the tools to do it better. And I could see the problems that those product developers had that we're slowing them down with using the computing systems of the time. When we built solid works, that was almost 30 years ago. People don't realize that it was in the early >>nineties, and, you know, we did the >>best we could for the early nineties, but what we did, we didn't anticipate the world of today. And so people were having problems with just installing the systems. Dave, you wouldn't believe how hard it is to install these systems. You need a spec up a special windows computer, you know, and make sure you've got all the memory and graphics you need and getting to get that set up. You need to make sure the device drivers air, right, install a big piece of software. Ah, license key. I'm not making this up. They're still around. You may not even know what those are. You know, Dennis laughing because, you know, zero cool people do things like this anymore on but only runs some windows. You want a second user to use it? They need a copy. They need a code. Are they on the same version? It's a nightmare. The teams change. You know? You just say, Well, get everyone on the software. Well, who's everyone? You know? You got a new vendor today? A new customer tomorrow, a new employee. People come on and off the team. The other problem is the data stored in files, thousands of files. This isn't like a spreadsheet or word processor where there's one file to pass around these air thousands of files to make one, even a simple product. People were tearing their hair out. John, what do we do? I've got copies everywhere. I don't know where the latest version is. We tried like, you know, locking people out so that only one person can change it at the time that works against speed. It works against innovation. We saw what was happening with Cloud Web and mobile. So what's happened in the years since is every one of the forces that product developers experience the need for speed, the need for innovation, the need to be more efficient with their people in their capital. Resource is every one of those trends have been amplified since we started on shape by a lot of forces in the world. And covert is amplified all those the need for agility and remote work cove it is amplified all that the same time, The acceptance of cloud. You know, a few years ago, people were like cloud, you know, how is that gonna work now They're saying to me, you know, increasingly, how would you ever even have done this without the cloud? How do you make solid works Work without the cloud? How would that even happen? You know, And once people understand what on shapes about >>and we're the >>Onley full SAS solution software as a service, full SAS solution in our industry. So what's happened in those years? Same problems we saw earlier, but turn up the gain, their bigger problems. And with cloud, we've seen skepticism of years ago turn into acceptance. And now even embracement in the cova driven new normal. >>Yeah. So a lot of friction in the previous environments cloud obviously a huge factor on, I guess. I guess Dana John could see it coming, you know, in the early days of solid works with Salesforce, which is kind of the first major independent SAS player. Well, I guess that was late nineties. So it was post solid works, but pre in shape and their work day was, you know, pre on shape in the mid two thousands. And and but But, you know, the bet was on the SAS model was right for Crick had and and product development, you know, which Maybe the time wasn't a no brainer. Or maybe it was I don't know, but Dana is there. Is there anything that you would invest in today that's not Cloud based? >>Um, that's a great question. I mean, I think we still see things all the time in the manufacturing world that are not cloud based. I think you know, the closer you get to the shop floor in the production environment. Um, e think John and the PTC folks would agree with this, too, but that it's, you know, there's reliability requirements. There's performance requirements. There's still this attitude of, you know, don't touch the printing press. So the cloud is still a little bit scary sometimes. And I think hybrid cloud is a real thing for those or on premise. Solutions, in some cases is still a real thing. What, what were more focused on. And, um, despite whether it's on premise or hybrid or or SAS and Cloud is a frictionless go to market model, um, in the companies we invest in so sass and cloud, or really make that easy to adopt for new users, you know, you sign up, start using a product, um, but whether it's hosted in the cloud, whether it's as you can still distribute buying power. And, um, I would I'm just encouraging customers in the customer world and the more industrial environment to entrust some of their lower level engineers with more budget discretionary spending so they can try more products and unlock innovation. >>Right? The unit economics are so compelling. So let's bring it, you know, toe today's you know, situation. John, you decided to exit about a year ago. You know? What did you see in PTC? Other than the obvious money? What was the strategic fit? >>Yeah, Well, David, I wanna be clear. I didn't exit anything. Really? You >>know, I love you and I don't like that term exit. I >>mean, Dana had exit is a shareholder on and so it's not It's not exit for me. It's just a step in the journey. Um, what we saw in PTC was a partner. First of all, that shared our vision from the top down at PTC. Jim Hempleman, the CEO. He had a great vision for for the impact that SAS can make based on cloud technology. And really is Dana of highlighted so much. It's not just the technology is how you go to market and the whole business being run and how you support and make the customers successful. So Jim shared a vision for the potential. And really, really, um said Hey, come join us and we can do this bigger, Better, faster. We expanded the vision really to include this Atlas platform for hosting other SAS applications. That P D. C. I mean, David Day arrived at PTC. I met the head of the academic program. He came over to me and I said, You know, and and how many people on your team? I thought he'd say 5 40 people on the PTC academic team. It was amazing to me because, you know, we were we were just near about 100 people were required are total company. We didn't even have a dedicated academic team and we had ah, lot of students signing up, you know, thousands and thousands. Well, now we have hundreds of thousands of students were approaching a million users, and that shows you the power of this team that PTC had combined with our product and technology whom you get a big success for us and for the teachers and students to the world. We're giving them great tools. So so many good things were also putting some PTC technology from other parts of PTC back into on shape. One area, a little spoiler, little sneak peek. Working on taking generative design. Dana knows all about generative design. We couldn't acquire that technology were start up, you know, just to too much to do. But PTC owns one of the best in the business. This frustrated technology we're working on putting that into on shaping our customers. Um, will be happy to see it, hopefully in the coming year sometime. >>It's great to see that two way exchange. Now, you both know very well when you start a company, of course, a very exciting time. You know, a lot of baggage, you know, our customers pulling you in a lot of different directions and asking you for specials. You have this kind of clean slate, so to speak in it. I would think in many ways, John, despite you know, your install base, you have a bit of that dynamic occurring today especially, you know, driven by the forced march to digital transformation that cove it caused. So when you sit down with the team PTC and talk strategy, you now have more global resource is you got cohorts selling opportunities. What's the conversation like in terms of where you want to take the division? >>Well, Dave, you actually you sounds like we should have you coming in and talking about strategy because you've got the strategy down. I mean, we're doing everything said global expansion were able to reach across selling. We've got some excellent PTC customers that we can reach reach now and they're finding uses for on shape. I think the plan is to, you know, just go, go, go and grow, grow, grow where we're looking for this year, priorities are expand the product. I mentioned the breath of the product with new things PTC did recently. Another technology that they acquired for on shape. We did an acquisition. It was it was small, wasn't widely announced. It, um, in an area related to interfacing with electrical cad systems. So? So we're doing We're expanding the breath of on shape. We're going Maura. Depth in the areas were already in. We have enormous opportunity. Add more features and functions that's in the product. Go to market. You mentioned it global global presence. That's something we were a little light on a year ago. Now we have a team. Dana may not even know what we have a non shape, dedicated team in Barcelona, based in Barcelona but throughout Europe were doing multiple languages. Um, the academic program just introduced a new product into that space. That's that's even fueling more success and growth there, Um, and of course, continuing to to invest in customer success. And this Atlas platform story I keep mentioning, we're going to soon have We're gonna soon have four other major PTC brands shipping products on our Atlas Saas platform. And so we're really excited about that. That's good for the other PTC products. It's also good for on shape because now there's there's. There's other interesting products that are on shape customers can use take advantage of very easily using, say, a common log in conventions about user experience there used to invest of all their SAS based, so they that makes it easier to begin with. So that's some of the exciting things going on. I think you'll see P. D. C. Um expanding our lead in saas based applications for this sector for our target sectors, not just in in cat and data management. But another area, PTC's Big and his augmented reality with of euphoria, product line leader and industrial uses of a R. That's a whole other story we should do. A whole nother show augmented reality. But these products are amazing. You can You can help factory workers people on, uh, people who are left out of the digital transformation. Sometimes we're standing from machine >>all day. >>They can't be sitting like we are doing Zoom. They could wear a R headset in our tools. Let them create great content. This is an area Dana is invested in in other companies, but what I wanted to note is the new releases of our authoring software. For this, our content getting released this month, used through the Atlas platform, the SAS components of on shape for things like revision management and collaboration on duh workflow activity. All that those are tools that we're able to share leverage. We get a lot of synergy. It's just really good. It's really fun to We'll have a good time, >>that's awesome. And then we're gonna be talking to John MacLean later about Atlas and do a little deeper dive on that. And, Dana, what is your involvement today with with on shape? But you're looking for you know, which of their customers air actually adopting, and they're gonna disrupt their industries. You get good pipeline from that. How do you collaborate today? >>That sounds like a great idea. Um, a Z John will tell you I'm constantly just ask him for advice and impressions of other entrepreneurs and picking his brain on ideas. No formal relationship clearly, but continue to count John and and John and other people in on shaping in the circle of experts that I rely on for their opinions. >>All right, so we have some questions from the crowd here. Uh, one of the questions is for the dream team. You know, John and Dana. What's your next next collective venture? I don't think we're there yet, are we? No. I >>just say, as Dana said, we love talking to her about. You know, Dana, you just returned the compliment. We would try and give you advice and the deals you're looking at, and I'm sort of casually mentoring at least one of your portfolio entrepreneurs, and that's been a lot of fun for May on hopefully a value to them. But also Dana, We uran important pipeline to us in the world of some new things that are happening that we wouldn't see if you know you've shown us some things that you've said. What do you think of this business? And for us, it's like, Wow, it's cool to see that's going on And that's what's supposed to work in an ecosystem like this. So we we deeply value the ongoing relationship. And no, we're not starting something new. I got a lot of work left to do with what I'm doing and really happy. But we can We can collaborate in this way on other ventures. >>I like this question to somebody asking with the cloud options like on shape, Wilmore students have stem opportunities s Oh, that's a great question. Are you because of sass and cloud? Are you able to reach? You know, more students? Much more cost effectively. >>Yeah, Dave, I'm so glad that that that I was asked about this because Yes, and it's extremely gratifying us. Yes, we are because of cloud, because on shape is the only full cloud full SAS system. Our industry were able to reach stem education brings able to be part of bringing step education to students who couldn't get it otherwise. And one of most gratifying gratifying things to me is the emails were getting from teachers, um, that that really, um, on the phone calls that were they really pour their heart out and say We're able to get to students in areas that have very limited compute resource is that don't have an I T staff where they don't know what computer that the students can have at home, and they probably don't even have a computer. We're talking about being able to teach them on a phone to have an android phone a low end android phone. You could do three D modeling on there with on shape. Now you can't do it any other system, but with on shape, you could do it. And so the teacher can say to the students, They have to have Internet access, and I know there's a huge community that doesn't even have Internet access, and we're not able, unfortunately to help that. But if you have Internet and you have even an android phone, we can enable the educator to teach them. And so we have case after case of saving a stem program or expanding it into the students that need it most is the ones we're helping here. So really excited about that. And we're also able to let in addition to the run on run on whatever computing devices they have, we also offer them the tools they need for remote teaching with a much richer experience. You know, could you teach solid works remotely? Well, maybe if the student ran it had a windows workstation, you know, big, big, high and workstation. Maybe it could, but it would be like the difference between collaborating with on shape and collaborate with solid works. Like the difference between a zoom video call and talking on the landline phone. You know, it's a much richer experience, and that's what you need in stem teaching. Stem is hard. So, yeah, we're super super excited about bringing stem to more students because of clouds. >>Yeah, we're talking about innovation for good, and then the discussion, John, you just had it. Really? There could be a whole another vector here. We could discuss on diversity, and I wanna end with just pointing out So, Dana, your new firm. It's a woman led firm, too. Two women leaders, you know, going forward. So that's awesome to see, so really? Yeah, thumbs up on that. Congratulations on getting that off the ground. Yeah. Thank you. Okay. So thank you guys. Really appreciate It was a great discussion. I learned a lot, and I'm sure the audience did a swell in a moment. We're gonna talk with on shape customers to see how they're applying tech for good and some of the products that they're building. So keep it right there. I'm Dave Volonte. You're watching innovation for good on the Cube, the global leader in digital tech event coverage. Stay right there. Yeah.

>>from around the globe. It's the Cube presenting innovation for good. Brought to you by on shape. >>Okay, we're back. This is Dave Volonte and you're watching innovation for good. A program on Cuba 3 65 made possible by on shape of BTC company. We're live today really live TV, which is the heritage of the Cuban. Now we're gonna go to the sources and talkto on shape customers to find out how they're applying technology to create real world innovations that are changing the world. So let me introduce our panel members. Rafael Gomez Fribourg is with the Chan Zuckerberg bio hub. A very big idea. And collaborative nonprofit was initiative that was funded by Mark Zuckerberg and his wife, Priscilla Chan, and really around diagnosing and curing and better managing infectious diseases. So really timely topic. Philip Tabor is also joining us. He's with silver side detectors which develops neutron detective detection systems. Yet you want to know if early if neutrons and radiation or in places where you don't want them, so this should be really interesting. And last but not least, Matthew Shields is with the Charlottesville schools and is gonna educate us on how he and his team are educating students in the use of modern engineering tools and techniques. Gentlemen, welcome to the Cuban to the program. This should be really interesting. Thanks for coming on. >>Hi. Or pleasure >>for having us. >>You're very welcome. Okay, let me ask each of you because you're all doing such interesting and compelling work. Let's start with Rafael. Tell us more about the bio hub and your role there, please. >>Okay. Yes. As you said, the Bio Hope is a nonprofit research institution, um, funded by Mark Zuckerberg and his wife, Priscilla Chan. Um and our main mission is to develop new technologies to help advance medicine and help, hopefully cure and manage diseases. Um, we also have very close collaborations with Universe California, San Francisco, Stanford University and the University California Berkeley on. We tried to bring those universities together, so they collaborate more of biomedical topics. And I manage a team of engineers in by joining platform. Um, and we're tasked with creating instruments for the laboratory to help the scientist boats inside the organization and also in the partner universities do their experiments in better ways in ways that they couldn't do before >>in this edition was launched five years ago. It >>was announced at the end of 2016, and we actually started operations in the beginning of 2017, which is when I joined um, so this is our third year. >>And how's how's it going? How does it work? I mean, these things >>take time. It's been a fantastic experience. Uh, the organization works beautifully. Um, it was amazing to see it grow from the beginning. I was employee number 12, I think eso When I came in, it was just a nem p off his building and MP labs. And very quickly we had something running about from anything. Eso I'm very proud of the work that we have done to make that possible. Um And then, of course, that's you mentioned now, with co vid, um, we've been able to do a lot of very cool work, um, very being of the pandemic In March, when there was a deficit of testing, uh, capacity in California, we spun up a testing laboratory in record time in about a week. It was crazy. It was a crazy project. Um, but but incredibly satisfying. And we ended up running all the way until the beginning of November, when the lab was finally shut down, we could process about 3000 samples a day. I think at the end of it all, we were able to test about 100 on the road, 150,000 samples from all over the state. We were providing free testing toe all of the Department of Public Health Department of Public Health in California, which, at the media pandemic, had no way to do testing affordably and fast. So I think that was a great service to the state. Now the state has created a testing system that will serve those departments. So then we decided that it was unnecessary to keep going with testing in the other biopsy that would shut down, >>right? Thank you for that. Now, Now, Philip, you What you do is mind melting. You basically helped keep the world safe. Maybe you describe a little bit more about silver side detectors and what your role is there and how it all works. >>Tour. So we make a nuclear bomb detectors and we also make water detectors. So we try and do our part. Thio Keep the world from blowing up and make it a better place at the same time. Both of these applications use neutron radiation detectors. That's what we make. Put them out by a port border crossing Places like that they can help make sure that people aren't smuggling, shall we say, very bad things. Um, there's also a burgeoning field of research and application where you can use neutrons with some pretty cool physics to find water so you can do things like but a detector up in the mountains and measure snowpack. Put it out in the middle of the field and measure soil moisture content. And as you might imagine, there's some really cool applications in, uh, research and agronomy and public policy for this. >>All right, so it's OK, so it's It's much more than you know, whatever fighting terrorism, it's there's a riel edge, or I kind of i o t application for what you guys do. >>You do both Zito shares. You might >>say a mat. I I look at your role is kind of scaling the brain power for for the future. Maybe tell us more about Charlottesville schools and in the mission that you're pursuing and what you do. >>Thank you. Um, I've been in Charlottesville city schools for about 11 or 12 years. I started their teaching, Um, a handful of classes, math and science and things like that. But Thescore board and my administration had the crazy idea of starting an engineering program about seven years ago. My background is an engineering is an engineering. My masters is in mechanical and aerospace engineering. And, um, I basically spent a summer kind of coming up with what might be a fun engineering curriculum for our students. And it started with just me and 30 students about seven years ago, Um, kind of a home spun from scratch curriculum. One of my goals from the outside was to be a completely project based curriculum, and it's now grown. We probably have about six or 700 students, five or six full time teachers. We now have pre engineering going on at the 5th and 6th grade level. I now have students graduating. Uh, you know, graduating after senior year with, like, seven years of engineering under their belt and heading off to doing some pretty cool stuff. So it's It's been a lot of fun building up a program and, um, and learning a lot in the process. >>That's awesome. I mean, you know, Cuba's. We've been passionate about things like women in tech, uh, diversity stem. You know, not only do we need more more students in stem, we need mawr underrepresented women, minorities, etcetera. We were just talking to John her stock and integrate Grayson about this is do you do you feel is though you're I mean, first of all, the work that you do is awesome, but but I'll go one step further. Do you feel as though it's reaching, um, or, you know, diverse base and And how is that going? >>That's a great question. I think research shows that a lot of people get funneled into one kind of track or career path or set of interests really early on in their educational career. And sometimes that that funnels kind of artificial. And so that's one of the reasons we keep pushing back. Um, so our school systems introducing kindergartners to programming on DSO. We're trying to push back how we expose students to engineering and to stem fields as early as possible, and we've definitely seen the fruits of that in my program. In fact, my engineering program, uh, sprung out of an after school in Extracurricular Science Club that actually three girls started at our school. So I think that actually has helped that three girls started the club That eventually is what led our engineering programs that sort of baked into the DNA and also are a big public school. And we have about 50% of the students are under the poverty line, and we should I mean, Charlottesville, which is a big refugee town. And so I've been adamant from Day one that there are no barriers to entry into the program. There's no test you have to take. You don't have to have be taking a certain level of math or anything like that. That's been a lot of fun. To have a really diverse set of kids and or the program and be successful, >>that's phenomenal. That's great to hear. So, Philip, I wanna come back to you. You know, I think about maybe some day we'll be able to go back to a sporting events, and I know when I when I'm in there, there's somebody up on the roof looking out for me, you know, watching the crowd. And they have my back. And I think in many ways, the products that you build, you know, our similar I may not know they're there, but they're keeping us safe or they're measuring things that that that I don't necessarily see. But I wonder if you could talk about a little bit more detail about the products you build and how they're impacting society. >>Sure, So there are certainly a lot of people who are who are watching, trying to make sure things were going well in keeping you safe that you may or may not be aware of. And we try and support ah lot of them. So we have detectors that are that are deployed in a variety of variety of uses with a number of agencies and governments that dio like I was saying, ports and border crossing some other interesting applications that are looking for looking for signals that should not be there and working closely to fit into the operations these folks do Onda. We also have ah lot of outreach to researchers and scientists trying to help them support the work they're doing, um, using neutron detection for soil moisture monitoring is a some really cool opportunities for doing it at large scale and with much less, um, expense or complication then would have been done previous technologies. Mhm. You know, they were talking about collaboration in the previous segment. We've been able to join a number of conferences for that, virtually including one that was supposed to be held in Boston. But another one that was held, uh, of the University of Heidelberg in Germany. And, uh, this is sort of things that in some ways, the pandemic is pushing people towards greater collaboration than there would have been able to do. Had it all but in person. >>Yeah, we did. Uh, the cube did live works a couple years ago in Boston. It was awesome show. And I think, you know, with this whole trend toward digit, I call it the forced march to digital. Thanks to cove it I think that's just gonna continue. Thio grow Raphael one. If you could describe the process that you used to better understand diseases and what's your organization's involvement? Been in more detail, addressing the cove in pandemic. >>Um, so so we have the bio be structured in, Um um, in a way that foster So the combination of technology and science. So we have to scientific tracks, one about infectious diseases and the other one about understanding just basic human biology how the human body functions and especially how the cells in the human body function on how they're organized to create teachers in the body. Um, and then it has the set of platforms. Um, mind is one of them by engineering that are all technology. Read it. So we have data science platform, all about data analysis, machine learning, things like that. Um, we have a mass spectrometry platform is all about mass spectrometry technologies to, um, exploit those ones in service for the scientists on. We have a genomics platform. That is all about sequencing DNA in our DNA. Um, and then an advanced microscopy. It's all about developing technologies, uh, to look at things with advanced microscopes and the little technologies to marry computation on microscope. So, um, the scientists said the agenda and the platforms we just serve their needs, support their needs, and hopefully develop technologies that help them do their experiments better, faster, or allow them to the experiment that they couldn't do in any other way before. Um And so with cove, it because we have that very strong group of scientists that work on. I have been working on infectious disease before, and especially in viruses, we've been able to very quickly pivot to working on that s O, for example, my team was able to build pretty quickly a machine to automatically purified proteins, and it's being used to purify all these different important proteins in the cove. It virus the SARS cov to virus on Dwyer, sending some of those purified proteins all over the world. Two scientists that are researching the virus and trying to figure out how to develop vaccines, understand how the virus affects the body and all that. So some of the machines we built are having a very direct impact on this. Um, Also for the copy testing lab, we were able to very quickly develop some very simple machines that allowed the lab to function sort of faster and more efficiently. Sort of had a little bit of automation in places where we couldn't find commercial machines that would do it. >>Um, God s o mat. I mean, you gotta be listening to this in thinking about, Okay? Some. Someday your students are gonna be working at organizations like Like like Bio Hub and Silver Side. And you know, a lot of young people that just have I don't know about you guys, but like my kids, they're really passionate about changing the world. You know, there's way more important than, you know, the financial angles and that z e I gotta believe you're seeing that you're right in the front lines there. >>Really? Um, in fact, when I started the curriculum six or seven years ago, one of the first bits of feedback I got from my students is they said Okay, this is a lot of fun. So I had my students designing projects and programming microcontrollers raspberry, PiS and order We nose and things like that. The first bit of feedback I got from students was they said Okay, when do we get to impact the world? I've heard engineering is about making the world a better place, and robots are fun and all, but, you know, where is the real impact? And so, um do Yeah, thanks to the guidance of my students, I'm baking that Maurin. Now I'm like Day one of engineering one. We talk about how the things that the tools they're learning and the skills they're gaining eventually you know, very soon could be could be used to make the world a better place. >>You know, we all probably heard that famous line By Jeff Hammond Barker. The greatest minds of my generation are trying to figure out how to get people to click on ads. E. I think we're really generally generationally finally, at the point where you know young students and engineering and really you know it passionate about affecting society. I wanna get into the product, you know, side and understand how each of you are using on shape and and the value that that it brings. Maybe Raphael, you could start how long you've been using it. You know, what's your experience with it? Let's let's start there. >>I begin for about two years, and I switched to it with some trepidation. You know, I was used to always using the traditional product that you have to install on your computer, that everybody uses that. So I was kind of locked into that, but I started being very frustrated with the way it worked, um, and decided to give on ship chance. Which reputation? Because any change always, you know, causes anxiety. But very quickly my engineers started loving it. Uh, just because it's it's first of all, the learning curve wasn't very difficult at all. You can transfer from one from the traditional product to entree very quickly and easily. You can learn all the concepts very, very fast. It has all the functionality that we needed, and and what's best is that it allows to do things that we couldn't do before or we couldn't do easily. Um, now we can access the our cat documents from anywhere in the world. Um, so when we're in the lab fabricating something or testing a machine, any computer we have next to us or a tablet or on iPhone, we can pull it up and look at the cad and check things or make changes that something that couldn't do before because before you had to pay for every installation off the software for the computer, and I couldn't afford to have 20 installations to have some computers with the cat ready to use them like once every six months would have been very inefficient. So we love that part. And the collaboration features are fantastic. Especially now with Kobe, that we have to have all the remote meetings, eyes fantastic, that you can have another person drive the cad while the whole team is watching that person change the model and do things and point to things that is absolutely revolutionary. We love it. The fact that you have very, very sophisticated version control before it was always a challenge asking people, please, if you create anniversary and apart, how do we name it so that people find it? And then you end up with all these collection of files with names that nobody remembers, what they are, the person left and now nobody knows which version is the right one m s with on shape on the version ING system it has, and the fact that you can go back in history off the document and go back to previous version so easily and then go back to the press and version and explore the history of the part that is truly, um, just world changing for us, that we can do that so easily on for me as a manager to manage this collection of information that is critical for our operations. It makes it so much easier because everything is in one place. I don't have to worry about file servers that go down that I have to administer that have to have I t taken care off that have to figure how to keep access to people to those servers when they're at home. And they need a virtual private network and all of that mess disappears. I just simply give give a personal account on shape. And then, magically, they have access to everything in the way I want. And we can manage the lower documents and everything in a way, that is absolutely fantastic. >>Rafael, what was your what? What were some of the concerns you had mentioned? You had some trepidation. Was it a performance? Was it security? You know, some of the traditional cloud stuff and I'm curious as to how How whether any of those act manifested were they really that you had to manage? What were your concerns? >>Look, the main concern is how long is it going to take for everybody in the team? to learn to use the system like it and buy into it because I don't want to have my engineers using tools against their will write. I want everybody to be happy because that's how they're productive. They're happy and they enjoyed the tools they have. That was my main concern. I was a little bit worried about the whole concept of not having the files in a place where I couldn't quote unquote seat in some serving on site, but that that's kind of an outdated concept, right? So that took a little bit of a mind shift. But very quickly. Then I started thinking, Look, I have a lot of documents on Google Drive like I don't worry about that. Why would I worry about my cat on on shape? Right is the same thing. So I just needed to sort of put things in perspective that way. Um, the other, um, you know, their concern was the learning curve right is like how is he will be for everybody to and for me to learn it on whether it had all of the features that we needed and there were a few features that I actually discussed with, um uh, Cody at on shape on. They were actually awesome about using their scripting language in on shape to sort of mimic some of the features of the old cat, uh, in on shaped in a way that actually works even better than the old system. So it was It was amazing. Yeah. >>Great. Thank you for that, Phillip. What's your experience been? Maybe you could take us through your journey with on shape? >>Sure. So we've been we've been using on shaped Silver Side for coming up on about four years now, and we love it. We're very happy with it. We have a very modular product line, so and we make anything from detectors that would go into backpacks? Two vehicles, two very large things that a shipping container would go through and saw. Excuse me. Shape helps us to track and collaborate faster on the design, have multiple people working a same time on a project. And it also helps us to figure out if somebody else comes to us and say, Hey, I want something new. How we congrats modules from things that we already have. Put them together and then keep track of the design development and the different branches and ideas that we have, how they all fit together. A za design comes together and it's just been fantastic from a mechanical engineering background. I will also say that having used a number of different systems and solid works was the greatest thing since sliced bread. Before I got using on shape, I went, Wow, this is amazing. And I really don't want to design in any other platform after after getting on Lee a little bit familiar with it. >>You know, it's funny, right? I will have the speed of technology progression. I was explaining to some young guns the other day how e used to have a daytime er and that was my life. And if I lost that day, timer, I was dead. And I don't know how we weigh existed without, you know, Google Maps. Eso did we get anywhere? I don't know, but, uh, but so So, Matt, you know, it's interesting to think about, um, you know, some of the concerns that Raphael brought up, you hear? For instance, you know, all the time. Wow. You know, I get my Amazon bill at the end of the month It's through the roof in. But the reality is that Yeah, well, maybe you are doing more, but you're doing things that you couldn't have done before. And I think about your experience in teaching and educating. I mean, you so much more limited in terms of the resource is that you would have had to be able to educate people. So what's your experience been with With on shape and what is it enabled? >>Um, yeah, it was actually talking before we went with on shape. We had a previous CAD program and I was talking to my vendor about it, and he let me know that we were actually one of the biggest CAD shops in the state. Because if you think about it a really big program, you know, really big company might employ 5, 10, 15, 20 cad guys, right? I mean, when I worked for a large defense contractor, I think there were probably 20 of us as the cad guys. I now have about 300 students doing cat. So there's probably more students with more hours of cat under their belt in my building than there were when I worked for the big defense contractor. Um, but like you mentioned, uh, probably our biggest hurdle is just re sources. And so we want We want one of things I've always prided myself and trying to do in this programs provide students with access two tools and skills that they're going to see either in college or in the real world. So it's one of the reason we went with a big professional cad program. There are, you know, sort of k 12 oriented software and programs and things. But, you know, I want my kids coding and python and using slack and using professional type of tools on DSO when it comes to cat. That's just that that was a really hurt. I mean, you know, you could spend $30,000 on one seat of, you know, professional level cad program, and then you need a $30,000 computer to run it on if you're doing a heavy assemblies, Um, and so one of my dreams and it was always just a crazy dream. And I was the way I would always pitcher in my school system and say someday I'm gonna have a kid on a school issued chromebook in subsidized housing on public WiFi doing professional level bad and that that was a crazy statement until a couple of years ago. So we're really excited that I literally and, you know, march in, um, you said the forced march the forced march into, you know, modernity, March 13th kids sitting in my engineering lab that we spent a lot of money on doing. Cad March 14th. Those kids were at home on their school shoot chromebooks on public WiFi, uh, keeping their designs going and collaborating. And then, yeah, I could go on and on about some of the things you know, the features that we've learned since then they're even better. So it's not like this is some inferior, diminished version of the cat. And there's so much about it, E >>wanna I wanna ask you that I may be over my skis on this, but we're seeing we're starting to see the early days of the democratization of CAD and product design. It is the the citizen engineer. I mean, maybe insulting to the engineers in the room, but but is that we're beginning to see that >>I have to believe that everything moves into the cloud. Part of that is democratization that I don't need. I can whether you know, I think artists, you know, I could have a music studio in my basement with a nice enough software package. And Aiken, I could be a professional for now. My wife's a photographer. I'm not allowed to say that I could be a professional photographer with, you know, some cloud based software. And so, yeah, I do think that's part of what we're seeing is more and more technology is moving to the cloud >>Philip or Rafael anything. Your dad, >>I think I mean yeah, that that that combination of cloud based cat and then three D printing that is becoming more and more affordable on ubiquitous It's truly transformative, and I think for education is fantastic. I wish when I was a kid I had the opportunity to play with those kinds of things because I was always the late things. But, you know, the in a very primitive way. So, um, I think there's a dream for kids Thio to be able to do this. And, um, yeah, there's so many other technologies coming on, like Arduino and all of these electronic things that live. Kids play at home very cheaply with things that back in my day would have been unthinkable. >>So we know there's a go ahead. Philip Way >>had a pandemic and silver site moved to a new manufacturing facility this year. I was just on the shop floor, talking with contractors, standing 6 ft apart, pointing at things. But through it all, our CAD system was completely unruffled. Nothing stopped in our development work. Nothing stopped in our support for existing systems in the field. We didn't have to think about it. We had other server issues, but none with our, you know, engineering cad, platform and product development and support world right ahead, which was cool, but also a That's point. I think it's just really cool what you're doing with the kids. The most interesting secondary and college level engineering work that I did was project based. It's an important problem to the world. Go solve it and that is what we do here. That is what my entire career has been. And I'm super excited to see See what your students are gonna be doing, uh, in there home classrooms on their chromebooks now and what they do. Building on that. >>Yeah, I'm super excited to see your kids coming out of college with engineering degrees because yeah, I think that project based experience is so much better than just sitting in a classroom, taking notes and doing math problems on. And I think he will give the kids a much better flavor What engineering is really about. Think a lot of kids get turned off by engineering because they think it's kind of dry because it's just about the math for some very abstract abstract concept, and they are there. But I think the most important thing is just that. Hands on a building and the creativity off, making things that you can touch that you can see that you can see functioning. >>Great. So you know, we all know the relentless pace of technology progression. So when you think about when you're sitting down with the folks that on shape and there the customer advisor for one of the things that you want on shape to do that it doesn't do today >>I could start by saying, I just love some of the things that does do because it's such a modern platform and I think some of these, uh, some some platforms that have a lot of legacy and a lot of history behind them. I think we're dragging some of that behind them. So it's cool to see a platform that seemed to be developed in a modern era. And so that's, you know, it is the Google docks. And so the fact that collaboration and version ing and link sharing is, and, like, platform agnostic abilities the fact that that seems to be just built into the nature of the thing so far, that's super exciting as far as things that it to go from there, Um, I don't know. >>Other than price, >>you can't say I >>can't say lower price. >>Yeah, so far on a PTC s that worked with us. Really well, so I'm not complaining. There. You there? >>Yeah. Yeah. No Gaps, guys. Whitespace, Come on. >>We've been really enjoying the three week update Cadence. You know, there's a new version every three weeks and we don't have to install it. We just get all the latest and greatest goodies. One of the trends that we've been following and enjoying is the the help with a revision management and release work flows. Um, and I know that there's more than on shape is working on that we're very excited for, because that's a big important part about making real hardware and supporting it in the field. Um, something that was cool. They just integrated Cem markup capability In the last release that took, we were doing that anyway, but we were doing it outside of on shapes, and now we get to streamline our workflow and put it in the CAD system where we're making those changes anyway, when we're reviewing drawings and doing this kind of collaboration. And so I think from our perspective, we continue to look forward toa further progress on that. There's a lot of capability in the cloud that I think they're just kind of scratching the surface on you. >>I would. I mean, you're you're asking to knit. Pick. I would say one of the things that I would like to see is is faster regeneration speed. There are a few times with comics necessities that regenerating the document takes a little longer than I would like to. It's not a serious issue, but anyway, I'm being spoiled, >>you know. That's good. I've been doing this a long time and I like toe Ask that question of practitioners and to me, it it's a signal like when you're nit picking and that you're struggling to knit. Pick that to me is a sign of a successful product. And And I wonder, I don't know, uh, have the deep dive into the architecture, But are things like alternative processors? You're seeing them hit the market in a big way. Uh, you know, maybe a helping address the challenge, But I'm gonna ask you the big, chewy question now, then would maybe go to some audience questions when you think about the world's biggest problems. I mean, we're global pandemics. Obviously top of mind. You think about nutrition, you know, feeding the global community. We've actually done a pretty good job of that. But it's not necessarily with the greatest nutrition climate change, alternative energy, the economic divides. You've got geopolitical threats and social unrest. Health care is a continuing problem. What's your vision for changing the world and how product innovation for good can be applied to some of the the problems that that you all are passionate about? Big question. But who wants toe start >>not biased. But for years I've been saying that if you want to solve the economy, the environment, uh, global unrest, pandemics education is the case If you wanna if you want to, um, make progress in those in those realms, I think funding funding education is probably gonna pay off pretty well. >>Absolutely. And I think stem is key to that. I mean, all of the, ah lot of the well being that we have today and then industrialized countries, thanks to science and technology, right, improvements in health care, improvements in communication, transportation, air conditioning. Um, every aspect of life is touched by science and technology. So I think having more kids studying and understanding that is absolutely key. Yeah, I agree, >>Philip, you got anything they had? >>I think there's some big technical problems in the world today, Raphael and ourselves there certainly working on a couple of them. Think they're also collaboration problems and getting everybody doing ableto pull together instead of pulling, pulling separately and to be able to spur the idea is onwards. So that's where I think the education side is really exciting. What Matt is doing and and it just kind of collaboration in general when we could do provide tools to help people do good work? Uh, that is, I think, valuable. >>Yeah, I think that's a very good point. And along those lines, we have some projects that are about creating very low cost instruments for low research settings places in Africa, Southeast Asia, South America so that they can do, um, um, biomedical research that it's difficult to do in those place because they don't have the money to buy the fancy lab machines that cost $30,000 an hour. Um, so we're trying to sort of democratize some of those instruments. And I think thanks to tools like Kahn shaped and is easier, for example, to have a conversation with somebody in Africa and show them the design that we have and discuss the details of it with them. Andi, that's amazing. Right? To have somebody you know, 10 time zones away, Um, looking really life in real time with you about your design and discussing the details or teaching them how to build a machine. Right? Because, um, you know, they have a three d printer. You can you just give them the design and say, like, you build it yourself, uh, even cheaper than and, you know, also billing and shipping it there. Um, so all that that that aspect of it is also so super important, I think, for any of these efforts to improve, um, some of the hardest part was in the world from climate change. Do you say, as you say, poverty, nutrition issues? Um, you know, availability of water. You have that project at about finding water. Um, if we can also help deploy technologies that teach people remotely how to create their own technologies or how to build their own systems that will help them solve those forms locally. I think that's very powerful. >>Yeah, that point about education is right on. I think some people in the audience may be familiar with the work of Erik Brynjolfsson and Andrew McAfee, the second machine age where they sort of put forth the premise that, uh, is it laid it out. Look, for the first time in history, machines air replacing humans from a cognitive perspective. Machines have always replaced humans, but that's gonna have an impact on jobs. But the answer is not toe protect the past from the future. Uh, the answer is education and public policy. That really supports that. So I couldn't agree more. I think it's a really great point. Um, we have We do have some questions from the audience. If if we can. If I can ask you guys, um, you know, this one kind of stands out. How do you see artificial intelligence? I was just talking about machine intelligence. Um, how do you see that? Impacting the design space guys trying to infuse a I into your product development. What can you tell me? >>Um, absolutely. Like, we're using AI for some things, including some of these very low cost instruments that will hopefully help us diagnose certain diseases, especially this is that are very prevalent in the Third World. Um, and some of those diagnostics are these days done by thes armies of technicians that are trained to look under the microscope. But, um, that's a very slow process. Is very error prone and having machine learning systems that can, to the same diagnosis faster, cheaper and also little machines that can be taken to very remote places to these villages that have no access to a fancy microscope to look at a sample from a patient that's very powerful, and I we don't do this. But I have read quite a bit about how certain places air, using a Tribune attorneys to actually help them optimize designs for parts. So you get these very interesting looking parts that you would have never thought off. A person would have never thought off, but that are incredibly light ink earlier strong and I have all sort of properties that are interesting thanks to artificial intelligence machine learning in particular, >>yet another, uh, advantage you get when when your work is in the cloud I've seen. I mean, there's just so many applications that so if the radiology scan is in the cloud and the radiologist is goes to bed at night, radiologist could come in in the morning and and say, Oh, the machine while you were sleeping was using artificial intelligence to scan these 40,000 images. And here's the five that we picked out that we think you should take a closer look at or like Raphael said. I can design my part. My, my, my, my, my you know, mount or bracket or whatever and go to sleep. And then I wake up in the morning. The machine has improved. It for me has made it strider strider stronger and lighter. Um And so just when your when your work is in the cloud, that's just that's a really cool advantage that you get that you can have machines doing some of your design work for you. >>Yeah, we've been watching, uh, you know, this week is this month, I guess is aws re invent and it's just amazing to see how much effort is coming around machine learning machine intelligence. You know, Amazon has sage maker Google's got, you know, embedded you no ML and big query. Certainly Microsoft with Azure is doing tons of stuff and machine learning. I think the point there is that that these things will be infused in tow R and D and in tow software products by the vendor community. And you all will apply that to your business and and build value through the unique data that your collecting you know, in your ecosystems. And and that's how you add value. You don't have to be necessarily, you know, developers of artificial intelligence, but you have to be practitioners to apply that. Does that make sense to you, Philip? >>Yeah, absolutely. And I think your point about value is really well chosen. We see AI involved from the physics simulations all the way up to interpreting radiation data, and that's where the value question, I think, is really important because it's is the output of the AI giving helpful information that the people that need to be looking at it. So if it's curating a serious of radiation alert, saying, Hey, like these are the anomalies you need to look at eyes it, doing that in a way that's going to help a good response on. In some cases, the II is only as good as the people. That sort of gave it a direction and turn it loose. And you want to make sure that you don't have biases or things like that underlying your AI that air going to result in, uh in less than helpful outcomes coming from it. So we spend quite a lot of time thinking about how do we provide the right outcomes to people who are who are relying on our systems? >>That's a great point, right? Humans, air biased and humans build models, so models are inherently biased. But then software is hitting the market. That's gonna help us identify those biases and help us, you know? Of course. Correct. So we're entering Cem some very exciting times, guys. Great conversation. I can't thank you enough for spending the time with us and sharing with our audience the innovations that you're bringing to help the world. So thanks again. >>Thank you so much. >>Thank you. >>Okay. You're welcome. Okay. When we come back, John McElheny is gonna join me. He's on shape. Co founder. And he's currently the VP of strategy at PTC. He's gonna join the program. We're gonna take a look at what's next and product innovation. I'm Dave Volonte and you're watching innovation for good on the Cube, the global leader. Digital technology event coverage. We'll be right back

>>from around the globe. It's the Cube presenting innovation for good. Brought to >>you by on shape. Okay, welcome back to innovation. For good. With me is John McElheny, who is one of the co founders of on Shape and is now the VP of strategy at PTC. John, good to see you. Thanks for making the time to come on the program. Thanks, Dave. So we heard earlier some of the accomplishments that you've made since the acquisition. How has the acquisition affected your strategy? Maybe you could talk about what resource is PTC brought to the table that allowed you toe sort of rethink or evolve your strategy? What can you share with us? >>Sure. You know, a year ago when John and myself met with Jim Hempleman early on is we're we're pondering started joining PTC. One of things became very clear is that we had a very clear shared vision about how we could take the on shape platform and really extended for for all of the PTC products, particular sort of their augmented reality as well as their their thing works or the i o. T business and their product. And so from the very beginning, there was a clear strategy about taking on shape, extending the platform and really investing, um, pretty significantly in the product development as well as go to market side of things, uh, toe to bring on shape out to not only the PTC based but sort of the broader community at large. So So So PTC has been terrific. Terrific, um, sort of partner as we've we've gonna go on after this market together. Eso we've added a lot of resource and product development side of things. Ah, lot of resource and to go to market and customer success and support. So really, on many fronts, that's with both resource is, as well a sort of support at the corporate level from from a strategic standpoint and then in the field, we've had wonderful interactions with many large enterprise customers as well as the PTC channels. So it's been really a great a great year. >>Well, and you think about the challenges of your business going to sas what you guys, you know, took on that journey, you know, 78 years ago. Uh, it's not trivial for a lot of companies to make that transition, especially company. That's been around as long as PTC. So So I'm wondering how much you know, I was just asking you what PC PTC brought the table. E gotta believe you're bringing a lot to the table to in terms of the mindset, uh, even things is, is mundane is not the right word. But things like how you compensate sales people, how you interact with customers, the notion of a service versus a product. I wonder if you could address >>that. Yeah, it's a It's a really great point. In fact, after we had met Jim last year, John and I one of the things we walked out in the seaport area in Boston one of things we sort of said is you know, Jim really gets what we're trying to do here and and part of let me bring you into the thinking early on. Part of what Jim talked about is there's lots of, you know, installed base sort of software that's inside of PTC base. That helped literally thousands of customers around the world. But the idea of moving to sass and all that it entails both from a technology standpoint, but also a cultural standpoint, like how do you not not just compensate the sales people as an example? But how do you think about customers? Success? In the past, it might have been that you had professional services that you bring out to a customer, help them deploy your solutions. Well, when you're thinking about a SAS based offering, it's really critical that you get customers successful with it. Otherwise, you may have turned, and you know it will be very expensive in terms of your business long term. So you've got to get customers success with software in the very beginning. So you know, Jim really looked at on shape and he said that John and I from a cultural standpoint, you know, a lot of times companies get acquired and they've acquired technology in the past that they integrate directly into into PTC and then sort of roll it out through their products or their distribution channels, he said. In some respects, John John, think about it as we're gonna take PTC and we want to integrate it into on shape because we want you to share with us both on the sales side and customer success on marketing on operations, you know, all the things because long term, we believe the world is a SAS world, that the whole industry is gonna move too. So, really, it was sort of an inverse in terms of the thought process related to normal transactions >>on that makes a lot of sense to me. You mentioned Sharon turns the silent killer of a SAS company. And you know, there's a lot of discussion, you know, in the entrepreneurial community because you live this, you know, what's the best path? I mean, today, you see, you know, you you watch Silicon Valley double, double, triple triple. But but there's a lot of people who believe, and I wonder, if you come in there is the best path to, you know, in the X Y axis. If if it's if it's, uh, growth on one and retention on the other axis, what's the best way to get to the upper right on? Really, the the best path is probably make sure you've nailed obviously the product market fit, but make sure that you can retain customers and then throw gas on the fire. You see a lot of companies they burn out trying to grow too fast, but they haven't figured out, you know that. But there's too much churn. They haven't figured out those metrics. I mean, obviously on shape. You know, you were sort of a pioneer in here. I gotta believe you've figured out that customer retention before you really? You know, put the pedal to the >>metal. Yeah. And you know, growth growth can mask a lot of things, but getting getting customers, especially the engineering space. Nobody goes and sits there and says, Tomorrow we're gonna go and and, you know, put 100 users on this and and immediately swap out all of our existing tools. These tools are very rich and deep in terms of capability, and they become part of the operational process of how a company designs and builds products. So any time anybody is actually going through the purchasing process, typically they will run a try along or they'll run a project where they look at Kind of What? What is this new solution gonna help them dio. How are we gonna orient ourselves for success? Longer term. So for us, you know, getting new customers and customer acquisition is really critical. But getting those customers to actually deploy the solution to be successful with it. You know, we like to sort of, say, the marketing or the lead generation and even some of the initial sales. That's sort of like the Kindle ing. But the fire really starts when customers deploy it and get successful with the solution because they bring other customers into the fold. And then, of course, if they're successful with it, you know, then in fact, you have negative turn which, ironically, means growth in terms of your inside of your install Bates. >>Right? And you've seen that with some of the emerging, you know, SAS companies, where you're you're actually you know, when you calculate whatever its net retention or renew ALS, it's actually from a dollar standpoint that's up in the high nineties or even over 100% >>so and >>that's a trend we're gonna continue. See, I wonder if we could sort of go back. Uh, and when you guys were starting on shape, some of the things that you saw that you were trying to strategically leverage and what's changed, you know, today we were talking. I was talking to John earlier about in a way, you kinda you kinda got a blank slate is like doing another startup. You're not. Obviously you've got installed base and customers to service, but but it's a new beginning for you guys. So one of the things that you saw then you know, cloud and and sas and okay, but that's we've been there, done that. What are you seeing? You know, today? >>Well, you know, So So this is a journey, of course, that that on shape on its own has gone through. And had, I'll sort of say, you know, several iterations, both in terms of of of, you know, how do you How do you get customers? How do you How do you get them successful? How do you grow those customers? And now that we've been part of PTC, the question becomes okay, One, there is certainly a higher level of credibility that helps us in terms of our our megaphone is much bigger than it was when we're standalone company. But on top of that now, figuring out how to work with their channel with their direct sales force, you know, they have, um, for example, you know, very large enterprises. Well, many of those customers are not gonna go in forklift out their existing solution to replace it with with on shape. However, many of them do have challenges in their supply chain and communications with contractors and vendors across the globe. And so, you know, finding our fit inside of those large enterprises as they extend out with their their customers is a very interesting area that we've really been sort of incremental to to PTC. And then, you know, they they have access to lots of other technology, like the i O. T business. And now, of course, the augmented reality business that that we can bring things to bear. For example, in the augmented reality world they've they've got something called expert capture. And this is essentially imagined, you know, in a are, ah, headset that allows you to be ableto to speak to it but also capture images, still images in video, and you could take somebody who's doing their task and capture literally the steps that they're taking its geo location and from their builds steps for new employees. We'll learn and understand how todo use that technology to help them do their job better. Well, when they do that if there is replacement products or variation of of some of the tools that that they built the original design instruction set for they now have another version. Well, they have to manage multiple versions. Well, that's what on shape is really great at doing and so taking our technology and helping their solutions as well. So it's not only expanding our customer footprint, it's expanding the application footprint in terms of how we can help them and help customers. >>So that leads me to the tam discussion. And again, it was part of your strategist role. How do you think about that? Was just talking to some of your customers earlier about the democratization of cat and engineering. You know, I kind of joked, sort of like citizen engineering, but but so that, you know, the demographics are changing the number of users potentially that can access the products because the it's so much more of a facile experience. How are you thinking about the total available market? >>It really is a great question, you know, It used to be when you when you sold boxes of software, it was how many engineers were out there, and that's the size of the market. The fact that matter is now when, When you think about access to that information, that data is simply a pane of glass. Whether it's a computer, whether it's a laptop, uh, a cell phone or whether it's a tablet, the ability to to use different vehicles, access information and data expands the capabilities and power of a system to allow feedback and iteration. I mean, one of the one of the very interesting things is in technology is when you can take something and really unleash it to a larger audience and builds, you know, purpose built applications. You can start to iterate, get better feedback. You know, there's a classic case in the clothing industry where Zara, you know, is a fast, sort of turnaround agile manufacturer. And there was a great New York Times article written a couple years ago. My wife's a fan of Zara, and I think she justifies any purchases by saying, you know, was Are you gotta purchase it now. Otherwise it may not be there the next time. Yet you go back to the store. They had some people in the store in New York that had this woman's throw kind of covering Shaw, and they said, Well, it would be great if we could have this little clip here so we could hook it through or something. And they sent a note back toe to the factory in Spain and literally two weeks later they had, you know, 4000 of these things in store, and they sold out because they had a closed loop and iterative process. And so if we could take information and allow people access in multiple ways through different devices and different screens, that could be very specific information that, you know, we remove a lot of the engineering data book, bring the end user products conceptually to somebody that would have had to wait months to get the actual physical prototype, and we could get feedback. Well, Weaken have a better chance of making sure whatever product we're building is the right product when it ultimately gets delivered to a customer. So it's really it's a much larger market that has to be thought of rather than just the kind of selling a boxes off where to an engineer, >>that's a great story, and and again, it's gotta be exciting for you guys to see that on day with the added resource is that you have a PTC eso. Let's talk. I promise people we want to talk about Atlas. Let's talk about the platform. A little bit of Atlas was announced last year. Atlas. For those who don't know it's a SAS space platform, it purports to go beyond product lifecycle management and you you're talking cloudlike agility and scale to CAD and product design. But, John, you could do a better job than I. What do >>we need to know about Atlas? Well, I think Atlas is a great description because it really is metaphorically, sort of holding up all of the PTC applications themselves. But from the very beginning, when John and I met with Jim, part of what we were intrigued about was that he shared a vision that on shape was more than just going to be a cad authoring tool that, in fact, you know, in the past, these engineering tools were very powerful, but they were very narrow in their purpose and focus, and we had specialty applications to manage diversions, etcetera. What we did in on shape is we kind of inverted that thinking we built this collaboration and sharing engine at the core and then kind of wrap the CAD system around it. But that collaboration sharing and version ING engine is really powerful. And it was that vision that Jim had that he shared that we had from the beginning, which was, how do we take this thing to make a platform that could be used for many other applications inside of inside of any company? And so not only do we have a partner application area that is is much like the APP store or Google play store. Uh, that was sort of our first misty initiation of this this this platform. But now we're extending out to broader applications and much meatier applications. And internally, that's the thing works in the in the augmented reality. But there'll be other applications that ultimately find its way on top of this platform, and so they'll get all the benefits of of the collaboration, sharing the version ing the multi platform multi device. And that's an extremely extremely, um, strategic leverage point for the company. >>You know, it's interesting, John, you mentioned the seaport before, So PTC For those who don't know built a beautiful facility down at the seaport in Boston. And of course, when PTC started back in the mid 19 eighties, this there was nothing at the seaport s. >>So it's >>kind of kind of ironic, you know, we were way seeing the transformation of the seaport. We're seeing the transformation of industry and of course, PTC. And I'm sure someday you'll get back into that beautiful office, you know? Wait. Yeah, I'll Bet. And, uh and but I wanna bring this up because I want I want you to talk about the future. How you how you see that our industry and you've observed this has moved from very product centric, uh, plat platform centric with sass and cloud. And now we're seeing ecosystems form around those products and platforms and in data flowing through the ecosystem, powering you new innovation. I wonder if you could paint a picture for us of what the future looks like to you from your vantage point. >>Yeah, I think one of the key words you said there is data because up until now, data for companies really was sort of trapped in different applications. And it wasn't because people with nefarious and they want to keep it limited. It was just the way in which things were built, and you know, when people use an application like on shape, what ends up happening is there their day to day interactions and everything that they dio is actually captured by the platform. And you know, we don't have access to that data. Of course it's it's the customer's data. But as as an artifact of them using the system than doing their day to day job, what's happening is they're creating huge amounts of information that can then be accessed and analyzed to help them both improve their design process, improve their efficiencies, improve their actual schedules in terms of making sure they can hit delivery times and be able to understand where there might be roadblocks in the future. So the way I see it is, companies now are deploying SAS based tools like an shape and an artifact of them. Using that platform is that they have now analytics and tools to better understand and an instrument and manage their business. And then from there, I think you're going to see, because these systems are all you know extremely well. architected allow through, you know, very structured AP. I calls to connect other SAS based applications. You're gonna start seeing closed loop sort of system. So, for example, people design using on shape. They end up going and deploying their system or installing it, or people use the end using products. People then may call back into the customers support line and report issues problems, challenges. They'll be able to do traceability back to the underlying design. They'll be able to do trend analysis and defect analysis from the support lines and tie it back and closed loop the product design, manufacture, deployment in the field sort of cycles. In addition, you can imagine there's many things that air sort of as designed. But then when people go on site and they have to install it, there's some alterations modifications. Think about think about like a large air conditioning units for buildings. You go and you go to train and you get a large air conditioning unit that put up on the top of building with a crane. They have to build all kinds of adaptors to make sure that that will fit inside of of of the particulars of that building. You know, with on shape and tools like this, you'll be able to not only take the design of what the air conditioning system might be, but also the all the adapter plates, but also how they installed it. So it sort of as designed as manufactured as stalled. And all these things can be traced just like if you think about the transformation of customer service or customer contacts. In the early days, you used to have tools that were PC based tools called contact management solution, you know, kind of act or gold mine. And these were basically glorified Elektronik role in Texas. It had a customer names, and they had phone numbers and whatever else. And Salesforce and Siebel, these types of systems really broadened out the perspective of what a customer relationship waas. So it wasn't just the contact information it was, you know, How did they come to find out about you as a company? So all the pre sort of marketing and then kind of what happens after they become a customer and it really was a 3 60 view. I think that 3 60 view gets extended to not just to the customers, but also tools and the products they use. And then, of course, the performance information that could come back to the manufacturer. So, you know, as an engineer, one of the things you learn about with systems is the following. And if you remember, when the 501st came out CDs that used to talk about four times over sampling or eight times over sampling and it was really kind of, you know, the fidelity the system. And we know from systems theory that the best way to improve the performance of a system is to actually have more feedback. The more feedback you have, the better system could be. And so that's why you got 16 60 for example, etcetera. Same thing here. The more feedback we have of different parts of a company that a better performance. The company will be better customer relationships, better overall financial performance as well. So that's that's the view I have of how these systems all tied together. >>The great vision in your point about the data is, I think, right on. It used to be so fragmented in silos, and in order to take a system view, you've gotta have a system view of the data. Uh, for years we've optimized maybe on one little component of the system and that sometimes we lose sight of the overall outcome. And so what you just described, I think is, I think sets up. You know very well as we exit. Hopefully soon we exit this this covert era on John. I hope that you and I can sit down face to face at a PTC on shape event in the near term. Who's >>in the seaport in the >>seaport Would tell you that great facility toe have have an event for sure. It >>z wonderful >>there. So So, John McElhinney. Thanks so much for for participating in the program. It was really great to have you on. >>Right. Thanks, Dave. >>Okay. And I want to thank everyone for participating. Today. We have some great guest speakers. And remember, this is a live program, so give us a little bit of time. We're gonna flip this site over to on demand mode so you can share it with your colleagues and you, or you can come back and and watch the sessions that you heard today. Uh, this is Dave Volonte for the Cube and on shape PTC. Thank you so much for watching innovation for good. Be well, have a great holiday and we'll see you next time.

>> Welcome everyone to the Cube Virtual. I'm your host, Rebecca Knight. With me today is Clint Poole, who is the head of marketing at Onshape for PTC. Clint, welcome to the Cube. >> Well, thanks for having me. Appreciate it. Great to be here. >> It is a pleasure to have you. And we are here marking the one-year anniversary of PTCs acquisition of Onshape, which PTC acquired last year for just under five, 500 million dollars. Congratulations. >> Thank you. >> So tell our viewers a little bit about Onshape. It is a true software and service company that helps product developers, by combining computer-aided design and with data management and collaboration tooling. Tell our viewers a little bit more about Onshape and why this space is growing so fast. >> I'm happy to do that. Onshape is the first cloud native, meaning it was built in the cloud for the cloud product development platform. And what that really means is it enables product designers, engineers from across the globe to create and collaborate on the development of any type of product. From mundane, household goods to very complex technology for the automotive industry. And it was founded actually by a group of folks who built the largest player in this market already. Won't be named. And they knew , intimately, the challenges that customers were facing with the old technology, which was the only technology available, when the previous set of products was built. So that was the motivation. It's very centered on the mission of the customers and what they're trying to do. And the way that we see it as is our role is just to give them a better set of tools so they can enable, to enable them to accomplish their mission of creating better products for a better world. >> So in terms of the challenges that customers face in this realm, can you dig in a little deeper there and talk, talk about what customers are trying to accomplish here? >> Well, they were trying to create, in whatever sector they work, the best possible products for their end customers, sets of customers. And the challenges are it's hard enough to ideate and design, but historically they've been restricted in the way that they had to work, because the technology that was available. The second generation of technology that most people are using today, wasn't built for global collaboration and most organizations, even if they're a small business, are collaborating with suppliers outside of the organization who are across the globe, different time zones, different locations. And certainly, for larger companies, you know, you have global design teams and, you know, their ability to work in real time on the set, the same set of models, same set of data is restricted. So we really unlock that challenge for them and just give them the power to work the way that they want to for the modern era. >> Empowering these people to collaborate. And as you said in real time, which is so critical, right now. >> Correct. So we've seen the SaaSification in CRM and also in human resources, in other things. Is this product lifecycle management, is this just a logical extension of those trends, in your mind? >> It is. I mean, again, this is, you know, Onshape itself was founded because this was an industry that hadn't seen much innovation in, really, decades on the product development side. And the team set out to build, you know, the only SaaS native product in the market. And we're seeing an increased adoption of the platform. Really was a natural extension of a macro trend called digital transformation, right? That's something this entire sector has been focused on. Manufacturers and product development teams have been focused on transforming their businesses and in every facet, whether it's, you know, enabling engineers to work from anywhere, if it's bringing additive manufacturing, 3D printing into the supply chain, robotics and factory automation on the factory floor, digitization of the supply chain, et cetera. You know, that macro trend has been going on for some time. And the industry, because it's so complex, has sort of been laggard in the adoption. And some are more mature along that digital transformation curve than others. Or what you saw with the pandemic was an absolute accelerated need to make movement and to become more mature, because you, really, you couldn't even work. You couldn't work the way that you had traditionally for 20 years. And it requires a completely different set of tools to accomplish the same set of goals. And, you know, we were built for the future and the future was accelerated by, you know, external crisis that no one could predict, but it is opening manufacturer's and design teams eyes to what they could accomplish if they work differently. >> So, as you're saying, a lot of companies are under a lot of stress and strain because of the pandemic, but it's also been a time for a lot of innovation and ingenuity and resourcefulness. And you said yourself that the pandemic has been almost a forcing mechanism to make companies make changes that maybe they had planned to make within the next five or 10 years. And they're having to make them now, out of necessity. So what kinds of products are you seeing customers making that's particularly interesting, in relation to the COVID-19 pandemic? >> Yeah. You know, our, again, the vision for us, which is it's a customer centric vision was always to give them something, our customer, a platform to enable them to make better products for a better world. That's our, our joint mission. And so pre-pandemic I described a little bit, you know, there's everything from the mundane to light switches. And lighting to really, really cool tech, you know. You know, battery packs for electric vehicles and even more socially conscious products like farm bots and affordable prosthetics. What has been incredible during the COVID crisis is engineers around the world have really come together and you know, focused on areas where they could help and particularly in the healthcare sector, which has been under siege with patients and was facing a shortage of supplies and in particular personal protective equipment, PPE and ventilators. And we watched these groups of teams, you know, sometimes formally under makeshift non-profits, and sometimes just informally with groups of engineers in their local community. You know, really thinking outside of the box and doing things like retrofitting scuba gear to be a medical grade masks and delivering those to their local hospital care providers. Designing, you know, face shields that could be easily manufactured with 3D printing locally and source and deliver. And folks taking all their equipment and retrofitting it to be ventilators, you know, and again, you know, mass manufacturing them as, as fast as they could in their local communities. It was really at the local level and you're seeing it globally. And it was incredible to watch the power of this customer community and what they can do. It was a living example of what we call innovation for good. >> Well, I definitely want to talk more about innovation for good, but of course, there's that Winston Churchill quote, never let it never let a crisis go to waste. It sounds as though the way you're talking about the way these engineers have come together, it's really inspiring. And it sounds, they got really creative. Do you think that this will be a lasting trend or does it take a crisis situation to have these people come together to solve these really difficult problems? >> No, this is a group of folks who solve problems every day and that's their persona. It's their passion and, you know, our role in their lives is to give them better capabilities to do that. It was just an opportunity for a set of professionals who are uniquely qualified to solve a problem in real time to come together. But it did show that what the modern workforce should look like for the sector. It showed how mint product design teams and manufacturers could collaborate globally, all right? Some of these groups of non-profits, were pulled together globally, overnight. You know, engineers who've never met each other, working across time zones on products. And then also with the advancements in manufacturing, additive manufacturing, 3D printing, you could build things locally, right? So maybe you don't need a global supply chain. It really was an illustration of how the industry needs to rethink how it designs, how it works and how it delivers products. There's ways to do things on a global basis more quickly, there's ways to source things locally and rethink supply chains. So it was really an inflection moment for a group of folks who've been trying to advance really better products for a better world, for some time. >> How do you see this confluence of CAD and PLM workflow affecting society in the coming decades? >> It really comes back to the joint mission that, you know, Onshape has with our customers and everybody in our space has with our customers. Developing better products for a better world. The idea of bringing those technologies together was integrating CAD with the management capabilities and a SaaS platform was all about enabling product teams to more easily collaborate on designs with colleagues or external vendors in real time, across locations, across time zones and not being inhibited by legacy technology. And this means that they can spend more time iterating on actual ideas and actually doing designing and focused on innovation and, and less time on the menial tasks that the former technology required of them. You know, administrative tasks, always tasks in every sector, inhibit innovation and good work. And, you know, because that's been removed what you're going to see as a better outcome at the end of the product development process. And that benefits everyone because as we've, as I said, they're working a lot of these folks are working on socially conscious products. They're all working on products that make our daily lives better as consumers, as patients and as customers. >> And making things better for society too. And importantly, they're enjoying their jobs more, which leads to bet better engagement and better productivity too. >> Correct. No employee likes to show up and have headaches with the way that you're supposed to work. And it's never been a driver of employee engagement. And for types of employees like this, who have this intellectual curiosity, you know, for the ability to expand that the scope of the team that they'd work on to give them more access to other intellectuals and professionals like themselves, and other times zones across the globe, you know, all brought together in a singular technology platform and empowered by it. You know, it is really a new way of thinking in a new way of working. Which yes, it drives our employee engagement, because this is, it suits their their persona and it suits how they want to work here. And that's a good thing for everybody because when employees are showing up fully engaged and fully empowered to do their best work, you have a better outcome. >> And excited to solve these important problems. You know, we talk a lot on the Cube about tech for good, and this is the theme innovation for good, which has been a theme of our conversation here today. And it's also an event that the Cube is co-hosting with you next month. The event is December 9th. What can people look forward to at the innovation for good event? Who should attend? What's in it for them? Give us the lowdown. >> Yeah. I mean, everybody who's involved in the product development and manufacturing sector, anyone who's involved in the development and the design and the development and manufacturing of products will benefit from the content that they'll see. And it's educators, it's professionals. You're going to hear from actual customers who are doing this, who are doing the actual work. You know, everything we do in Onshape is about the customer. You know, we enable the great work of other people, just to be very clear. And so you're going to hear from technology companies, aerospace companies, defense companies and academia. What they're doing to solve these challenges, what they're doing to leverage technology, to drive innovation for good. And you're going to walk away with insights of what you can do differently inside your workforce. >> So it will be actual news you can use and bring back to your company and apply the lessons that you're learning at the event. >> Correct. >> Excellent. Well, Clint Poole, a really, a pleasure to talk to you. By all means, join us on December 9th for Onshape's innovation for good event. We'll put the link in the description of this video. So please register and add the event to your calendar. We hope to see you there. Clint, again, thank you so much for coming on the Cube. Real pleasure talking to you. >> My pleasure. Hope it really joins us. >> Indeed. >> I'm Rebecca Knight. Stay tuned for more of the Cube Virtual.

(plane engine roars) (upbeat music) >> They call me Dr. Goh. I'm Senior Vice President and Chief Technology Officer of AI at Hewlett Packard Enterprise. And today I'm in Munich, Germany. Home to one and a half million people. Munich is famous for everything from BMW, to beer, to breathtaking architecture and festive markets. The Bavarian capital is the beating heart of Germany's automobile industry. Over 50,000 of its residents work in automotive engineering, and to date, Munich allocated around 30 million euros to boost electric vehicles and infrastructure for them. (upbeat music) >> Hello, everyone, my name is Dr. Jerome Baudry. I am a professor at the University of Alabama in Huntsville. Our mission is to use a computational resources to accelerate the discovery of drugs that will be useful and efficient against the COVID-19 virus. On the one hand, there is this terrible crisis. And on the other hand, there is this absolutely unique and rare global effort to fight it. And that I think is a is a very positive thing. I am working with the Cray HPE machine called Sentinel. This machine is so amazing that it can actually mimic the screening of hundreds of thousands, almost millions of chemicals a day. What we take weeks, if not months, or years, we can do in a matter of a few days. And it's really the key to accelerating the discovery of new drugs, new pharmaceuticals. We are all in this together, thank you. (upbeat music) >> Hello, everyone. I'm so pleased to be here to interview Dr. Jerome Baudry, of the University of Alabama in Huntsville. >> Hello, Dr. Goh, I'm very happy to be meeting with you here, today. I have a lot of questions for you as well. And I'm looking forward to this conversation between us. >> Yes, yes, and I've got lots of COVID-19 and computational science questions lined up for you too Jerome. Yeah, so let's interview each other, then. >> Absolutely, let's do that, let's interview each other. I've got many questions for you. And , we have a lot in common and yet a lot of things we are addressing from a different point of view. So I'm very much looking forward to your ideas and insights. >> Yeah, especially now, with COVID-19, many of us will have to pivot a lot of our research and development work, to address the most current issues. I watch your video and I've seen that you're very much focused on drug discovery using super computing. The central notebook you did, I'm very excited about that. Can you tell us a bit more about how that works, yeah? >> Yes, I'd be happy to in fact, I watch your video as well manufacturing, and it's actually quite surprisingly close, what we do with drugs, and with what other people do with planes or cars or assembly lanes. we are calculating forces, on molecules, on drug candidates, when they hit parts of the viruses. And we essentially try to identify what small molecules will hit the viruses or its components, the hardest to mess with its function in a way. And that's not very different from what you're doing. What you are describing people in the industry or in the transportation industry are doing. So that's our problem, so to speak, is to deal with a lot of small molecules. Guy creating a lot of forces. That's not a main problem, our main problem is to make intelligent choices about what calculates, what kind of data should we incorporate in our calculations? And what kind of data should we give to the people who are going to do the testing? And that's really something I would like you to do to help us understand better. How do you see artificial intelligence, helping us, putting our hands on the right data to start with, in order to produce the right data and accuracy. >> Yeah, that's that's a great question. And it is a question that we've been pondering in our strategy as a company a lot recently. Because more and more now we realize that the data is being generated at the far out edge. By edge. I mean, something that's outside of the cloud and data center, right? Like, for example, a more recent COVID-19 work, doing a lot of cryo electron microscope work, right? To try and get high resolution pictures of the virus and at different angles, so creating lots of movies under electron microscope to try and create a 3D model of the virus. And we realize that's the edge, right, because that's where the microscope is, away from the data center. And massive amounts of data is generated, terabytes and terabytes of data per day generated. And we had to develop means, a workflow means to get that data off the microscope and provide pre-processing and processing, so that they can achieve results without delay. So we learned quite a few lessons there, right, especially trying to get the edge to be more intelligent, to deal with the onslaught of data coming in, from these devices. >> That's fantastic that you're saying that and that you're using this very example of cryo-EM, because that's the kind of data that feeds our computations. And indeed, we have found that it is very, very difficult to get the right cryo-EM data to us. Now we've been working with HPE supercomputer Sentinel, as you may know, for our COVID-19 work. So we have a lot of computational power. But we will be even faster and better, frankly, if we knew what kind of cryo-EM data to focus on. In fact, most of our discussions are based on not so much how to compute the forces of the molecules, which we do quite well on an HP supercomputer. But again, what cryo-EM 3D dimensional space to look at. And it's becoming almost a bottleneck. >> Have access to that. >> And we spend a lot of time, do you envision a point where AI will be able to help us, to make this kind of code almost live or at least as close to live as possible, as that that comes from the edge? How to pack it and not triage it, but prioritize it for the best possible computations on supercomputers? >> What a visionary question and desire, right? Like exactly the vision we have, right? Of course, the ultimate vision, you aim for the best, and that will be a real time stream of processed data coming off the microscope straight, providing your need, right? We are not there. Before this, we are far from there, right? But that's the aim, the ability to push more and more intelligence forward, so that by the time the data reaches you, it is what you need, right, without any further processing. And a lot of AI is applied there, particularly in cryo-EM where they do particle picking, right, they do a lot of active pictures and movies of the virus. And then what they do is, they rotate the virus a little bit, right? And then to try and figure out in all the different images in the movies, to try and pick the particles in there. And this is very much image processing that AI is very good at. So many different stages, application is made. The key thing, is to deal with the data that is flowing at this at this speed, and to get the data to you in the right form, that in time. So yes, that's the desire, right? >> It will be a game changer, really. You'll be able to get things in a matter of weeks, instead of a matter of years to the colleague who will be doing the best day. If the AI can help me learn from a calculation that didn't exactly turn out the way we want it to be, that will be very, very helpful. I can see, I can envision AI being able to, live AI to be able to really revolutionize all the process, not only from the discovery, but all the way to the clinical, to the patient, to the hospital. >> Well, that's a great point. In fact, I caught on to your term live AI. That's actually what we are trying to achieve. Although I have not used that term before. Perhaps I'll borrow it for next time. >> Oh please, by all means. >> You see, yes, we have done, I've been doing also recent work on gene expression data. So a vaccine, clinical trial, they have the blood, they get the blood from the volunteers after the first day. And then to run very, very fast AI analytics on the gene expression data that the one, the transcription data, before translation to emit amino acid. The transcription data is enormous. We're talking 30,000, 60,000 different items, transcripts, and how to use that high dimensional data to predict on day one, whether this volunteer will get an adverse event or will have a good antibody outcome, right? For efficacy. So yes, how to do it so quickly, right? To get the blood, go through an SA, right, get the transcript, and then run the analytics and AI to produce an outcome. So that's exactly what we're trying to achieve, yeah. Yes, I always emphasize that, ultimately, the doctor makes that decision. Yeah, AI only suggests based on the data, this is the likely outcome based on all the previous data that the machine has learned from, yeah. >> Oh, I agree, we wouldn't want the machine to decide the fate of the patient, but to assist the doctor or nurse making the decision that will be invaluable? And are you aware of any kind of industry that already is using this kind of live AI? And then, is there anything in, I don't know in sport or crowd control? Or is there any kind of industry? I will be curious to see who is ahead of us in terms of making this kind of a minute based decisions using AI? Yes, in fact, this is very pertinent question. We as In fact, COVID-19, lots of effort working on it, right? But now, industries and different countries are starting to work on returning to work, right, returning to their offices, returning to the factories, returning to the manufacturing plants, but yet, the employers need to reassure the employees that things, appropriate measures are taken for safety, but yet maintain privacy, right? So our Aruba organization actually developed a solution called contact location tracing inside buildings, inside factories, right? Why they built this, and needed a lot of machine learning methods in there to do very, very well, as you say, live AI right? To offer a solution? Well, let me describe the problem. The problem is, in certain countries, and certain states, certain cities where regulations require that, if someone is ill, right, you actually have to go in and disinfect the area person has been to, is a requirement. But if you don't know precisely where the ill person has been to, you actually disinfect the whole factory. And if you have that, if you do that, it becomes impractical and cost prohibitive for the company to keep operating profitably. So what they are doing today with Aruba is, that they carry this Bluetooth Low Energy tag, which is a quarter size, right? The reason they do that is, so that they extract the tag from the person, and then the system tracks, everybody, all the employees. We have one company, there's 10,000 employees, right? Tracks everybody with the tag. And if there is a person ill, immediately a floor plan is brought up with hotspots. And then you just targeted the cleaning services there. The same thing, contact tracing is also produced automatically, you could say, anybody that is come in contact with this person within two meters, and more than 15 minutes, right? It comes up the list. And we, privacy is our focused here. There's a separation between the tech and the person, on only restricted people are allowed to see the association. And then things like washrooms and all that are not tracked here. So yes, live AI, trying to make very, very quick decisions, right, because this affects people. >> Another question I have for you, if you have a minute, actually has to be the same thing. Though, it's more a question about hardware, about computer hardware purify may. We're having, we're spending a lot of time computing on number crunching giant machines, like Sentinel, for instance, which is a dream to use, but it's very good at something but when we pulled it off, also spent a lot of time moving back and forth, so data from clouds from storage, from AI processing, to the computing cycles back and forth, back and forth, did you envision an architecture, that will kind of, combine the hardware needed for a massively parallel calculations, kind of we are doing. And also very large storage, fast IO to be more AI friendly, so to speak. You see on the horizon, some kind of, I would say you need some machine, maybe it's to be determined, to be ambitious at times but something that, when the AI ahead plan in terms of passing the vector to the massively parallel side, yeah, that makes sense? >> Makes a lot of sense. And you ask it I know, because it is a tough problem to solve, as we always say, computation, right, is growing capability enormously. But bandwidth, you have to pay for, latency you sweat for, right? >> That's a very good >> So moving data is ultimately going to be the problem. >> It is. >> Yeah, and we've move the data a lot of times, right, >> You move back and forth, so many times >> Back and forth, back and forth, from the edge that's where you try to pre-process it, before you put it in storage, yeah. But then once it arrives in storage, you move it to memory to do some work and bring it back and move it memory again, right, and then that's what HPC, and then you put it back into storage, and then the AI comes in you, you do the learning, the other way around also. So lots of back and forth, right. So tough problem to solve. But more and more, we are looking at a new architecture, right? Currently, this architecture was built for the AI side first, but we're now looking and see how we can expand that. And this is that's the reason why we announced HPE Ezmeral Data Fabric. What it does is that, it takes care of the data, all the way from the edge point of view, the minute it is ingested at the edge, it is incorporated in the global namespace. So that eventually where the data arrives, lands at geographically one, or lands at, temperature, hot data, warm data or cold data, regardless of eventually where it lands at, this Data Fabric checks everything, from in a global namespace, in a unified way. So that's the first step. So that data is not seen as in different places, different pieces, it is a unified view of all the data, the minute that it does, Just start from the edge. >> I think it's important that we communicate that AI is purposed for good, A lot of sci-fi movies, unfortunately, showcase some psychotic computers or teams of evil scientists who want to take over the world. But how can we communicate better that it's a tool for a change, a tool for good? >> So key differences are I always point out is that, at least we have still judgment relative to the machine. And part of the reason we still have judgment is because our brain, logical center is automatically connected to our emotional center. So whatever our logic say is tempered by emotion, and whatever our emotion wants to act, wants to do, right, is tempered by our logic, right? But then AI machine is, many call them, artificial specific intelligence. They are just focused on that decision making and are not connected to other more culturally sensitive or emotionally sensitive type networks. They are focus networks. Although there are people trying to build them, right. That's this power, reason why with judgment, I always use the phrase, right, what's correct, is not always the right thing to do. There is a difference, right? We need to be there to be the last Judge of what's right, right? >> Yeah. >> So that says one of the the big thing, the other one, I bring up is that humans are different from machines, generally, in a sense that, we are highly subtractive. We, filter, right? Well, machine is highly accumulative today. So an AI machine they accumulate to bring in lots of data and tune the network, but our brains a few people realize, we've been working with brain researchers in our work, right? Between three and 30 years old, our brain actually goes through a pruning process of our connections. So for those of us like me after 30 it's done right. (laughs) >> Wait till you reach my age. >> Keep the brain active, because it prunes away connections you don't use, to try and conserve energy, right? I always say, remind our engineers about this point, about prunings because of energy efficiency, right? A slice of pizza drives our brain for three hours. (laughs) That's why, sometimes when I get need to get my engineers to work longer, I just offer them pizza, three more hours, >> Pizza is universal solution to our problems, absolutely. Food Indeed, indeed. There is always a need for a human consciousness. It's not just a logic, it's not like Mr. Spock in "Star Trek," who always speaks about logic but forgets the humanity aspect of it. >> Yes, yes, The connection between the the logic centers and emotional centers, >> You said it very well. Yeah, yeah and the thing is, sleep researchers are saying that when you don't get enough REM sleep, this connection is weakened. Therefore, therefore your decision making gets affected if you don't get enough sleep. So I was thinking, people do alcohol test breathalyzer test before they are allowed to operate sensitive or make sensitive decisions. Perhaps in the future, you have to check whether you have enough REM sleep before, >> It is. This COVID-19 crisis obviously problematic, and I wish it never happened, but there is something that I never experienced before is, how people are talking to each other, people like you and me, we have a lot in common. But I hear more about the industry outside of my field. And I talk a lot to people, like cryo-EM people or gene expression people, I would have gotten the data before and process it. Now, we have a dialogue across the board in all aspects of industry, science, and society. And I think that could be something wonderful that we should keep after we finally fix this bug. >> Yes. yes, yes. >> Right? >> Yes, that's that's a great point. In fact, it's something I've been thinking about, right, for employees, things have changed, because of COVID-19. But very likely, the change will continue, yeah? >> Right. Yes, yes, because there are a few positive outcomes. COVID-19 is a tough outcome. But there positive side of things, like communicating in this way, effectively. So we were part of the consortium that developed a natural language processing system in AI system that would allow you scientists to do, I can say, with the link to that website, allows you to do a query. So say, tell me the latest on the binding energy between the Sasko B2 virus like protein and the AC receptor. And then you will, it will give you a list of 10 answers, yeah? And give you a link to the papers that say, they say those answers. If you key that in today to NLP, you see 315 points -13.7 kcal per mole, which is right, I think the general consensus answer, and see a few that are highly out of out of range, right? And then when you go further, you realize those are the earlier papers. So I think this NLP system will be useful. (both chattering) I'm sorry, I didn't mean to interrupt, but I mentioned yesterday about it, because I have used that, and it's a game changer indeed, it is amazing, indeed. Many times by using this kind of intelligent conceptual, analyzes a very direct use, that indeed you guys are developing, I have found connections between facts, between clinical or pharmaceutical aspects of COVID-19. That I wasn't really aware of. So a it's a tool for creativity as well, I find it, it builds something. It just doesn't analyze what has been done, but it creates the connections, it creates a network of knowledge and intelligence. >> That's why three to 30 years old, when it stops pruning. >> I know, I know. (laughs) But our children are amazing, in that respect, they see things that we don't see anymore. they make connections that we don't necessarily think of, because we're used to seeing a certain way. And the eyes of a child, are bringing always something new, which I think is what AI could potentially bring here. So look, this is fascinating, really. >> Yes, yes, difference between filtering subtractive and the machine being accumulative. That's why I believe, the two working together, can have a stronger outcome if used properly. >> Absolutely. And I think that's how AI will be a force for good indeed. Obviously see, seems that we would have missed that would end up being very important. Well, we are very interested in or in our quest for drug discovery against COVID-19, we have been quite successful so far. We have accelerated the process by an order of magnitude. So we're having molecules that are being tested against the virus, otherwise, it would have taken maybe three or four years to get to that point. So first thing, we have been very fast. But we are very interested in natural products, that chemicals that come from plants, essentially. We found a way to mine, I don't want to say explore it, but leverage, that knowledge of hundreds of years of people documenting in a very historical way of what plants do against what diseases in different parts of the world. So that really has been a, not only very useful in our work, but a fantastic bridge to our common human history, basically. And second, yes, plants have chemicals. And of course we love chemicals. Every living cell has chemicals. The chemicals that are in plants, have been fine tuned by evolution to actually have some biological function. They are not there just to look good. They have a role in the cell. And if we're trying to come up with a new growth from scratch, which is also something we want to do, of course, then we have to engineer a function that evolution hasn't already found a solution to, for in plants, so in a way, it's also artificial intelligence. We have natural solutions to our problems, why don't we try to find them and see their work in ourselves, we're going to, and this is certainly have to reinvent the wheel each time. >> Hundreds of millions of years of evolution, >> Hundreds of millions of years. >> Many iterations, >> Yes, ending millions of different plants with all kinds of chemical diversity. So we have a lot of that, at our disposal here. If only we find the right way to analyze them, and bring them to our supercomputers, then we will, we will really leverage this humongus amount of knowledge. Instead of having to reinvent the wheel each time we want to take a car, we'll find that there are cars whose wheels already that we should be borrowing instead of, building one each time. Most of the keys are out there, if we can find them, They' re at our disposal. >> Yeah, nature has done the work after hundreds of millions of years. >> Yes. (chattering) Is to figure out, which is it, yeah? Exactly, exactly hence the importance of biodiversity. >> Yeah, I think this is related to the Knowledge Graph, right? Where, yes, to objects and the linking parameter, right? And then you have hundreds of millions of these right? A chemical to an outcome and the link to it, right? >> Yes, that's exactly what it is, absolutely the kind of things we're pursuing very much, so absolutely. >> Not only only building the graph, but building the dynamics of the graph, In the future, if you eat too much Creme Brulee, or if you don't run enough, or if you sleep, well, then your cells, will have different connections on this graph of the ages, will interact with that molecule in a different way than if you had more sleep or didn't eat that much Creme Brulee or exercise a bit more, >> So insightful, Dr. Baudry. Your, span of knowledge, right, impressed me. And it's such fascinating talking to you. (chattering) Hopefully next time, when we get together, we'll have a bit of Creme Brulee together. >> Yes, let's find out scientifically what it does, we have to do double blind and try three times to make sure we get the right statistics. >> Three phases, three clinical trial phases, right? >> It's been a pleasure talking to you. I like we agreed, you knows this, for all that COVID-19 problems, the way that people talk to each other is, I think the things that I want to keep in this in our post COVID-19 world. I appreciate very much your insight and it's very encouraging the way you see things. So let's make it happen. >> We will work together Dr.Baudry, hope to see you soon, in person. >> Indeed in person, yes. Thank you. >> Thank you, good talking to you.

>> Narrator: Live from Orlando, Florida, it's theCUBE covering Grace Hopper Celebration of Women in Computing brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's coverage of the Grace Hopper conference here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Jeff Frick. We are joined by Yasmine Mustafa. She is the founder of ROAR. Thanks so much for joining us. >> Thank you. >> So ROAR is a self-defense wearable technology for women. Tell our viewers a little bit more about the technology and also really where you got the idea. >> Sure, I got the idea about four years ago. I decided to do something a little bit crazy. I got rid of all my possessions. I got rid of my apartment. I put a backpack on, and I booked a solo trip to South America for six months, and I did it for two reasons. The first reason is refugee, and when I came here, even though I was brought here when I was 15 applying for colleges, I actually found out I was undocumented, so I spent about 10 years working under the table trying to become legalized, and it was a very long, hard battle. It was very difficult to go to school and get a real job, and once I became a US citizen which happened five years ago, I was also able to sell my first company. I had a software company before ROAR. And after those two events, I said, "You know what, I'm 30 years old. "I deserve a break. "I've had a long journey. "I'm going to go celebrate." >> Jeff: Start another long journey. (laughs) >> Yeah, exactly. (laughing) I wanted to travel for so long and I couldn't 'cause when you're undocumented, it's really-- >> Hard to get back into the country. >> And you don't have the right credentials and even after I got my Green Card, I could. You can travel after getting your Green Card but I was so worried that I wouldn't be able to come back 'cause I've heard stories that I intentionally didn't, and so I booked this six-month trip as a way to reward myself and as a way to kind of make up for everything that had happened beforehand, and it was amazing trip. It was really life-changing. When I talk about it, I talk about my life in relation to before the trip and after the trip because it was so transformational, and I went to Spanish school for three weeks, did full Spanish immersions, stayed with a Spanish family in Ecuador, and then I went to Colombia and Argentina, Chile, Bolivia, Peru. I spent a month in each country but as incredible as it was, it was also incredibly eye-opening because everywhere that I went and visited, I just kept hearing story after story of a time a woman had been attacked or abused or harassed, and it really opened my eyes to the violence women face every day, and a week after I came back to Philadelphia, it was in a downtown, when my neighbor went out to her car. It's a horrible story. She was grabbed from behind. she was dragged into an alley. She was severely assaulted, brutally assaulted. When I saw the news story the next day, that was when the light bulb moment hit, and I called up my cofounder, my formal adviser of my last company and told him about it, Anthony Gold, and we ended together to start ROAR for Good, and the concept initially was completely different. We thought the problem was that existing self-defense tools, pepper sprays, tasers was that you have to pull them out of your pocket or your purse for them to be useful, and it's not like you could just be like, "Excuse me. "One second," (laughing) and dig it out, so we thought let's make it wearable so that it's readily accessible. This is when Fitbit was huge, and the initial idea was actually called the macelet, mace in a bracelet, and (laughs) exactly, and as clever as that name was, we found out through market research that it was actually a terrible idea, that the number one fear that women had of self-defense tools is, "I'm afraid I'm going to be overpowered, and my own self-defense device used as a weapon against me," and another one, "What if I use it against myself accidentally?" And when we did more research, we found that existing self-defense tools are actually made by men for other men, and when the market opportunity for women came about, they shrunk it, they shrinked it and pinked it, and they didn't really account for women's needs, so we went back to the drawing board, and we said, "All right, we need to make something "that's stylish but discreet, "something that can call for help, "something that can ward off an attack, "and something that cannot be used "against the person wearing it", and that's how we came up with Athena. >> So do you have one that you can show are yours, what it looks like? >> I do, I do, yes. >> This is what it looks like. >> How it works, okay. >> So it has a magnetic band. Initially it was actually a bracelet, and when we were doing self-defense classes with prototypes, we actually found out the worst place to wear a safety device is on your wrist, and can you guess why? >> Somebody grabs your wrist, grabs your arm, right? >> Exactly, or now you only have the opposite hand to activate it, so we said, "No, we need to make something "that's more readily accessible "where both hands can be free," so we designed it with this magnetic strip so that you can clip it on any which way you want. The most popular options we've seen are purse, pocket bra strap, or lapel, and the way it works is if you feel nervous, if you want someone to watch over you, you triple press the button, and it sends your coordinates to your family and friends showing exactly where you are, and if there is danger, if you really need help right away, you press and hold it for three seconds, and it will also sound an alarm, and in about seven rings, you'll also be able to call emergency number, the local PSAP, 911 center in your neighborhood. >> Wow. >> It's such a great concept. As are so many great inventions are, it's really assembling a bunch of components that already exist, your cellphone, an app on your phone, your network of your contacts, the GPS in your phone, and assembling it in a slightly different way for a very specific application. >> Everything that's commonplace, it's in the device. There's nothing proprietary about it. It's just the way that we put it together. Again, we took existing technology and put it together in a way and tested it to make sure that it's something that can work, and we worked with police officers and self-defense instructors to put it together, which is really eye-opening as well. >> And the other part, if you can explore, it's a different way to interact with 911 so if it is an emergency, you're not picking up the phone, you're not talking but according to your website, it's faster, in a lot of ways, it's more efficient. There's a lot of benefits to a not phone call connection with what traditionally has been the way you ask for help, and how did getting that through, is that a regulatory thing? How did that whole process work? >> That's a great question. It's something that we probably spent about a year working on, and we actually have a partner that does it for us, so this partner, what's really cool about them is that they have a relationship with all 500 PSAPs, so a PSAP is just your local 911 center in your area, and our service is going to be able to to leverage their partnership to be able to connect with all of them. The way their system works is they can actually better track you through their service than your normal cellphone can, which is also really cool, and if you're my emergency contacts and I press this button 'cause I can't call 911 and you're in Orlando, I'm in Philadelphia, it will actually route you to the PSAP in my neighborhood versus your local PSAP so then it saves the time in terms of calling the Orlando PSAP and then having them call the Philadelphia PSAP and then finding me, so we're really, really excited about this opportunity. >> So apart from the technology, I want to talk to you a little bit about funding. Funding is one of the greatest barriers that really, all technologists but in particular, women founders face. Can you describe a little bit about how you went about finding sources of money? You already sold a company by then so you'd already been successful. >> Yes. >> But what about people without the track record? What would you say? >> Sure. I'd love to touch on the social mission aspect at some point too if you don't mind. For funding, I'm very lucky in the sense that my cofounder, he's also founded several companies in the past and fundraising is his thing, so he's been the one to lead it but what we did initially, so we spent about 18 months in product development, and we did a lot of testing, I mean really awkward, we put ourselves in really awkward situations where we went to parks and coffee shops, and showed people this and said, "Why would you not use this? "Tell me why you don't like this," and then we went back to the drawing board and did it again and again, and then we got to the point where people said, "Yeah, I want this. "I want this for my mom. "I want this for my child. "I want this for my college student." But there is a world of difference between, say, yeah, I want it versus buying it, so what we did initially is we actually launched a crowdfunding campaign. We launched an Indiegogo campaign, and for us, it was really a way to test if we really had, we were onto something. We initially had the goal of $40,000. The results really blew us away. We hit that $40,000 goal within the second day, got to 100 by the 10th day, 100,000, and then we ended the campaign with a little bit over 300,000 funding, and that really allowed us to do our seat stage round, and we were lucky from the sense we have a really interesting story. There is a billionaire couple in the UK that found out about us through the campaign after it took off. We had sales in every state in the country, 50 countries worldwide. Ashton Kutcher tweeted about it. It was amazing. It went viral for a little bit, which was incredible, but they learned about it, and then reached out to Indiegogo and said, "We want to meet this team, the company behind this team," and we connected with them, and they immediately put $2 million into the company. We went and met with them in Chicago after they came over, and within three days, we had the money in our bank account, so we got a little bit lucky but having that crowdfunding campaign, the success as validation really helped us to be able to raise that additional funding, and then we went to Ben Franklin Technology Partners, and they put in $250,000, our local economic resource center that does matching, and that's how we raised our initial seed to growth. >> And you mentioned the social mission piece so I want you to tell our viewers a little bit more. >> Yeah, so I, for a long tIme, lived in fear, so being undocumented, not really knowing what could happen, and I'm actually giving a talk tomorrow about my whole journey, and learning about women living in fear in another different way while traveling throughout South America. I didn't want to build a company that just built products and sold them to women that just put the onus on women 'cause it's too common for us to say were you drinking when something happens or don't do, don't wear this, don't go here, and we wanted to change that narrative, hence, the ROAR for Good aspect, and what we found after talking with psychologists and researchers is that violence against women stems from gender discrimination and inequality, and that there is one trait, if taught to young kids when they're most impressionable, can actually reduce violence against women, and that's empathy, and that empathy has actually decreased 40% over the last 20 years, and there is a controversy on whether or not it's something that's learned or innate but wherever you fall in that category, there is no denying that it is falling regardless, so we invest, we have what we call a ROAR Back program, which is we invest a portion of proceeds of every sale to nonprofits that specifically focus on teaching respect and healthy relationships to young kids when it matters most. >> Yasmine, thank you so much for joining us. >> Thank you. >> It's a really exciting technology. Thank you. >> Hopefully we'll see you at Philly. We got to have a Philly show. >> Come to Philly, please. >> So you got Josh as a buddy so-- >> Yes. >> Come on, Josh. We got to have us some Philly. (laughing) >> I'm Rebecca Knight with Jeff Frick. We will have more from Grace Hopper just after this. (light music)

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

>> Welcome everyone. Thank you for coming to the Intel AI lounge and joining us here for this economist world event. My name is Jack. I'm the chief architect of our autonomist driving solutions at Intel and I'm very happy to be here and to be joined by an esteemed panel of colleagues who are joining to, I hope, engage you all in a frayed dialogue and discussion. There will be time for questions as well, so keep your questions in mind. Jot them down so you ask them to us later. So first, let me introduce the panel. Next to me we have Michelle, who's the co-founder and CEO of Fine Mind. She just did an interview here shortly. Fine Mind is a company that provides a technology platform for retailers and brands that uses artificial intelligence as the heart of the experiences that her company's technology provides. Joe from Intel is the head of partnerships and acquisitions for artificial intelligence and software technologies. He participated in the recent acquisition of Movidius, a computer vision company that Intel recently acquired and is involved in a lot of smart city activities as well. And then finally, Sarush, who is data scientist by training, but now has JDA labs, which is researching emerging technologies and their application in the supply chain worldwide. So at the end of the day, the internet things that artificial intelligence really promises to improve our lives in quite incredible ways and change the way that we live and work. Often times the first thing that we think about when we think about AI is Skynet, but we at Intel believe in AI for good and that there's a lot of things that can happen to improve the way people live, work, and enjoy life. So as things in the Internet, as things become connected, smart, and automated, artificial intelligence is really going to be at the heart of those new experiences. So as I said my role is the architect for autonomous driving. It's a common place when people think about artificial intelligence, because what we're trying to do is replace a human brain with a machine brain, which means we need to endow that machine with intelligent thoughts, contexts, experiences. All of these things that sort of make us human. So computer vision is the space, obviously, with cameras in your car that people often think about, but it's actually more complicated than that. How many of us have been in a situation on a two lane road, maybe there's a car coming towards us, there's a road off to the right, and you sort of sense, "You know what? That car might turn in front of me." There's no signal. There's no real physical cue, but just something about what that driver's doing where they're looking tells us. So what do we do? We take our foot off the accelerator. We maybe hover it over the brake, just in case, right? But that's intelligence that we take for granted through years and years and years of driving experience that tells us something interesting is happening there. And so that's the challenge that we face in terms of how to bring that level of human intelligence into machines to make our lives better and richer. So enough about automated vehicles though, let's talk to our panelists about some of the areas in which they have expertise. So first for Michelle, I'll ask... Many of us probably buy stuff online everyday, every week, every hour, hourly delivery now. So a lot has been written about the death of traditional retail experiences. How will artificial intelligence and the technology that your company has rejuvenate that retail experience, whether it be online or in the traditional brick and mortar store? >> Yeah, excuse me. So one of the things that I think is a common misconception. You hear about the death of the brick and mortar store, the growth of e-commerce. It's really that e-commerce is beating brick and mortar in growth only and there's still over 90% of the world's commerce is done in physical brick and mortar store. So e-commerce, while it has the growth, has a really long way to go and I think one of the things that's going to be really hard to replace is the very human element of interaction and connection that you get by going to a store. So just because a robot named Pepper comes up to you and asks you some questions, they might get you the answer you need faster and maybe more efficiently, but I think as humans we crave interaction and shopping for certain products especially, is an experience better enjoyed in person with other people, whether that's an associate in the store or people you come with to the store to enjoy that experience with you. So I think artificial intelligence can help it be a more frictionless experience, whether you're in store or online to get you from point A to buying the thing you need faster, but I don't think that it's going to ever completely replace the joy that we get by physically going out into the world and interacting with other people to buy products. >> You said something really profound. You said that the real revolution for artificial intelligence in retail will be invisible. What did you mean by that? >> Yeah, so right now I think that most of the artificial intelligence that's being applied in the retail space is actually not something that shoppers like you and I see when we're on a website or when we're in the store. It's actually happening behind the scenes. It's happening to dynamically change the webpage to show you different stuff. It's happening further up the supply chain, right? With how the products are getting manufactured, put together, packaged, shipped, delivered to you, and that efficiency is just helping retailers be smarter and more effective with their budgets. And so, as they can save money in the supply chain, as they can sell more product with less work, they can reinvest in experience, they can reinvest in the brand, they can reinvest in the quality of the products, so we might start noticing those things change, but you won't actually know that that has anything to do with artificial intelligence, because not always in a robot that's rolling up to you in an aisle. >> So you mentioned the supply chain. That's something that we hear about a lot, but frankly for most of us, I think it's very hard to understand what exactly that means, so could you educate us a bit on what exactly is the supply chain and how is artificial intelligence being implied to improve it? >> Sure, sure. So for a lot of us, supply chain is the term that we picked up when we went to school or we read about it every so often, but we're not that far away from it. It is in fact a key part of what Michelle calls the invisible part of one's experience. So when you go to a store and you're buying a pair of shoes or you're picking up a box of cereal, how often do we think about, "How did it ever make it's way here?" We're the constituent components. They probably came from multiple countries and so they had to be manufactured. They had to be assembled in these plants. They had to then be moved, either through an ocean vessel or through trucks. They probably have gone through multiple warehouses and distribution centers and then finally into the store. And what do we see? We want to make sure that when I go to pick up my favorite brand of cereal, it better be there. And so, one of the things where AI is going to help and we're doing a lot of active work in this, is in the notion of the self learning supply chain. And what that means is really bringing in these various assets and actors of the supply chain. First of all, through IOT and others, generating the data, obviously connecting them, and through AI driving the intelligence, so that I can dynamically figure out the fact that the ocean vessel that left China on it's way to Long Beach has been delayed by 24 hours. What does that mean when you go to a Foot Locker to buy your new pair of shoes? Can I come up with alternate sourcing decisions, so it's not just predicting. It's prescribing and recommending as well. So behind the scenes, bringing in a lot of the, generating a lot of the data, connecting a lot of these actors and then really deriving the smarts. That's what the self learning supply chain is all about. >> Are supply chains always international or can they be local as well? >> Definitely local as well. I think what we've seen over the last decades, it's kind of gotten more and more global, but a lot of the supply chain can really just be within the store as well. You'd be surprised at how often retailers do not know where their product is. Even is it in the front of the store? Is it in the back of the store? Is it in the fitting room? Even that local information is not really available. So to have sensors to discover where things are and to really provide that efficiency, which right now doesn't exist, is a key part of what we're doing. >> So Joe, as you look at companies out there to partner or potentially acquire, do you tend to see technologies that are very domain specific for retail or supply chain or do you see technologies that could bridge multiple different domains in terms of the experiences we could enjoy? >> Yeah, definitely. So both. A lot of infant technologies start out in very niched use cases, but then there are technologies that are pervasive across multiple geographies and multiple markets. So, smart cities is a good way to look at that. So let's level set really quick on smart cities and how we think about that. I have a little sheet here to help me. Alright, so, if anybody here played Sim City before, you have your little city that's a real world that sits here, okay? So this is reality and you have little buildings and cars and they all travel around and you have people walking around with cell phones. And what's happening is as we develop smart cities, we're putting sensors everywhere. We're putting them around utilities, energies, water. They're in our phones. We have cameras and we have audio sensors in our phones. We're placing these on light poles, which is existing sustaining power points around the city. So we have all these different sensors and they're not just cameras and microphones, but they're particulate sensors. They're able to do environmental monitoring and things like that. And so, what we have is we have this physical world with all these sensors here. And then what we have is we've created basically this virtual world that has a great memory because it has all the data from all the sensors and those sensors really act as ties, if you think of it like a quilt, trying a quilt together. You bring it down together and everywhere you have a stitch, you're stitching that virtual world on top of the physical world and that just enables incredible amounts of innovation and creation for developers, for entrepreneurs, to do whatever they want to do to create and solve specific problems. So what really makes that possible is communications, connectivity. So that's where 5G comes in. So with 5G it's not just a faster form of connectivity. It's new infrastructure. It's new communication. It includes multiple types of communication and connectivity. And what it allows it to do is all those little sensors can talk to each other again. So the camera on the light pole can talk to the vehicle driving by or the sensor on the light pole. And so you start to connect everything and that's really where artificial intelligence can now come in and sense what's going on. It can then reason, which is neat, to have computer or some sort of algorithm that actually reasons based on a situation that's happening real time. And it acts on that, but then you can iterate on that or you can adapt that in the future. So if we think of an actual use case, we'll think of a camera on a light post that observes an accident. Well it's programmed to automatically notify emergency services that there's been an accident. But it knows the difference between a fender bender and an actual major crash where we need to send an ambulance or maybe multiple firetrucks. And then you can create iterations and that learns to become more smart. Let's say there was a vehicle that was in the accident that had a little yellow placard on it that said hazard. You're going to want to send different types of emergency services out there. So you can iterate on what it actually does and that's a fantastic world to be in and that's where I see AI really playing. >> That's a great example of what it's all about in terms of making things smart, connective, and autonomous. So Michelle as somebody who has founded the company and the space with technology that's trying to bring some of these experiences to market, there may be folks in the audience who have aspirations to do the same. So what have you learned over the course of starting your company and developing the technology that you're now deploying to market? >> Yeah, I think because AI is such a buzz word. You can get a dot AI domain now, doesn't mean that you should use it for everything. Maybe 7, 10, 15 years ago... These trends have happened before. In the late 90s, it was technology and there was technology companies and they sat over here and there was everybody else. Well that not true anymore. Every company uses technology. Then fast forward a little bit, there was social media was a thing. Social media was these companies over here and then there was everybody else and now every company needs to use social media or actually maybe not. Maybe it's a really bad idea for you to spend a ton of money on social media and you have to make that choice for yourself. So the same thing is true with artificial intelligence and what I tell... I did a panel on AI for Adventure Capitalists last week, trying to help them figure out when to invest and how to evaluate and all that kind of stuff. And what I would tell other aspiring entrepreneurs is "AI is means to an end. "It's not an end in itself." So unless you're a PH.D in machine learning and you want to start an AI as a service business, you're probably not going to start an AI only company. You're going to start a company for a specific purpose, to solve a problem, and you're going to use AI as a means to an end, maybe, if it makes sense to get there, to make it more efficient and all that stuff. But if you wouldn't get up everyday for ten years to do this business that's going to solve whatever problem you're solving or if you wouldn't invest in it if AI didn't exist, then adding dot AI at the end of a domain is not going to work. So don't think that that will help you make a better business. >> That's great advice. Thank you. Surash, as you talked about the automation then of the supply chain, what about people? What about the workers whose jobs may be lost or displaced because of the introduction of this automation? What's your perspective on that? >> Well, that's a great question. It's one that I'm asked quite a bit. So if you think about the supply chain with a lot of the manufacturing plants, with a lot of the distribution centers, a lot of the transportation, not only are we talking about driverless cars as in cars that you and I own, but we're talking about driverless delivery vehicles. We're talking about drones and all of these on the surface appears like it's going to displace human beings. What humans used to do, now machines will do and potentially do better. So what are the implications around human beings. So I'm asked that question quite a bit, especially from our customers and my general perception on this is that I'm actually cautiously optimistic that human beings will continue to do things that are strategic. Human beings will continue to do things that are creative and human being will probably continue to do things that are truly catastrophic, that machines simply have not been able to learn because it doesn't happen very often. One thing that comes to mind is when ATM machines came about several years ago before my time, that displaced a lot of teller jobs in the banking industry, but the banking industry did not go belly up. They found other things to do. If anything, they offered more services. They were more branches that were closed and if I were to ask any of you now if you would go back and not have 24/7 access to cash, you would probably laugh at me. So the thing is, this is AI for good. I think these things might have temporary impact in terms of what it will do to labor and to human beings but I think we as human beings will find bigger, better, different things to do and that's just in the nature of the human journey. >> Yeah, there's definitely a social acceptance angle to this technology, right? Many of us technologists in the room, it's easier for us to understand what the technology is, how it works, how it was created, but for many of our friends and family, they don't. So there's a social acceptance angle to this. So Michelle as you see this technology deployed in retail environments, which is a space where almost every person in every country goes, how do you think about making it feel comfortable for people to interact with this kind of technology and not be afraid of the robots or the machines behind the curtain. >> Yeah, that's a great question. I think that user experience always has to come first, so if you're using AI for AI's sake or for the cool factor, the wow factor, you're already doing it wrong. Again, it needs to solve a problem and what I tend to tell people who are like, "Oh my God. AI sounds so scary. "We can't let this happen." I'm like, "It's already happening "and you're already liking it. "You just don't know "because it's invisible in a lot of ways." So if you can point of those scenarios where AI has already benefited you and it wasn't scary because it was a friendly kind of interaction, you might not even have realized it was there versus something that looks so different and... Like panic driving. I think that's why the driverless car thing is a big deal because you're so used to seeing, in America at least, someone on the left side of the car in the front seat. And not seeing that is like, woah, crazy. So I think that it starts with the experience and making it an acceptable kind of interface or format that doesn't give you that, "Oh my God. Something is wrong here," kind of feeling. >> Yeah, that's a great answer. In fact, it reminds me there was this really amazing study by a Professor Nicholas Eppily that was published in the journal of social psychology and the name of this study was called A Mind In A Machine. And what he did was he took subjects and had a fully functional automated vehicle and then a second identical fully functional automated vehicle, but this one had a name and it had a voice and it had sort of a personality. So it had human anthropomorphics characteristics. And he took people through these two different scenarios and in both scenarios he's evil and introduced a crash in the scenario where it was unavoidable. There was nothing going to happen. You were going to get into an accident in these cars. And then afterwards, he pulled the subjects and said, "Well, what did you feel about that accident? "First, what did you feel about the car?" They were more comfortable in the one that had anthropomorphic features. They felt it was safer and they'd be more willing to get into it, which is not terribly surprising, but the kicker was the accident. In the vehicle that had a voice and a name, they actually didn't blame the self-driving car they were in. They blamed the other car. But in the car that didn't have anthropomorphic features, they blamed the machine. They said there's something wrong with that car. So it's one of my favorite studies because I think it does illustrate that we have to remember the human element to these experiences and as artificial intelligence begins to replace humans, or some of us even, we need to remember that we are still social beings and how we interact with other things, whether they be human or non-human, is important. So, Joe, you talk about evaluating companies. Michelle started a company. She's gotten funding. As you go out and look at new companies that are starting up, there's just so much activity, companies that just add dot AI to the name as Michelle said, how do you cut through the noise and try to get to the heart of is there any value in a technology that a company's bringing or not? >> Definitely. Well, each company has it's unique, special sauce, right? And so, just to reiterate what Michelle was talking about, we look for companies that are really good at doing what they do best, whatever that may be, whatever that problem that they're solving that a customer's willing to pay for, we want to make sure that that company's doing that. No one wants a company that just has AI in the name. So we look for that number one and the other thing we do is once we establish that we have a need or we're looking at a company based on either talent or intellectual property, we'll go in and we'll have to do a vetting process and it takes a whole. It's a very long process and there's legal involved but at the end of the day, the most important thing for the start up to remember is to continue doing what they do best and continue to build upon their special sauce and make sure that it's very valuable to their customer. And if someone else wants to look at them for acquisition so be it, but you need to be meniacally focused on your own customer. That's my two cents. >> I'm thinking again about this concept of embedding human intelligence, but humans have biases right? And sometimes those biases aren't always good. So how do we as technologists in this industry try to create AI for good and not unintentionally put some of our own human biases into models that we train about what's socially acceptable or not? Anyone have any thoughts on that? >> I actually think that the hype about AI taking over and destroying humanity, it's possible and I don't want to disagree with Steven Hawking as he's way smarter than I am. But he kind of recognizes it could go both ways and so right now, we're in a world where we're still feeding the machine. And so, there's a bunch of different issues that came up with humans feeding the machine with their foibles of racism and hatred and bias and humans experience shame which causes them to lash out and what to put somebody else down. And so we saw that with Tay, the Microsoft chatbot. We saw that with even Google's fake news. They're like picking sources now to answer the question in the top box that might be the wrong source. Ads that Google serves often show men high paying jobs, $200,000 a year jobs, and women don't get those same ones. So if you trace that back, it's always coming back to the inputs and the lens that humans are coming at it from. So I actually think that we could be in a way better place after this singularity happens and the machines are smarter than us and they take over and they become our overlords. Because when we think about the future, it's a very common tendency for humans to fill in the blanks of what you don't know in the future with what's true today. And I was talking to you guys at lunch. We were talking about this harbored psychology professor who wrote a book and in the book he was talking about how 1950s, they were imagining the future and all these scifi stories and they have flying cars and hovercrafts and they're living in space, but the woman still stays at home and everyone's white. So they forgot to extrapolate the social things to paint the picture in, but I think when we're extrapolating into the future where the computers are our overlords, we're painting them with our current reality, which is where humans are kind of terrible (laughs). And maybe computers won't be and they'll actually create this Utopia for us. So it could be positive. >> That's a very positive view. >> Thanks. >> That's great. So do we have this all figured out? Are there any big challenges that remain in our industries? >> I want to add a little bit more to the learning because I'm a data scientist by training and a lot of times, I run into folks who think that everything's been figured out. Everything is done. This is so cool. We're good to go and one of the things that I share with them is something that I'm sure everyone here can relate to. So if a kindergartner goes to school and starts to spell profanity, that's not because the kid knows anything good or bad. That is what the kid has learned at home. Likewise, if we don't train machines well, it's training will in fact be biased to your point. So one of the things that we have to kep in mind when we talk about this is we have to be careful as well because we're the ones doing the training. It doesn't automatically know what is good or bad unless that set of data is also fed to it. So I just wanted to kind of add to your... >> Good. Thank you. So why don't we open it up a little bit for questions. Any questions in the audience for our panelists? There's one there looks like (laughs). Emily, we'll get to you soon. >> I had a question for Sarush based on what you just said about us training or you all training these models and teaching them things. So when you deploy these models to the public with them being machine learning and AI based, is it possible for us to retrain them and how do you build in redundancies for the public like throwing off your model and things like that? What are some of the considerations that go into that? >> Well, one thing for sure is training is continuous. So no system should be trained once, deployed, and then forgotten. So that is something that we as AI professionals need to absolutely, because... Trends change as well. What was optimal two years ago is no longer optimal. So that part needs to continue to happen and we're the where the whole IOT space is so important is it will continue to generate relevant consumable data that these machines can continuously learn. >> So how do you decide what data though, is good or bad, as you retrain and evolve that data over time? As a data scientist, how do you do selection on data? >> So, and I want to piggyback on what Michelle said because she's spot on. What is the problem that you're trying to solve? It always starts from there because we have folks who come in to CIOs, "Oh look. "When big data was hot, we started to collect "a lot of the data, but nothing has happened." But data by itself doesn't automatically do magic for you, so we ask, "What kind of problem are you trying to solve? "Are you trying to figure out "what kinds of products to sell? "Are you trying to figure out "the optimal assortment mix for you? "Are you trying to find the shortest path "in order to get to your stores?" And then the question is, "Do you now have the right data "to solve that problem?" A lot of times we put the science and I'm a data scientist by training. I would love to talk about the science, but really, it's the problem first. The data and the science, they come after. >> Thanks, good advice. Any other questions in the audience? Yes, one right up here. (laughing) >> Test, test. Can you hear me? >> Yep. >> So with AI machinery becoming more commonplace and becoming more accessible to developers and visionaries and thinkers alike rather than being just a giant warehouse of a ton of machines and you get one tiny machine learning, do you foresee more governance coming into play in terms of what AI is allowed to do and the decisions of what training data is allowed to be fed to Ais in terms of influence? You talk about data determining if AI will become good or bad, but humans being the ones responsible for the training in the first place, obviously, they can use that data to influence as they, just the governance and the influence. >> Jack: Who wants to take that one? >> I'll take a quick stab at it. So, yes, it's going to be an open discussion. It's going to have to take place, because really, they're just machines. It's machine learning. We teach it. We teach it what to do, how to act. It's just an extension of us and in fact, I think you had a really great conversation or a statement at lunch where you talked about your product being an extension of a designer because, and we can get into that a little bit, but really, it's just going to do what we tell it to do. So there's definitely going to have to be discussions about what type of data we feed. It's all going to be centered around the use case and what that solves the use case. But I imagine that that will be a topic of discussion for a long time about what we're going to decide to do. >> Jack: Michelle do you want to comment on this thought of taking a designer's brain and putting it into a model somehow? >> Well, actually, what I wanted to say was that I think that the regulation and the governance around it is going to be self imposed by the the developer and data science community first, because I feel like even experts who have been doing this for a long time don't rally have their arms fully around what we're dealing with here. And so to expect our senators, our congressmen, women, to actually make regulation around it is a lot, because they're not technologists by training. They have a lot of other stuff going on. If the community that's already doing the work doesn't quite know what we're dealing with, then how can we expect them to get there? So I feel like that's going to be a long way off, but I think that the people who touch and feel and deal with models and with data sets and stuff everyday are the kind of people who are going to get together and self-regulate for a while, if they're good hearted people. And we talk about AI for good. Some people are bad. Those people won't respect those convenance that we come up with, but I think that's the place we have to start. >> So really you're saying, I think, for data scientists and those of us working in this space, we have a social, ethical, or moral obligation to humanity to ensure that our work is used for good. >> Michelle: No pressure. (laughing) >> None taken. Any other questions? Anything else? >> I just wanted to talk about the second part of what she said. We've been working with a company that builds robots for the store, a store associate if you will. And one of their very interesting findings was that the greatest acceptance of it right now has been at car dealerships because when someone goes to the car dealer and we all have had terrible experiences doing that. That's why we try to buy it online, but just this perception that a robot would be unbiased, that it will give you the information without trying to push me one way or the other. >> The hard sell. >> So there's that perception side of it too that, it isn't that the governance part of your question, but more the biased perception side of what you said. I think it's fascinating how we're already trained to think that this is going to have an unbiased opinion, whether or not that true. >> That's fascinating. Very cool. Thank you Sarush. Any other questions in the audience? No, okay. Michelle, could I ask, you've got a station over there that talks a little bit more about your company, but for those that haven't seen it yet, could you tell us a little bit about what is the experience like or how is the shopping experience different for someone that's using your company's technology than what it was before? >> Oh, free advertising. I would love to. No, but actually, I started this company because as a consumer I found myself going back to the user experience piece, just constantly frustrated with the user experience of buying products one at a time and then getting zero help. And then here I am having to google how to wear a white blazer to not look like an idiot in the morning when I get dressed with my white blazer that I just bought and I was excited about. And it's a really simple thing, which is how do I use the product that I'm buying and that really simple thing has been just abysmally handled in the retail industry, because the only tool that the retailers have right now are manual. So in fashion, some of our fashion customers like John Varvatos is an example we have over there, it's like a designer for high-end men's clothing, and John Varvatos is a person, it's not just the name of the company. He's an actual person and he has a vision for what he wants his products to look like and the aesthetic and the style and there's a rockstar vibe and to get that information into the organization, he would share it verbally with PDFs, thing like that. And then his team of merchandisers would literally go manually and make outfits on one page and then go make an outfit on another page with the same exact items and then products would go out of stock and they'd go around in circles and that's a terrible, terrible job. So to the conversation earlier about people losing jobs because of artificial intelligence. I hope people do lose jobs and I hope they're the terrible jobs that no one wanted to do in the first place, because the merchandisers that we help, like the one form John Varvatos, literally said she was weeks away from quitting and she got a new boss and said, "If you don't ix this part of my job, I'm out of here." And he had heard about us. He knew about us and so he brought us in to solve that problem. So I don't think it's always a bad thing, because if we can take that route, boring, repetitive task off of human's plates, what more amazing things can we do with our brain that is only human and very unique to us and how much more can we advance ourselves and our society by giving the boring work to a robot or a machine. >> Well, that's fantastic. So Joe, when you talk about Smart Cities, it seems like people have been talking about Smart Cities for decades and often people cite funding issues, regulatory environment or a host of other reasons why these things haven't happened. Do you think we're on the cusp of breaking through there or what challenges still remain for fulfilling that vision of a smart city? >> I do, I do think we're on the cusp. I think a lot of it has to do, largely actually, with 5G and connectivity, the ability to process and send all this data that needs to be shared across the system. I also think that we're getting closer and more conscientious about security, which is a major issue with IOT, making sure that our in devices or our edge devices, those things out there sensing, are secure. And I think interocular ability is something that we need to champion as well and make sure that we basically work together to enable these systems. So very, very difficult to create little, tiny walled gardens of solutions in a smart city. You may corner a certain part of the market, but you're definitely not going to have that ubiquitous benefit to society if you establish those little walled gardens, so those are the areas I think we need to focus on and I think we are making serious progress in all of them. >> Very good. Michelle, you mentioned earlier that artificial intelligence was all around us in lots of places and things that we do on a daily basis, but we probably don't realize it. Could you share a couple examples? >> Yeah, so I think everything you do online for the most part, literally anything you might do, whether that's googling something or you go to some article, the ads might be dynamically picked for you using machine learning models that have decided what is appropriate based on you and your treasure trove of data that you have out there that you're giving up all the time and not really understanding you're giving up >> The shoes that follow you around the internet right? >> Yeah, exactly. So that's basically anything online. I'm trying to give in the real-world. I think that, to your point earlier about he supply chain, just picking a box of cereal off the shelf and taking it home, there's not artificial intelligence in that at all, but the supply chain behind it. So the supply chain behind pretty much everything we do even in television, like how media gets to us and get consumed. At some point in the supply chain, there's artificial intelligence playing in there as well. >> So to start us in the supply chain where we can get the same day even within the hour delivery. How do you get better than that? What's coming that's innovative in the supply chain that will be new in the future? >> Well, so that is one example of it, but you'd be surprised at how inefficient the supply chain is, even with all the advances that have already gone in, whether it's physical advances around building modern warehouses and modern manufacturing plants, whether it's through software and others that really help schedule things and optimize things. What has happened in the supply chain just given how they've evolved is they're very siloed, so a lot of times the manufacturing plant does things that the distribution folks do not know. The distribution folks do things that the transportation folks don't know and then the store folks know nothing other than when the trucks pulls up, that's the first time they find out about things. So where the great opportunity in my mind is, in the space that I'm in, is really the generation of data, the connection of data, and finally, deriving the smarts that really help us improve efficiency. There's huge opportunity there. And again, we don't know it because it's all invisible to us. >> Good. Let me pause and see if there's any questions in the audience. There, we got one there. >> Thank you. Hi guys, you alright? I just had a question about ethics and the teaching of ethics. As you were saying, we feed the artificial intelligence, whereas in a scenario which is probably a little bit more attuned to automated driving, in a car crash scenario between do we crash these two people or three people? I would be choosing two, whereas the scenario may be it's actually better to just crash the car and kill myself. That thought would never go through my mind, because I'm human. My rule number one is self preservation. So how do we teach the computer this sort of side of it? Is there actually the AI ethic going to be better than our own ethics? How do we start? >> Yeah, that's a great question. I think the opportunity is there as Michelle was talking earlier about maybe when you cross that chasm and you get this new singularity, maybe the AI ethics will be better than human ethics because the machine will be able to think about greater concerns perhaps other than ourselves. But I think just from my point of view, working in the space of automated vehicles, I think it is going to have to be something that the industry, and societies are different, different geographies, and different countries. We have different ways of looking at the world. Cultures value different things and so I think technologists in those spaces are going to have to get together and agree amongst the community from a social contract theory standpoint perhaps in a way that's going to be acceptable to everyone who lives in that environment. I don't think we can come up with a uniform model that would apply to all spaces, but it's got to be something though that we all, as members of a community, can accept. And so yeah, that would be the right thing to do in that situation and that's not going to be an easy task by any means, which is, I think, one of the reasons why you'll continue to see humans have an important role to play in automated vehicles so that the human could take over in exactly that kind of scenario, because the machines perhaps aren't quite smart enough to do it or maybe it's not the smarts or the processing capability. It's maybe that we haven't as technologists and ethicists gotten together long enough to figure out what are those moral and ethical frameworks that we could use to apply to those situations. Any other thoughts? >> Yeah, I wanted to jump in there real quick. Absolutely questions that need to be answered, but let's come together and make a solution that needs to have those questions answered. So let's come together first and fix the problems that need to be fixed now so that we can build out those types of scenarios. We can now put our brainpower to work to decide what to do next. There was a quote I believe by Andrew Ningh Bidou and he was saying in concerning deep questions about what's going to happen in the future with AI. Are we going to have AI overlords or anything like that? And it's kind of like worrying about overpopulation at the point of Mars. Because maybe we're going to get there someday and maybe we're going to send people there and maybe we're going to establish a human population on Mars and then maybe it will get too big and then maybe we'll have problems on Mars, but right now we haven't landed on the planet and I thought that really does a good job of putting in perspective that that overall concern about AI taking over. >> So when you think about AI being applied for good and Michelle you talked about don't do AI just for AI's sake, have a problem to solve, I'll open it up to any of the three of you, what's a problem in your life or in your work experience that you'd love somebody out here would go solve with AI? >> I have one. Sorry, I wanted to do this real quick. There's roads blocked off and it's raining and I have to walk a mile to find a taxi in the rain right now after this to go home. I would love for us to have some sort of ability to manage parking spaces and determine when and who can come in to which parts of the city and when there's a spot downtown, I want my autonomous vehicle to know which one's available and go directly to that spot and I want it to be cued in a certain manner to where I'm next in line and I know. And so I would love for someone to go solve that problem. There's been some development on the infrastructure side for that kind of solution. We have a partnership Intel does with GE and we're putting sensors that have, it's an IOT sensor basically. It's called City IQ. It has environmental monitoring, audio, visual sensors and it allows this type of use case to take place. So I would love to see iterations on that. I would love to see, sorry there's another one that I'm particular about. Growing up I lived in Southern California right against the hills, a housing development, because the hills and there was not a factory, but a bunch of oil derricks back there. I would love to have sensor that senses the particulate in the air to see if there was too many fumes coming from that oil field into my yard growing up as a little kid. I would love for us to solve problems like that, so that's the type of thing that we'll be able to solve. Those are the types of innovations that will be able to take place once we have these sensors in place, so I'm going to sit down on that one and let someone else take over. >> I'm really glad you said the second one because I was thinking, "What I'm about to say is totally going to "trivialize Joe's pain and I don't want to do that." But cancer is my answer, because there's so much data in health and all these patterns are there waiting to be recognized. There's so many things you don't know about cancer and so many indicators that we could capture if we just were able to unmask the data and take a look, but I knew a brilliant company that was using artificial intelligence specifically around image processing to look at CAT scans and figure out what the leading indicators might be in a cancerous scenario. And they pivoted to some way more trivial problem which is still a problem and not to trivialize parking an whatnot, but it's not cancer. And they pivoted away from this amazing opportunity because of the privacy and the issues with HIPPA around health data. And I understand there's a ton of concern with it getting into the wrong hands and hacking and all of this stuff. I get that, but the opportunity in my mind far outweighs the risk and the fact that they had to change their business model and change their company essentially broke my heart because they were really onto something. >> Yeah that's a shame and it's funny you mention that. Intel has an effort that we're calling the cancer cloud and what we're trying to do is provide some infrastructure to help with that problem and the way cancer treatments work today is if you go to a university hospital let's say here in Texas, how you interpret that scan and how you respond and apply treatment, that knowledge is basically just kept within that hospital and within that staff. And so on the other side of the country, somebody could go in and get a scan and maybe that scan brand new to that facility and so they don't know how to treat it, but if you had an opportunity with machine learning to be able to compare scans from people, not only just in this country, but around the world and understand globally, all of the hundreds of different treatment pads that were applied to that particular kind of cancer, think how many lives could be saved, because then you're sharing knowledge with what courses of treatment worked. But it's one of those things like you say, sometimes it's the regulatory environment or it's other factors that hold us back from applying this technology to do some really good things, so it's a great example. Okay, any other questions in the audience? >> I have one. >> Good Emily. >> So this goes off of the HIPPA question, which is, and you were talking about just dynamically displaying ads earlier. What does privacy look like in a fully autonomous world? Anybody can answer that one. Are we still private citizens? What does it look like? >> How about from a supply chain standpoint? You can learn a lot about somebody in terms of the products that they buy and I think to all of us, we sort of know maybe somebody's tracking what we're buying but it's still creepy when we think about how people could potentially use that against us. So, how do you from a supply chain standpoint approach that problem? >> Yeah and it's something that comes up in my life almost every day because one of the thing's we'd like to do is to understand consumer behavior. How often am I buying? What kinds of products am I buying? What am I returning? And so for that you need transactional data. You really get to understand the individual. That then starts to get into this area of privacy. Do you know too much about me? And so a lot of times what we do is data is clearly anonymized so all we know is customer A has this tendency, customer B has this tendency. And that then helps the retailers offer the right products to these customers, but to your point, there are those privacy concerns and I think issues around governance, issues around ethics, issues around privacy, these will continue to be ironed out. I don't think there's a solid answer for any of these just yet. >> And it's largely a reflection of society. How comfortable are we with how much privacy? Right now I believe we put the individual in control of as much information as possible that they are able to release or not. And so a lot of what you said, everyone's anonymizing everything at the moment, but that may change as society's values change slightly and we'll be able to adapt to what's necessary. >> Why don't we try to stump the panel. Anyone have any ideas on things in your life you'd like to be solved with AI for good? Any suggestions out there that we could then hear from our data scientist and technologist and folks here? Any ideas? No? Alright good. Alright, well, thank you everyone. Really appreciate your time. Thank you for joining Intel here at the AI lounge at Autonomous World. We hope you've enjoyed the panel and we wish you a great rest of your event here at South by Southwest. (audience clapping) (bright music)

(upbeat music) >> Hello, everyone, welcome to theCUBE. I'm John Furrier, the host of theCUBE in Palo Alto, California. Also it's SiliconANGLE News. Got two great guests here to talk about AI, the impact of the future of the internet, the applications, the people. Amr Awadallah, the founder and CEO, Ed Alban is the CEO of Vectara, a new startup that emerged out of the original Cloudera, I would say, 'cause Amr's known, famous for the Cloudera founding, which was really the beginning of the big data movement. And now as AI goes mainstream, there's so much to talk about, so much to go on. And plus the new company is one of the, now what I call the wave, this next big wave, I call it the fifth wave in the industry. You know, you had PCs, you had the internet, you had mobile. This generative AI thing is real. And you're starting to see startups come out in droves. Amr obviously was founder of Cloudera, Big Data, and now Vectara. And Ed Albanese, you guys have a new company. Welcome to the show. >> Thank you. It's great to be here. >> So great to see you. Now the story is theCUBE started in the Cloudera office. Thanks to you, and your friendly entrepreneurship views that you have. We got to know each other over the years. But Cloudera had Hadoop, which was the beginning of what I call the big data wave, which then became what we now call data lakes, data oceans, and data infrastructure that's developed from that. It's almost interesting to look back 12 plus years, and see that what AI is doing now, right now, is opening up the eyes to the mainstream, and the application's almost mind blowing. You know, Sati Natel called it the Mosaic Moment, didn't say Netscape, he built Netscape (laughing) but called it the Mosaic Moment. You're seeing companies in startups, kind of the alpha geeks running here, because this is the new frontier, and there's real meat on the bone, in terms of like things to do. Why? Why is this happening now? What's is the confluence of the forces happening, that are making this happen? >> Yeah, I mean if you go back to the Cloudera days, with big data, and so on, that was more about data processing. Like how can we process data, so we can extract numbers from it, and do reporting, and maybe take some actions, like this is a fraud transaction, or this is not. And in the meanwhile, many of the researchers working in the neural network, and deep neural network space, were trying to focus on data understanding, like how can I understand the data, and learn from it, so I can take actual actions, based on the data directly, just like a human does. And we were only good at doing that at the level of somebody who was five years old, or seven years old, all the way until about 2013. And starting in 2013, which is only 10 years ago, a number of key innovations started taking place, and each one added on. It was no major innovation that just took place. It was a couple of really incremental ones, but they added on top of each other, in a very exponentially additive way, that led to, by the end of 2019, we now have models, deep neural network models, that can read and understand human text just like we do. Right? And they can reason about it, and argue with you, and explain it to you. And I think that's what is unlocking this whole new wave of innovation that we're seeing right now. So data understanding would be the essence of it. >> So it's not a Big Bang kind of theory, it's been evolving over time, and I think that the tipping point has been the advancements and other things. I mean look at cloud computing, and look how fast it just crept up on AWS. I mean AWS you back three, five years ago, I was talking to Swami yesterday, and their big news about AI, expanding the Hugging Face's relationship with AWS. And just three, five years ago, there wasn't a model training models out there. But as compute comes out, and you got more horsepower,, these large language models, these foundational models, they're flexible, they're not monolithic silos, they're interacting. There's a whole new, almost fusion of data happening. Do you see that? I mean is that part of this? >> Of course, of course. I mean this wave is building on all the previous waves. We wouldn't be at this point if we did not have hardware that can scale, in a very efficient way. We wouldn't be at this point, if we don't have data that we're collecting about everything we do, that we're able to process in this way. So this, this movement, this motion, this phase we're in, absolutely builds on the shoulders of all the previous phases. For some of the observers from the outside, when they see chatGPT for the first time, for them was like, "Oh my god, this just happened overnight." Like it didn't happen overnight. (laughing) GPT itself, like GPT3, which is what chatGPT is based on, was released a year ahead of chatGPT, and many of us were seeing the power it can provide, and what it can do. I don't know if Ed agrees with that. >> Yeah, Ed? >> I do. Although I would acknowledge that the possibilities now, because of what we've hit from a maturity standpoint, have just opened up in an incredible way, that just wasn't tenable even three years ago. And that's what makes it, it's true that it developed incrementally, in the same way that, you know, the possibilities of a mobile handheld device, you know, in 2006 were there, but when the iPhone came out, the possibilities just exploded. And that's the moment we're in. >> Well, I've had many conversations over the past couple months around this area with chatGPT. John Markoff told me the other day, that he calls it, "The five dollar toy," because it's not that big of a deal, in context to what AI's doing behind the scenes, and all the work that's done on ethics, that's happened over the years, but it has woken up the mainstream, so everyone immediately jumps to ethics. "Does it work? "It's not factual," And everyone who's inside the industry is like, "This is amazing." 'Cause you have two schools of thought there. One's like, people that think this is now the beginning of next gen, this is now we're here, this ain't your grandfather's chatbot, okay?" With NLP, it's got reasoning, it's got other things. >> I'm in that camp for sure. >> Yeah. Well I mean, everyone who knows what's going on is in that camp. And as the naysayers start to get through this, and they go, "Wow, it's not just plagiarizing homework, "it's helping me be better. "Like it could rewrite my memo, "bring the lead to the top." It's so the format of the user interface is interesting, but it's still a data-driven app. >> Absolutely. >> So where does it go from here? 'Cause I'm not even calling this the first ending. This is like pregame, in my opinion. What do you guys see this going, in terms of scratching the surface to what happens next? >> I mean, I'll start with, I just don't see how an application is going to look the same in the next three years. Who's going to want to input data manually, in a form field? Who is going to want, or expect, to have to put in some text in a search box, and then read through 15 different possibilities, and try to figure out which one of them actually most closely resembles the question they asked? You know, I don't see that happening. Who's going to start with an absolute blank sheet of paper, and expect no help? That is not how an application will work in the next three years, and it's going to fundamentally change how people interact and spend time with opening any element on their mobile phone, or on their computer, to get something done. >> Yes. I agree with that. Like every single application, over the next five years, will be rewritten, to fit within this model. So imagine an HR application, I don't want to name companies, but imagine an HR application, and you go into application and you clicking on buttons, because you want to take two weeks of vacation, and menus, and clicking here and there, reasons and managers, versus just telling the system, "I'm taking two weeks of vacation, going to Las Vegas," book it, done. >> Yeah. >> And the system just does it for you. If you weren't completing in your input, in your description, for what you want, then the system asks you back, "Did you mean this? "Did you mean that? "Were you trying to also do this as well?" >> Yeah. >> "What was the reason?" And that will fit it for you, and just do it for you. So I think the user interface that we have with apps, is going to change to be very similar to the user interface that we have with each other. And that's why all these apps will need to evolve. >> I know we don't have a lot of time, 'cause you guys are very busy, but I want to definitely have multiple segments with you guys, on this topic, because there's so much to talk about. There's a lot of parallels going on here. I was talking again with Swami who runs all the AI database at AWS, and I asked him, I go, "This feels a lot like the original AWS. "You don't have to provision a data center." A lot of this heavy lifting on the back end, is these large language models, with these foundational models. So the bottleneck in the past, was the energy, and cost to actually do it. Now you're seeing it being stood up faster. So there's definitely going to be a tsunami of apps. I would see that clearly. What is it? We don't know yet. But also people who are going to leverage the fact that I can get started building value. So I see a startup boom coming, and I see an application tsunami of refactoring things. >> Yes. >> So the replatforming is already kind of happening. >> Yes, >> OpenAI, chatGPT, whatever. So that's going to be a developer environment. I mean if Amazon turns this into an API, or a Microsoft, what you guys are doing. >> We're turning it into API as well. That's part of what we're doing as well, yes. >> This is why this is exciting. Amr, you've lived the big data dream, and and we used to talk, if you didn't have a big data problem, if you weren't full of data, you weren't really getting it. Now people have all the data, and they got to stand this up. >> Yeah. >> So the analogy is again, the mobile, I like the mobile movement, and using mobile as an analogy, most companies were not building for a mobile environment, right? They were just building for the web, and legacy way of doing apps. And as soon as the user expectations shifted, that my expectation now, I need to be able to do my job on this small screen, on the mobile device with a touchscreen. Everybody had to invest in re-architecting, and re-implementing every single app, to fit within that model, and that model of interaction. And we are seeing the exact same thing happen now. And one of the core things we're focused on at Vectara, is how to simplify that for organizations, because a lot of them are overwhelmed by large language models, and ML. >> They don't have the staff. >> Yeah, yeah, yeah. They're understaffed, they don't have the skills. >> But they got developers, they've got DevOps, right? >> Yes. >> So they have the DevSecOps going on. >> Exactly, yes. >> So our goal is to simplify it enough for them that they can start leveraging this technology effectively, within their applications. >> Ed, you're the COO of the company, obviously a startup. You guys are growing. You got great backup, and good team. You've also done a lot of business development, and technical business development in this area. If you look at the landscape right now, and I agree the apps are coming, every company I talk to, that has that jet chatGPT of, you know, epiphany, "Oh my God, look how cool this is. "Like magic." Like okay, it's code, settle down. >> Mm hmm. >> But everyone I talk to is using it in a very horizontal way. I talk to a very senior person, very tech alpha geek, very senior person in the industry, technically. they're using it for log data, they're using it for configuration of routers. And in other areas, they're using it for, every vertical has a use case. So this is horizontally scalable from a use case standpoint. When you hear horizontally scalable, first thing I chose in my mind is cloud, right? >> Mm hmm. >> So cloud, and scalability that way. And the data is very specialized. So now you have this vertical specialization, horizontally scalable, everyone will be refactoring. What do you see, and what are you seeing from customers, that you talk to, and prospects? >> Yeah, I mean put yourself in the shoes of an application developer, who is actually trying to make their application a bit more like magic. And to have that soon-to-be, honestly, expected experience. They've got to think about things like performance, and how efficiently that they can actually execute a query, or a question. They've got to think about cost. Generative isn't cheap, like the inference of it. And so you've got to be thoughtful about how and when you take advantage of it, you can't use it as a, you know, everything looks like a nail, and I've got a hammer, and I'm going to hit everything with it, because that will be wasteful. Developers also need to think about how they're going to take advantage of, but not lose their own data. So there has to be some controls around what they feed into the large language model, if anything. Like, should they fine tune a large language model with their own data? Can they keep it logically separated, but still take advantage of the powers of a large language model? And they've also got to take advantage, and be aware of the fact that when data is generated, that it is a different class of data. It might not fully be their own. >> Yeah. >> And it may not even be fully verified. And so when the logical cycle starts, of someone making a request, the relationship between that request, and the output, those things have to be stored safely, logically, and identified as such. >> Yeah. >> And taken advantage of in an ongoing fashion. So these are mega problems, each one of them independently, that, you know, you can think of it as middleware companies need to take advantage of, and think about, to help the next wave of application development be logical, sensible, and effective. It's not just calling some raw API on the cloud, like openAI, and then just, you know, you get your answer and you're done, because that is a very brute force approach. >> Well also I will point, first of all, I agree with your statement about the apps experience, that's going to be expected, form filling. Great point. The interesting about chatGPT. >> Sorry, it's not just form filling, it's any action you would like to take. >> Yeah. >> Instead of clicking, and dragging, and dropping, and doing it on a menu, or on a touch screen, you just say it, and it's and it happens perfectly. >> Yeah. It's a different interface. And that's why I love that UIUX experiences, that's the people falling out of their chair moment with chatGPT, right? But a lot of the things with chatGPT, if you feed it right, it works great. If you feed it wrong and it goes off the rails, it goes off the rails big. >> Yes, yes. >> So the the Bing catastrophes. >> Yeah. >> And that's an example of garbage in, garbage out, classic old school kind of comp-side phrase that we all use. >> Yep. >> Yes. >> This is about data in injection, right? It reminds me the old SQL days, if you had to, if you can sling some SQL, you were a magician, you know, to get the right answer, it's pretty much there. So you got to feed the AI. >> You do, Some people call this, the early word to describe this as prompt engineering. You know, old school, you know, search, or, you know, engagement with data would be, I'm going to, I have a question or I have a query. New school is, I have, I have to issue it a prompt, because I'm trying to get, you know, an action or a reaction, from the system. And the active engineering, there are a lot of different ways you could do it, all the way from, you know, raw, just I'm going to send you whatever I'm thinking. >> Yeah. >> And you get the unintended outcomes, to more constrained, where I'm going to just use my own data, and I'm going to constrain the initial inputs, the data I already know that's first party, and I trust, to, you know, hyper constrain, where the application is actually, it's looking for certain elements to respond to. >> It's interesting Amr, this is why I love this, because one we are in the media, we're recording this video now, we'll stream it. But we got all your linguistics, we're talking. >> Yes. >> This is data. >> Yep. >> So the data quality becomes now the new intellectual property, because, if you have that prompt source data, it makes data or content, in our case, the original content, intellectual property. >> Absolutely. >> Because that's the value. And that's where you see chatGPT fall down, is because they're trying to scroll the web, and people think it's search. It's not necessarily search, it's giving you something that you wanted. It is a lot of that, I remember in Cloudera, you said, "Ask the right questions." Remember that phrase you guys had, that slogan? >> Mm hmm. And that's prompt engineering. So that's exactly, that's the reinvention of "Ask the right question," is prompt engineering is, if you don't give these models the question in the right way, and very few people know how to frame it in the right way with the right context, then you will get garbage out. Right? That is the garbage in, garbage out. But if you specify the question correctly, and you provide with it the metadata that constrain what that question is going to be acted upon or answered upon, then you'll get much better answers. And that's exactly what we solved Vectara. >> Okay. So before we get into the last couple minutes we have left, I want to make sure we get a plug in for the opportunity, and the profile of Vectara, your new company. Can you guys both share with me what you think the current situation is? So for the folks who are now having those moments of, "Ah, AI's bullshit," or, "It's not real, it's a lot of stuff," from, "Oh my god, this is magic," to, "Okay, this is the future." >> Yes. >> What would you say to that person, if you're at a cocktail party, or in the elevator say, "Calm down, this is the first inning." How do you explain the dynamics going on right now, to someone who's either in the industry, but not in the ropes? How would you explain like, what this wave's about? How would you describe it, and how would you prepare them for how to change their life around this? >> Yeah, so I'll go first and then I'll let Ed go. Efficiency, efficiency is the description. So we figured that a way to be a lot more efficient, a way where you can write a lot more emails, create way more content, create way more presentations. Developers can develop 10 times faster than they normally would. And that is very similar to what happened during the Industrial Revolution. I always like to look at examples from the past, to read what will happen now, and what will happen in the future. So during the Industrial Revolution, it was about efficiency with our hands, right? So I had to make a piece of cloth, like this piece of cloth for this shirt I'm wearing. Our ancestors, they had to spend month taking the cotton, making it into threads, taking the threads, making them into pieces of cloth, and then cutting it. And now a machine makes it just like that, right? And the ancestors now turned from the people that do the thing, to manage the machines that do the thing. And I think the same thing is going to happen now, is our efficiency will be multiplied extremely, as human beings, and we'll be able to do a lot more. And many of us will be able to do things they couldn't do before. So another great example I always like to use is the example of Google Maps, and GPS. Very few of us knew how to drive a car from one location to another, and read a map, and get there correctly. But once that efficiency of an AI, by the way, behind these things is very, very complex AI, that figures out how to do that for us. All of us now became amazing navigators that can go from any point to any point. So that's kind of how I look at the future. >> And that's a great real example of impact. Ed, your take on how you would talk to a friend, or colleague, or anyone who asks like, "How do I make sense of the current situation? "Is it real? "What's in it for me, and what do I do?" I mean every company's rethinking their business right now, around this. What would you say to them? >> You know, I usually like to show, rather than describe. And so, you know, the other day I just got access, I've been using an application for a long time, called Notion, and it's super popular. There's like 30 or 40 million users. And the new version of Notion came out, which has AI embedded within it. And it's AI that allows you primarily to create. So if you could break down the world of AI into find and create, for a minute, just kind of logically separate those two things, find is certainly going to be massively impacted in our experiences as consumers on, you know, Google and Bing, and I can't believe I just said the word Bing in the same sentence as Google, but that's what's happening now (all laughing), because it's a good example of change. >> Yes. >> But also inside the business. But on the crate side, you know, Notion is a wiki product, where you try to, you know, note down things that you are thinking about, or you want to share and memorialize. But sometimes you do need help to get it down fast. And just in the first day of using this new product, like my experience has really fundamentally changed. And I think that anybody who would, you know, anybody say for example, that is using an existing app, I would show them, open up the app. Now imagine the possibility of getting a starting point right off the bat, in five seconds of, instead of having to whole cloth draft this thing, imagine getting a starting point then you can modify and edit, or just dispose of and retry again. And that's the potential for me. I can't imagine a scenario where, in a few years from now, I'm going to be satisfied if I don't have a little bit of help, in the same way that I don't manually spell check every email that I send. I automatically spell check it. I love when I'm getting type ahead support inside of Google, or anything. Doesn't mean I always take it, or when texting. >> That's efficiency too. I mean the cloud was about developers getting stuff up quick. >> Exactly. >> All that heavy lifting is there for you, so you don't have to do it. >> Right? >> And you get to the value faster. >> Exactly. I mean, if history taught us one thing, it's, you have to always embrace efficiency, and if you don't fast enough, you will fall behind. Again, looking at the industrial revolution, the companies that embraced the industrial revolution, they became the leaders in the world, and the ones who did not, they all like. >> Well the AI thing that we got to watch out for, is watching how it goes off the rails. If it doesn't have the right prompt engineering, or data architecture, infrastructure. >> Yes. >> It's a big part. So this comes back down to your startup, real quick, I know we got a couple minutes left. Talk about the company, the motivation, and we'll do a deeper dive on on the company. But what's the motivation? What are you targeting for the market, business model? The tech, let's go. >> Actually, I would like Ed to go first. Go ahead. >> Sure, I mean, we're a developer-first, API-first platform. So the product is oriented around allowing developers who may not be superstars, in being able to either leverage, or choose, or select their own large language models for appropriate use cases. But they that want to be able to instantly add the power of large language models into their application set. We started with search, because we think it's going to be one of the first places that people try to take advantage of large language models, to help find information within an application context. And we've built our own large language models, focused on making it very efficient, and elegant, to find information more quickly. So what a developer can do is, within minutes, go up, register for an account, and get access to a set of APIs, that allow them to send data, to be converted into a format that's easy to understand for large language models, vectors. And then secondarily, they can issue queries, ask questions. And they can ask them very, the questions that can be asked, are very natural language questions. So we're talking about long form sentences, you know, drill down types of questions, and they can get answers that either come back in depending upon the form factor of the user interface, in list form, or summarized form, where summarized equals the opportunity to kind of see a condensed, singular answer. >> All right. I have a. >> Oh okay, go ahead, you go. >> I was just going to say, I'm going to be a customer for you, because I want, my dream was to have a hologram of theCUBE host, me and Dave, and have questions be generated in the metaverse. So you know. (all laughing) >> There'll be no longer any guests here. They'll all be talking to you guys. >> Give a couple bullets, I'll spit out 10 good questions. Publish a story. This brings the automation, I'm sorry to interrupt you. >> No, no. No, no, I was just going to follow on on the same. So another way to look at exactly what Ed described is, we want to offer you chatGPT for your own data, right? So imagine taking all of the recordings of all of the interviews you have done, and having all of the content of that being ingested by a system, where you can now have a conversation with your own data and say, "Oh, last time when I met Amr, "which video games did we talk about? "Which movie or book did we use as an analogy "for how we should be embracing data science, "and big data, which is moneyball," I know you use moneyball all the time. And you start having that conversation. So, now the data doesn't become a passive asset that you just have in your organization. No. It's an active participant that's sitting with you, on the table, helping you make decisions. >> One of my favorite things to do with customers, is to go to their site or application, and show them me using it. So for example, one of the customers I talked to was one of the biggest property management companies in the world, that lets people go and rent homes, and houses, and things like that. And you know, I went and I showed them me searching through reviews, looking for information, and trying different words, and trying to find out like, you know, is this place quiet? Is it comfortable? And then I put all the same data into our platform, and I showed them the world of difference you can have when you start asking that question wholeheartedly, and getting real information that doesn't have anything to do with the words you asked, but is really focused on the meaning. You know, when I asked like, "Is it quiet?" You know, answers would come back like, "The wind whispered through the trees peacefully," and you know, it's like nothing to do with quiet in the literal word sense, but in the meaning sense, everything to do with it. And that that was magical even for them, to see that. >> Well you guys are the front end of this big wave. Congratulations on the startup, Amr. I know you guys got great pedigree in big data, and you've got a great team, and congratulations. Vectara is the name of the company, check 'em out. Again, the startup boom is coming. This will be one of the major waves, generative AI is here. I think we'll look back, and it will be pointed out as a major inflection point in the industry. >> Absolutely. >> There's not a lot of hype behind that. People are are seeing it, experts are. So it's going to be fun, thanks for watching. >> Thanks John. (soft music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>> Presenter: theCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Hey guys and girls. Welcome back to theCube's live coverage at Palo Alto Ignite '22. We're live at the MGM Grand Hotel in beautiful Las Vegas. Lisa Martin here with Dave Vellante. This is day one of our coverage. We've been talking with execs from Palo Alto, Partners, but one of our most exciting things is talking with Founders day. We get to do that next. >> The thing is, it's like I wrote this weekend in my breaking analysis. Understanding the problem in cybersecurity is really easy, but figuring out how to fix it ain't so much. >> It definitely isn't. >> So I'm excited to have Nir here. >> Very excited. Nir Zuk joins us, the founder and CTO of Palo Alto Networks. Welcome, Nir. Great to have you on the program. >> Thank you. >> So Palo Alto Networks, you founded it back in 2005. It's hard to believe that's been 18 years, almost. You did something different, which I want to get into. But tell us, what was it back then? Why did you found this company? >> I thought the world needed another cybersecurity company. I thought it's because there were so many cybersecurity vendors in the world, and just didn't make any sense. This industry has evolved in a very weird way, where every time there was a new challenge, rather than existing vendors dealing with a challenge, you had new vendors dealing with it, and I thought I could put a stop to it, and I think I did. >> You did something differently back in 2005, looking at where you are now, the leader, what was different in your mind back then? >> Yeah. When you found a new company, you have really two good options. There's also a bad option, but we'll skip that. You can either disrupt an existing market, or you can create a new market. So first, I decided to disrupt an existing market, go into an existing market first, network security, then cyber security, and change it. Change the way it works. And like I said, the challenges that every problem had a new vendor, and nobody just stepped back and said, "I think I can solve it with the platform." Meaning, I think I can spend some time not solving a specific problem, but building a platform that then can be used to solve many different problems. And that's what I've done, and that's what Palo Alto Networks has done, and that's where we are today. >> So you look back, you call it now, I think you call it a next gen firewall, but nothing in 2005, can it be next gen? Do you know the Silicon Valley Show? Do you know the show Silicon Valley? >> Oh! Yeah. >> Yeah, of course. >> You got to have a box. But it was a different kind of box- >> Actually. >> Explain that. >> Actually, it's exactly the same thing. You got to have a box. So I actually wanted to call it a necessary evil. Marketing wouldn't go for that. >> No. >> And the reason I wanted to call it a necessary evil, because one of the things that we've done in order to platform our cyber security, again, first network security now, also cloud security, and security operations, is to turn it into a SaaS delivered industry. Today every cyber security professional knows that, when they buy cyber security, they buy usually a SaaS delivered service. Back then, people thought I was crazy to think that customers are going to send their data to their vendor in order to process, and they wanted everything on premise and so on, but I said, "No, customers are going to send information to us for processing, because we have much more processing power than they have." And we needed something in the infrastructure to send us the information. So that's why I wanted to call it the necessary evil. We ended up calling it next generation firewall, which was probably a better term. >> Well, even Veritas. Remember Veritas? They had the no hardware agenda. Even they have a box. So it is like you say, you got to have it. >> It's necessary. >> Okay. You did this, you started this on your own cloud, kind of like Salesforce, ServiceNow. >> Correct. >> Similar now- >> Build your own data centers. >> Build your own data center. Okay, I call it a cloud, but no. >> No, it's the same. There's no cloud, it's just someone else's computer. >> According to Larry Ellison, he was actually probably right about that. But over time, you've had this closer partnership with the public clouds. >> Correct. >> What does that bring you and your customers, and how hard was that to navigate? >> It wasn't that hard for us, because we didn't have that many services. Usually it's harder. Of course, we didn't do a lift and shift, which is their own thing to do with the cloud. We rebuild things for the cloud, and the benefits, of course, are time to market, scale, agility, and in some cases also, cost. >> Yeah, some cases. >> In some cases. >> So you have a sort of a hybrid model today. You still run your own data centers, do you not? >> Very few. >> Really? >> There are very, very few things that we have to do on hardware, like simulating malware and things that cannot be done in a virtual machine, which is pretty much the only option you have in the cloud. They provide bare metal, but doesn't serve our needs. I think that we don't view cloud, and your viewers should not be viewing cloud, as a place where they're going to save money. It's a place where they're going to make money. >> I like that. >> You make much more money, because you're more agile. >> And that's why this conversation is all about, your cost of goods sold they're going to be so high, you're going to have to come back to your own data centers. That's not on your mind right now. What's on your mind is advancing the unit, right? >> Look, my own data center would limit me in scale, would limit my agility. If you want to build something new, you don't have all the PaaS services, the platform as a service, services like database, and AI, and so on. I have to build them myself. It takes time. So yeah, it's going to be cheaper, but I'm not going to be delivering the same thing. So my revenues will be much lower. >> Less top line. What can humans do better than machines? You were talking about your keynote... I'm just going to chat a little bit. You were talking about your keynote. Basically, if you guys didn't see the keynote, that AI is going to run every soc within five years, that was a great prediction that you made. >> Correct. >> And they're going to do things that you can't do today, and then in the future, they're going to do things that you can't... Better than you can do. >> And you just have to be comfortable with that. >> So what do you think humans can do today and in the future better than machines? >> Look, humans can always do better than machines. The human mind can do things that machines cannot do. We are conscious, I don't think machines will be conscious. And you can do things... My point was not that machines can do things that humans cannot do. They can just do it better. The things that humans do today, machines can do better, once machines do that, humans will be free to do things that they don't do today, that machines cannot do. >> Like what? >> Like finding the most difficult, most covert attacks, dealing with the most difficult incidents, things that machines just can't do. Just that today, humans are consumed by finding attacks that machines can find, by dealing with incidents that machines can deal with. It's a waste of time. We leave it to the machines and go and focus on the most difficult problems, and then have the machines learn from you, so that next time or a hundred or a thousand times from now, they can do it themselves, and you focus on the even more difficult. >> Yeah, just like after 9/11, they said that we lack the creativity. That's what humans have, that machines don't, at least today. >> Machines don't. Yeah, look, every airplane has two pilots, even though airplanes have been flying themselves for 30 years now, why do you have two pilots, to do the things that machines cannot do? Like land on the Hudson, right? You always need humans to do the things that machines cannot do. But to leave the things that machines can do to the machines, they'll do it better. >> And autonomous vehicles need breaks. (indistinct) >> In your customer conversations, are customers really grappling with that, are they going, "Yeah, you're right?" >> It depends. It's hard for customers to let go of old habits. First, the habit of buying a hundred different solutions from a hundred different vendors, and you know what? Why would I trust one vendor to do everything, put all my eggs in the same basket? They have all kind of slogans as to why not to do that, even though it's been proven again and again that, doing everything in one system with one brain, versus a hundred systems with a hundred brains, work much better. So that's one thing. The second thing is, we always have the same issue that we've had, I think, since the industrial revolution, of what machines are going to take away my job. No, they're just going to make your job better. So I think that some of our customers are also grappling with that, like, "What do I do if the machines take over?" And of course, like we've said, the machines aren't taking over. They're going to do the benign work, you're going to do the interesting work. You should embrace it. >> When I think about your history as a technology pro, from Check Point, a couple of startups, one of the things that always frustrated you, is when when a larger company bought you out, you ended up getting sucked into the bureaucratic vortex. How do you avoid that at Palo Alto Networks? >> So first, you mean when we acquire company? >> Yes. >> The first thing is that, when we acquire companies, we always acquire for integration. Meaning, we don't just buy something and then leave it on the side, and try to sell it here and there. We integrate it into the core of our products. So that's very important, so that the technology lives, thrives and continues to grow as part of our bigger platform. And I think that the second thing that is very important, from past experience what we've learned, is to put the people that we acquire in key positions. Meaning, you don't buy a company and then put the leader of that company five levels below the CEO. You always put them in very senior positions. Almost always, we have the leaders of the companies that we acquire, be two levels below the CEO, so very senior in the company, so they can influence and make changes. >> So two questions related to that. One is, as you grow your team, can you be both integrated? And second part of the question, can you be both integrated and best of breed? Second part of the question is, do you even have to be? >> So I'll answer it in the third way, which is, I don't think you can be best of breed without being integrated in cybersecurity. And the reason is, again, this split brain that I've mentioned twice. When you have different products do a part of cybersecurity and they don't talk to each other, and they don't share a single brain, you always compromise. You start looking for things the wrong way. I can be a little bit technical here, but please. Take the example of, traditionally you would buy an IDS/IPS, separately from your filtering, separately from DNS security. One of the most important things we do in network security is to find combining control connections. Combining control connections where the adversaries controlling something behind your firewall and is now going around your network, is usually the key heel of the attack. That's why attacks like ransomware, that don't have a commanding control connection, are so difficult to deal with, by the way. So commanding control connections are a key seal of the attacks, and there are three different technologies that deal with it. Neural filtering for neural based commanding control, DNS security for DNS based commanding control, and IDS/IPS for general commanding control. If those are three different products, they'll be doing the wrong things. The oral filter will try to find things that it's not really good at, that the IPS really need to find, and the DN... It doesn't work. It works much better when it's one product doing everything. So I think the choice is not between best of breed and integrated. I think the only choice is integrated, because that's the only way to be best of breed. >> And behind that technology is some kind of realtime data store, I'll call it data lake, database. >> Yeah. >> Whatever. >> It's all driven by the same data. All the URLs, all the domain graph. Everything goes to one big data lake. We collect about... I think we collect about, a few petabytes per day. I don't write the exact number of data. It's all going to the same data lake, and all the intelligence is driven by that. >> So you mentioned in a cheeky comment about, why you founded the company, there weren't enough cybersecurity companies. >> Yeah. >> Clearly the term expansion strategy that Palo Alto Networks has done has been very successful. You've been, as you talked about, very focused on integration, not just from the technology perspective, but from the people perspective as well. >> Correct. >> So why are there still so many cybersecurity companies, and what are you thinking Palo Alto Networks can do to change that? >> So first, I think that there are a lot of cybersecurity companies out there, because there's a lot of money going into cybersecurity. If you look at the number of companies that have been really successful, it's a very small percentage of those cybersecurity companies. And also look, we're not going to be responsible for all the innovation in cybersecurity. We need other people to innovate. It's also... Look, always the question is, "Do you buy something or do you build it yourself?" Now we think we're the smartest people in the world. Of course, we can build everything, but it's not always true that we can build everything. Know that we're the smartest people in the world, for sure. You see, when you are a startup, you live and die by the thing that you build. Meaning if it's good, it works. If it's not good, you die. You run out of money, you shut down, and you just lost four years of your life to this, at least. >> At least. >> When you're a large company, yeah, I can go and find a hundred engineers and hire them. And especially nowadays, it becomes easier, as it became easier, and give them money, and have them go and build the same thing that the startup is building, but they're part of a bigger company, and they'll have more coffee breaks, and they'll be less incentive to go and do that, because the company will survive with or without them. So that's why startups can do things much better, sometimes than larger companies. We can do things better than startups, when it comes to being data driven because we have the data, and nobody can compete against the amount of data that we have. So we have a good combination of finding the right startups that have already built something, already proven that it works with some customers, and of course, building a lot of things internally that we cannot do outside. >> I heard you say in one of the, I dunno, dozens of videos I've listened to you talked to. The industry doesn't need or doesn't want another IoT stovepipe. Okay, I agree. So you got on-prem, AWS, Azure, Google, maybe Alibaba, IoT is going to be all over the place. So can you build, I call it the security super cloud, in other words, a consistent experience with the same policies and edicts across all my estates, irrespective of physical location? Is that technically feasible? Is it what you are trying to do? >> Certainly, what we're trying to do with Prisma Cloud, with our cloud security product, it works across all the clouds that you mentioned, and Oracle as well. It's almost entirely possible. >> Almost. >> Almost. Well, the things that... What you do is you normalize the language that the different cloud scale providers use, into one language. This cloud calls it a S3, and so, AWS calls it S3, and (indistinct) calls it GCS, and so on. So you normalize their terminology, and then build policy using a common terminology that your customers have to get used to. Of course, there are things that are different between the different cloud providers that cannot be normalized, and there, it has to be cloud specific. >> In that instance. So is that, in part, your strategy, is to actually build that? >> Of course. >> And does that necessitate running on all the major clouds? >> Of course. It's not just part of our strategy, it's a major part of our strategy. >> Compulsory. >> Look, as a standalone vendor that is not a cloud provider, we have two advantages. The first one is we're security product, security focused. So we can do much better than them when it comes to security. If you are a AWS, GCP, Azure, and so on, you're not going to put your best people on security, you're going to put them on the core business that you have. So we can do much better. Hey, that's interesting. >> Well, that's not how they talk. >> I don't care how they talk. >> Now that's interesting. >> When something is 4% of your business, you're not going to put it... You're not going to put your best people there. It's just, why would you? You put your best people on 96%. >> That's not driving their revenue. >> Look, it's simple. It's not what we- >> With all due respect. With all due respect. >> So I think we do security much better than them, and they become the good enough, and we become the premium. But certainly, the second thing that give us an advantage and the right to be a standalone security provider, is that we're multicloud, private cloud and all the major cloud providers. >> But they also have a different role. I mean, your role is not the security, the Nitro card or the Graviton chip, or is it? >> They are responsible for securing up to the operating system. We secure everything. >> They do a pretty good job of that. >> No, they do, certainly they have to. If they get bridged at that level, it's not just that one customer is going to suffer, the entire customer base. They have to spend a lot of time and money on it, and frankly, that's where they put their best security people. Securing the infrastructure, not building some cloud security feature. >> Absolutely. >> So Palo Alto Networks is, as we wrap here, on track to nearly double its revenues to nearly seven billion in FY '23, just compared to 2020, you were quoted in the press by saying, "We will be the first $100 billion cyber company." What is next for Palo Alto to achieve that? >> Yeah, so it was Nikesh, our CEO and chairman, that was quoted saying that, "We will double to a hundred billion." I don't think he gave it a timeframe, but what it takes is to double the sales, right? We're at 50 billion market cap right now, so we need to double sales. But in reality, you mentioned that we're growing the turn by doing more and more cybersecurity functions, and taking away pieces. Still, we have a relatively small, even though we're the largest cybersecurity vendor in the world, we have a very low market share that shows you how fragmented the market is. I would also like to point out something that is less known. Part of what we do with AI, is really take the part of the cybersecurity industry, which are service oriented, and that's about 50% of the cybersecurity industry services, and turn it into products. I mean, not all of it. But a good portion of what's provided today by people, and tens of billions of dollars are spent on that, can be done with products. And being one of the very, very few vendors that do that, I think we have a huge opportunity at turning those tens of billions of dollars in human services to AI. >> It's always been a good business taking human labor and translating into R and D, vendor R and D. >> Especially- >> It never fails if you do it well. >> Especially in difficult times, difficult economical times like we are probably experiencing right now around the world. We, not we, but we the world. >> Right, right. Well, congratulations. Coming up on the 18th anniversary. Tremendous amount of success. >> Thank you. >> Great vision, clear vision, STEM expansion strategy, really well underway. We are definitely going to continue to keep our eyes. >> Big company, a hundred billion, that's market capital, so that's a big company. You said you didn't want to work for a big company unless you founded it, is that... >> Unless it acts like a small company. >> There's the caveat. We'll keep our eye on that. >> Thank you very much. >> It's such a pleasure having you on. >> Thank you. >> Same here, thank you. >> All right, for our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live emerging and enterprise tech coverage. (upbeat music)

>>Hey everyone. Welcome back to Miami, Miami Beach. Specifically, not a bad location to have a conference. Lisa Martin here with the Cube live at IFS Unleashed. We're gonna be having a great conversation next about Ization moments of Service Rules. Royces here, as is the C of IFS for aerospace and defense. Scott Camani. Nick Ward joins us as well, the VP of Digital Systems at Roll Royce. Guys, excited to have you on the program and welcome back. >>Thank you very much. Nice to be back. It's >>Been three years since the last IFS show. I love How's Scott? I was talking with Darren Roots earlier today and I said, Well, didn't it used to be IFS world? And he said, Yes. And I said, I love the name. I would love to, to unpack that with your cheek marketing officer because it, there's a lot of, of, of power behind Unleash. A lot of companies do such and such world or accelerate, but we're talking about unleashing the power of the technology to help customers deliver those moments of service. Yes. Love it. So Scott, start us off here. Talk about ization. That's a relatively new term to me. Sure. Help me understand what it means, because IFS is a pioneer in this sense. >>We are. So one of the things that IFS is always trying to do is to try to find a way to help our customers to realize a moment of service. And that moment of service is really when they found the ability to delight their customers. And when we look at the way in which we're trying to drive those business outcomes for our customers, ization seems to be at the core of it. So whether it's the ability for a company to use a product, a service, or an outcome, they're driving ization in a way where they're shaping their business. They're orchestrating their customers and their people and their assets behind a val value chain that helps them to provide a delightful experience for their customers. And with IFS being focused on Lifecycle asset management, we no longer have customers that have to choose from best of suite or best of breed. They can actually have both with ifs. And that's something we're really excited to provide to our customers and more excited for our customers to realize that value with their customers, their partners. Along the way. >>You, you mentioned customer delight and it's a term that we, we all use it, right? But there's so much power and, and capabilities and metrics behind that phrase, customer delight, which will unpack Nick bringing you into the conversation. Talk to us a little bit about what your role is at Rolls Royce. My first thought when I saw you was, oh, the fancy cars, but we're talking about aerospace and the fence, so give us a little bit of a history. >>Okay. So yes, we don't make cars is the first point. So we are, we are power, we do power as a service. So we are most well known, I guess for large aircraft airliners. You know, if you've, if you've flown here to Miami, there's probably a 50 50 chance you've flown on a Rod Roy powered aircraft. Our market segment is what we call wide bodied aircraft where you go on, there's two aisles. So the larger section of the market, and we, we provide power, so we provide the engines, but more importantly, we've been a ization company, a service company for at least two decades. We, we have a, a service relationship we call total care. And the whole idea of total care is, yes, I have my engine, it's on my aircraft, but I take care of it. I make sure it's available to fly when you need to fly it. And all of the things that have to come together to make that happen, it's a service company. >>Service company. Talk to me a little bit about, and I wanna get got your perspective as well, but the relationship that Roll Royce and IFS have this is a little bit unique. >>Well, I can start, but I I think Nick's gonna be better served to tell us about that as our customer. Nick and I actually started this journey about four years ago, and what we did was, is we were working closely with our perspective customer Rolls-Royce identified what they were looking for as a desired business outcome. And then we found a way through the technology and the software that we provide to all of our enterprise customers globally to find a solution that actually helped to provide a, an outcome not only to Rolls-Royce, but also to our collective downstream customers, commercial operators around the globe. So that's where we started the journey and we're continuing our discussions around other solutions, but that's how we started and it's been an incredible partnership. We're so happy and proud to have Nick as a customer and a advocate of all things ifs and I'll let him kind of continue from his point of view how he sees the partnership in the relationship. >>No, thank you Scott. I think we've, we've always, we've valued the kind of relationship that we have because I think IFS has always got Rolls Royce in terms of strategic direction. What do we try to do? I said, we're a service company. You know, we, we are, we have to have a service relationship with our, our customers, our airlines. To have a service relationship, you have to be able to connect to your service customer. And ifs is a big part of how we connect for data. That's how do we understand what the airline is doing with the engines, but it's also how we return data back into the airline. So we are, we're get a very close integrated relation between us, our airlines, through a bridge that, that ifs create through the maintenance product. Got it. So it works really well. >>I I think I'd make one other point. One of the things that we've always focused on is quantifiable business value. The only way a partnership like this could possibly work is if we have a desired business outcome, but if we're providing value, So the value work that we did in conjunction with Rolls Royce and really identifying that helped to support the business case that allowed this partnership to really begin and flourish. So I I, I'd be remiss if I didn't mention that business value element that's really core to everything we do and all the, the conversations that Nick and I have. >>Well, it's all about outcomes. Absolutely. It's all about outcomes. It >>Is, it has to be about, it's about moments of service, right? That's why we're here, right? So perhaps a moment of service for Robs Royce is every time you're a passenger, you're going through the terminal. You expect your aircraft to be there, ready, waiting for you to get on and depart on time. And our moment of service is every aircraft takes off on time, every time we live. When we die by the quality of that statement, how well we live up to that statement, I think I checked this morning, there's something alike, 600 aircraft in the sky right now with Rolls Royce power carrying passengers. All of those passengers have relied on that moment. Service happening regularly like clockwork. Every single time you don't get any forgiveness for a delay, you get very little forgiveness for a cancellation that has to happen. And then so many things have to come together for that to happen. >>Those 600 aircraft, that's maybe 200,000 people right now in the sky, Wow. Those 200,000 people are trying to connect, They're trying to connect with friends, they're trying to connect with loved ones, family, colleagues, whatever the purpose is of that trip. It's really important to them. And we just have to make sure that that happens for us. We've had something like a million flights so far this year, 300 million people relying on that moment of so is happening. So I really resonate with, with the language that Scott users about the importance of sort of that focal point on when does it all come together? It comes together when as a passenger, I get on the plane and it goes and I get no issues. >>Right. Well people don't tolerate fragmented experiences anymore. No, no. I think one of the things that was in short supply during the pandemic was patience and tolerance. Sure. Not sure how much of that's gonna come back, right? But those integrated connected experiences, as you described so eloquently, Nick, those are table stakes for the customers, but also the brands behind them because of customers are unhappy, the churn rates go way up. And you see that reflected in obviously the success of the business and what you guys are doing together is seems to be quite powerful. Now then when you were on the cube with us three years ago in Boston at IFS back then you first introduced the intelligent engine and the Blue Data thread. Let's talk about the intelligent engine. Just give our audience a refresher of what that actually entails. >>So perhaps if we just step one one step back for that, just to understand how this fits in. So Roro is a service organization. We talked about that. What that means is we take a lot of the, the risk and the uncertainty away from our airline customers on the availability, the costs and maintenance effort associated with having a, having a chat engine. These are incredibly complicated and complex and sophisticated pieces of equipment. The most expensive, most sophisticated pieces of an aircraft. Managing that is, is difficult. And every airline does not want to have to focus on that. They wanna focus on being able to get the passenger on the air after, fly it, look after the airframe. So our role in that is to take that risk away, is to manage those engines, look after their health, look after their life, make sure they're available to fly whenever they need to fly. >>So for us to understand that, we then have to have data, we have to understand the state of every engine, where it is, the health of the engine, the life of that engine, what do we need to do next to that engine? And we can't do that unless we have data and that data flows into a digital platform. The intelligent engine, which is our cloud based ai, big data, all of the iot, all of the big buzzwords are there, right? So the data flows into that, that lets us run the models. It lets us understand, I can see something maybe it's a, it's a small issue, but if I leave it alone, it become a bigger issue. And maybe that will cause disruption further down the line. So we need to understand that we need to preempt it. So preemptive predictive maintenance is a, is a big part of the intelligent engine, but it's more than just that. >>It's also, we can understand how that engine is being flown. We can understand is it having a really intense flight? Is it having a more benign, gentle flight? Wow. That change time after the flight, typically after the flight. But what that means is we can then understand, actually we can keep that engine on the wing longer then you might otherwise have to do, If you have no data, you have to be conservative, safety rules, everything. Sure. So data allows you to say, actually I'm being overly conservative in this space. I can get more flying bios, flying hours from my product by extending the interval between maintenance and the intelligent engine has a large part to play in us justifying that we're able to do that. And then the final part that it does is eventually the engine is gonna have to come off from maintenance. >>These things fly 5 million miles between overhauls. You imagine you try to do that in your family car. It's, it doesn't happen. It's incredibly sophisticated thing can fly 5 million miles and then we take it off for a major overhaul. But there are thousands of these engines in the fleet. We have to understand which engine is going to come off when for what reason, and prepare our maintenance network to then receive the engine and deal with it and get it back to the customer. So the intelligent engine has a massive part to play in understanding the maintenance demand that the flying fleet is then creating. >>Wow, that's fascinating. And so you talked about that three years ago. What's next for that? I imagine there's only more evolution that's gonna happen. >>It keeps growing. It keeps growing. It's driven by the data. The more data we have, the more that we can do with that. I think as well that, you know, one of the big places that we've we've gone is you can do as much predictive analytics as you, like, there's a lot of people we'll talk about doing predictive analytics, but if you don't do the hard yards of turning predictive analytics into outcome Yeah. Then what did you get? You, you got a bit of smart advice. So we, we take that maintenance demand, we then have to understand how that drives the orchestration and the management of all the parts, the people, the work scope definition, the allocating an engine into a maintenance slot, exactly when it's gonna go. And what are we gonna do to, how do we control and manage our inventory to make sure that engine is gonna go through. >>How do we then actually execute the work inside our, our our overall shops? How do we get that engine back and and integrate our logistics process. So the intelligent engine is, if you like, the shiny front end of a process, it's all the buzzwords, but actually the hard yards behind the scene is just as if not more important to get right. And again, this is why I really like the moment of service concept. Because without that, the moment of service doesn't happen. The engine's not there, the part wasn't there. The field service maintenance guy wasn't there to go fix it. >>And brands are affected >>An, an aircraft on the ground earns no revenue for anybody. No. It's, it's a cost. It's it's a big sink of cost. It >>Is, it is. Absolutely. >>And you're helping aircraft only earn engines only earn when they fly. Yeah, >>Yeah. Absolutely. And what a fascinating, the intelligent engine. Scott, talk a little bit about, we talking about power, we can't not talk about sustainability. Yes, I understand that IFS has a new inaugural awards program that Rolls Roys was a recipient of the Change for Good sustainability awards. Congratulations. Thank you very much. And to Scott, talk to me a little bit about the Change for Good program sustainability program. What types of organizations across the industries of expertise are you looking for and why does Rules ROY really highlight what a winner embodies? >>So since Darren has joined IFS as the ceo, he's had a lot of intentional areas that we focused on. And sustainability has been one that's at the top of the list. IFS has a US ambassador Lewis Pew, who's our Chief Sustainability officer, and he helps us to provide worldwide coverage of the efforts around sustainability. So it's not just about ifss ability to become a more sustainable organization, but it's the solutions that IFS is putting together in the five verticals that we focus on that can help those organizations achieve a level of sustainability for their, for their downstream customers, their partners, and for their enterprises themselves. So when we look at, you know, the social ability for us to be more conscientious about leaving the world a better place or trying to do our best to leave the world not as bad as we came into it, sustainability is a real focus for us. And, you know, the way in which we can support an organization like Rolls Royce and Nickel obviously share those areas of focus from Rolls Royce. It's a perfect fit. And congratulations again for the award. Thank you. We're, we're, we're so excited to, to have shared that with you. We have some other customers that have achieved it across different categories, but it's an area of current and continuous focus for ifs. >>Nick, talk to us, take us out here as our last question is the, the focus on sustainability at Rolls Royce. Talk to us a little bit about that and what some of the major efforts are that you've got underway. >>I think, you know, very similar as, as, as Scott taught there, the focus within Rolls Royce as a strategic group level is really high aviation particularly, I mean we're a, we're an engineering company. We're a power company. Power inherently consumes natural resources. It tends to generate climate affecting outcomes. But at the same time, we are an innovative organization and if anybody's gonna help solve climate challenges, it's gonna be organizations like Rolls Royce who are able to bring different technologies into the market. So we have a responsibility to manage and, and optimize the behavior of our, our existing product suite. But we also have a, a vested interest in trying to move aviation on into the next, the next phase. We talk about sustainable aviation. Aviation has to earn the right to exist. People have choices. We've come out of covid, people are used to doing zoom and not flying. >>People are used to doing things when they don't necessarily get on an aircraft and do something. The aviation business always has to earn the right from the public to exist. And increasingly people will make choices about how they fly when they fly, how far they fly based on the sustainability footprint. So it's really important to us to help both our customers operate the aircraft in as sustainable and climate friendly way as we can. It's really important to find those, those balance points between the cost of an operation and it's the impact of an operation. If you go all over and say, I am going to be net, well, not even net to, but zero carbon by almost inference, that means I'm not gonna operate. You have to operate to get to an outcome. But how do I do that? Why I manage my cost, I manage the, the profitability, the organization doing it, right? >>So it has to be financially sustainable, it has to be sustainable for the people operating within it. It has to be sustainable for the planet, right? So we do that in lots of different ways in small places and, and in big places. So small things we do is we help the operator understand if you change your flight profile, you'll generate fewer emissions. You may avoid controls if you flying a different way, maybe you create trails, you'll lose, you'll lose less fuel while you're doing that. So it's cost effective for you. There was always a balance point there between the wear and tear on the engine versus the, the, the environmental impact. And you find that optimum place. One of the first things we started doing with, with Scott is we have a, a way that we life our engine components. And one of the very simple outcomes of that is using that data, the blue data for connection to the customer. >>If we can see, effectively see inside the engine about how well it's wearing and we can extend those maintenance intervals as we talked about, what that eventually does is it reduces the need to take the engine off, ship it around the world. Probably on a great big 7, 4 7 or maybe year or two ago on an Anson off four big engines flying a long distance trek, shipping our engine to an overhaul facility. We're avoiding something like 200 of those shop visit overhauls a year. So every year that's 200 flights there and back again, which don't happen, right? Collectively that's around about 15,000 automobile equivalent emissions just don't happen. So simple things we can do just starts to have accumulative effect, >>Right? Simple things that you're doing that, that have a huge impact. We could talk for so much longer on stability, I'm sure we're out of time, but I can see why Roll Royce was, was the winner of the Inocular award. Congratulations. Well deserved. Well >>Deserved. I well >>Deserved. So interesting to hear about the intelligent engine. So you're gonna have to come back. Hopefully we'll be here next year and we can hear more of the evolution. Cuz I have a feeling there's never a dual moment in what you're doing. >>It's never a dull moment. There's never an end point. >>No. >>Okay, >>Going Scott, Nick, thank you so much for joining me on the program today. Thank you, Lisa. It's great to have you talk through what's going on at ifx and the partnership with Rolls Royce. We >>Appreciate, and again, Nick, Nick, thank you for your continued support in the partnership. >>I thank you, Scott. We appreciate it. Likewise, thank >>You. Kudos all around. All right, for my guests, I'm Lisa Martin, you're watching a Cube live from Miami. We're at IFS unleashed. We'll be back shortly after a break with our next guests. So stick around.

the cyber security landscape has changed dramatically over the past 24 to 36 months rapid cloud migration has created a new layer of security defense sure but that doesn't mean csos can relax in many respects it further complicates or at least changes the ciso's scope of responsibilities in particular the threat surface has expanded and that creates more seams and cisos have to make sure their teams pick up where the hyperscaler clouds leave off application developers have become a critical execution point for cyber assurance shift left is the kind of new buzz phrase for devs but organizations still have to shield right meaning the operational teams must continue to partner with secops to make sure infrastructure is resilient so it's no wonder that in etr's latest survey of nearly 1500 cios and it buyers that business technology executives cite security as their number one priority well ahead of other critical technology initiatives including collaboration software cloud computing and analytics rounding out the top four but budgets are under pressure and csos have to prioritize it's not like they have an open checkbook they have to contend with other key initiatives like those just mentioned to secure the funding and what about zero trust can you go out and buy xero trust or is it a framework a mindset in a series of best practices applied to create a security consciousness throughout the organization can you implement zero trust in other words if a machine or human is not explicitly allowed access then access is denied can you implement that policy without constricting organizational agility the question is what's the most practical way to apply that premise and what role does infrastructure play as the enforcer how does automation play in the equation the fact is that today's approach to cyber resilient type resilience can't be an either or it has to be an and conversation meaning you have to ensure data protection while at the same time advancing the mission of the organization with as little friction as possible and don't even talk to me about the edge that's really going to keep you up at night hello and welcome to the special cube presentation a blueprint for trusted infrastructure made possible by dell technologies in this program we explore the critical role that trusted infrastructure plays in cyber security strategies how organizations should think about the infrastructure side of the cyber security equation and how dell specifically approaches securing infrastructure for your business we'll dig into what it means to transform and evolve toward a modern security infrastructure that's both trusted and agile first up are pete gear and steve kenniston they're both senior cyber security consultants at dell technologies and they're going to talk about the company's philosophy and approach to trusted infrastructure and then we're going to speak to paris arcadi who's a senior consultant for storage at dell technologies to understand where and how storage plays in this trusted infrastructure world and then finally rob emsley who heads product marketing for data protection and cyber security he's going to take a deeper dive with rob into data protection and explain how it has become a critical component of a comprehensive cyber security strategy okay let's get started pete gear steve kenniston welcome to the cube thanks for coming into the marlboro studios today great to be here dave thanks dave good to see you great to see you guys pete start by talking about the security landscape you heard my little rap up front what are you seeing i thought you wrapped it up really well and you touched on all the key points right technology is ubiquitous today it's everywhere it's no longer confined to a monolithic data center it lives at the edge it lives in front of us it lives in our pockets and smartphones along with that is data and as you said organizations are managing sometimes 10 to 20 times the amount of data that they were just five years ago and along with that cyber crime has become a very profitable enterprise in fact it's been more than 10 years since uh the nsa chief actually called cyber crime the biggest transfer of wealth in history that was 10 years ago and we've seen nothing but accelerating cyber crime and really sophistication of how those attacks are perpetrated and so the new security landscape is really more of an evolution we're finally seeing security catch up with all of the technology adoption all the build out the work from home and work from anywhere that we've seen over the last couple of years we're finally seeing organizations and really it goes beyond the i t directors it's a board level discussion today security's become a board level discussion yeah i think that's true as well it's like it used to be the security was okay the secops team you're responsible for security now you've got the developers are involved the business lines are involved it's part of onboarding for most companies you know steve this concept of zero trust it was kind of a buzzword before the pandemic and i feel like i've often said it's now become a mandate but it's it's it's still fuzzy to a lot of people how do you guys think about zero trust what does it mean to you how does it fit yeah i thought again i thought your opening was fantastic in in this whole lead into to what is zero trust it had been a buzzword for a long time and now ever since the federal government came out with their implementation or or desire to drive zero trust a lot more people are taking a lot more seriously because i don't think they've seen the government do this but ultimately let's see ultimately it's just like you said right if if you don't have trust to those particular devices uh applications or data you can't get at it the question is and and you phrase it perfectly can you implement that as well as allow the business to be as agile as it needs to be in order to be competitive because we're seeing with your whole notion around devops and the ability to kind of build make deploy build make deploy right they still need that functionality but it also needs to be trusted it needs to be secure and things can't get away from you yeah so it's interesting we attended every uh reinforce since 2019 and the narrative there is hey everything in this in the cloud is great you know and this narrative around oh security is a big problem is you know doesn't help the industry the fact is that the big hyperscalers they're not strapped for talent but csos are they don't have the the capabilities to really apply all these best practices they're they're playing whack-a-mole so they look to companies like yours to take their r your r d and bake it into security products and solutions so what are the critical aspects of the so-called dell trusted infrastructure that we should be thinking about yeah well dell trusted infrastructure for us is a way for us to describe uh the the work that we do through design development and even delivery of our it system so dell trusted infrastructure includes our storage it includes our servers our networking our data protection our hyper converged everything that infrastructure always has been it's just that today customers consume that infrastructure at the edge as a service in a multi-cloud environment i mean i view the cloud as really a way for organizations to become more agile and to become more flexible and also to control costs i don't think organizations move to the cloud or move to a multi-cloud environment to enhance security so i don't see cloud computing as a panacea for security i see it as another attack surface and another uh aspect in front that organizations and and security organizations and departments have to manage it's part of their infrastructure today whether it's in their data center in a cloud or at the edge i mean i think it's a huge point because a lot of people think oh data's in the cloud i'm good it's like steve we've talked about oh why do i have to back up my data it's in the cloud well you might have to recover it someday so i don't know if you have anything to add to that or any additional thoughts on it no i mean i think i think like what pete was saying when it comes to when it comes to all these new vectors for attack surfaces you know people did choose the cloud in order to be more agile more flexible and all that did was open up to the csos who need to pay attention to now okay where can i possibly be attacked i need to be thinking about is that secure and part of the part of that is dell now also understands and thinks about as we're building solutions is it is it a trusted development life cycle so we have our own trusted development life cycle how many times in the past did you used to hear about vendors saying you got to patch your software because of this we think about what changes to our software and what implementations and what enhancements we deliver can actually cause from a security perspective and make sure we don't give up or or have security become a whole just in order to implement a feature we got to think about those things yeah and as pete alluded to our secure supply chain so all the way through knowing what you're going to get when you actually receive it is going to be secure and not be tampered with becomes vitally important and pete and i were talking earlier when you have tens of thousands of devices that need to be delivered whether it be storage or laptops or pcs or or whatever it is you want to be you want to know that that that those devices are can be trusted okay guys maybe pete you could talk about the how dell thinks about it's its framework and its philosophy of cyber security and then specifically what dell's advantages are relative to the competition yeah definitely dave thank you so we've talked a lot about dell as a technology provider but one thing dell also is is a partner in this larger ecosystem we realize that security whether it's a zero trust paradigm or any other kind of security environment is an ecosystem uh with a lot of different vendors so we look at three areas one is protecting data in systems we know that it starts with and ends with data that helps organizations combat threats across their entire infrastructure and what it means is dell's embedding security features consistently across our portfolios of storage servers networking the second is enhancing cyber resiliency over the last decade a lot of the funding and spending has been in protecting or trying to prevent cyber threats not necessarily in responding to and recovering from threats right we call that resiliency organizations need to build resiliency across their organization so not only can they withstand a threat but they can respond recover and continue with their operations and the third is overcoming security complexity security is hard it's more difficult because of the things we've talked about about distributed data distributed technology and and attack surfaces everywhere and so we're enabling organizations to scale confidently to continue their business but know that all all the i.t decisions that they're making um have these intrinsic security features and are built and delivered in a consistent security so those are kind of the three pillars maybe we could end on what you guys see as the key differentiators that people should know about that that dell brings to the table maybe each of you could take take a shot at that yeah i think first of all from from a holistic portfolio perspective right the uh secure supply chain and the secure development life cycle permeate through everything dell does when building things so we build things with security in mind all the way from as pete mentioned from from creation to delivery we want to make sure you have that that secure device or or asset that permeates everything from servers networking storage data protection through hyper converge through everything that to me is really a key asset because that means you can you understand when you receive something it's a trusted piece of your infrastructure i think the other core component to think about and pete mentioned as dell being a partner for making sure you can deliver these things is that even though those are that's part of our framework these pillars are our framework of how we want to deliver security it's also important to understand that we are partners and that you don't need to rip and replace but as you start to put in new components you can be you can be assured that the components that you're replacing as you're evolving as you're growing as you're moving to the cloud as you're moving to a more on-prem type services or whatever that your environment is secure i think those are two key things got it okay pete bring us home yeah i think one of one of the big advantages of dell is our scope and our scale right we're a large technology vendor that's been around for decades and we develop and sell almost every piece of technology we also know that organizations are might make different decisions and so we have a large services organization with a lot of experienced services people that can help customers along their security journey depending on whatever type of infrastructure or solutions that they're looking at the other thing we do is make it very easy to consume our technology whether that's traditional on-premise in a multi-cloud environment uh or as a service and so the best of breed technology can be consumed in any variety of fashion and know that you're getting that consistent secure infrastructure that dell provides well and dell's forgot the probably top supply chain not only in the tech business but probably any business and so you can actually take take your dog food and then and allow other billionaire champagne sorry allow other people to you know share share best practices with your with your customers all right guys thanks so much for coming thank you appreciate it okay keep it right there after this short break we'll be back to drill into the storage domain you're watching a blueprint for trusted infrastructure on the cube the leader in enterprise and emerging tech coverage be right back concern over cyber attacks is now the norm for organizations of all sizes the impact of these attacks can be operationally crippling expensive and have long-term ramifications organizations have accepted the reality of not if but when from boardrooms to i.t departments and are now moving to increase their cyber security preparedness they know that security transformation is foundational to digital transformation and while no one can do it alone dell technologies can help you fortify with modern security modern security is built on three pillars protect your data and systems by modernizing your security approach with intrinsic features and hardware and processes from a provider with a holistic presence across the entire it ecosystem enhance your cyber resiliency by understanding your current level of resiliency for defending your data and preparing for business continuity and availability in the face of attacks overcome security complexity by simplifying and automating your security operations to enable scale insights and extend resources through service partnerships from advanced capabilities that intelligently scale a holistic presence throughout it and decades as a leading global technology provider we'll stop at nothing to help keep you secure okay we're back digging into trusted infrastructure with paris sarcadi he's a senior consultant for product marketing and storage at dell technologies parasaur welcome to the cube good to see you great to be with you dave yeah coming from hyderabad awesome so i really appreciate you uh coming on the program let's start with talking about your point of view on what cyber security resilience means to to dell generally but storage specifically yeah so for something like storage you know we are talking about the data layer name and if you look at cyber security it's all about securing your data applications and infrastructure it has been a very mature field at the network and application layers and there are a lot of great technologies right from you know enabling zero trust advanced authentications uh identity management systems and so on and and in fact you know with the advent of you know the the use of artificial intelligence and machine learning really these detection tools for cyber securities have really evolved in the network and the application spaces so for storage what it means is how can you bring them to the data layer right how can you bring you know the principles of zero trust to the data layer uh how can you leverage artificial intelligence and machine learning to look at you know access patterns and make intelligent decisions about maybe an indicator of a compromise and identify them ahead of time just like you know how it's happening and other ways of applications and when it comes to cyber resilience it's it's basically a strategy which assumes that a threat is imminent and it's a good assumption with the severity of the frequency of the attacks that are happening and the question is how do we fortify the infrastructure in the switch infrastructure to withstand those attacks and have a plan a response plan where we can recover the data and make sure the business continuity is not affected so that's uh really cyber security and cyber resiliency and storage layer and of course there are technologies like you know network isolation immutability and all these principles need to be applied at the storage level as well let me have a follow up on that if i may the intelligence that you talked about that ai and machine learning is that do you do you build that into the infrastructure or is that sort of a separate software module that that points at various you know infrastructure components how does that work both dave right at the data storage level um we have come with various data characteristics depending on the nature of data we developed a lot of signals to see what could be a good indicator of a compromise um and there are also additional applications like cloud iq is the best example which is like an infrastructure wide health monitoring system for dell infrastructure and now we have elevated that to include cyber security as well so these signals are being gathered at cloud iq level and other applications as well so that we can make those decisions about compromise and we can either cascade that intelligence and alert stream upstream for uh security teams um so that they can take actions in platforms like sign systems xtr systems and so on but when it comes to which layer the intelligence is it has to be at every layer where it makes sense where we have the information to make a decision and being closest to the data we have we are basically monitoring you know the various parallels data access who is accessing um are they crossing across any geo fencing uh is there any mass deletion that is happening or a mass encryption that is happening and we are able to uh detect uh those uh patterns and flag them as indicators of compromise and in allowing automated response manual control and so on for it teams yeah thank you for that explanation so at dell technologies world we were there in may it was one of the first you know live shows that that we did in the spring certainly one of the largest and i interviewed shannon champion and a huge takeaway from the storage side was the degree to which you guys emphasized security uh within the operating systems i mean really i mean powermax more than half i think of the features were security related but also the rest of the portfolio so can you talk about the the security aspects of the dell storage portfolio specifically yeah yeah so when it comes to data security and broadly data availability right in the context of cyber resiliency dell storage this you know these elements have been at the core of our um a core strength for the portfolio and the source of differentiation for the storage portfolio you know with almost decades of collective experience of building highly resilient architectures for mission critical data something like power max system which is the most secure storage platform for high-end enterprises and now with the increased focus on cyber security we are extending those core technologies of high availability and adding modern detection systems modern data isolation techniques to offer a comprehensive solution to the customer so that they don't have to piece together multiple things to ensure data security or data resiliency but a well-designed and well-architected solution by design is delivered to them to ensure cyber protection at the data layer got it um you know we were talking earlier to steve kenniston and pete gear about this notion of dell trusted infrastructure how does storage fit into that as a component of that sort of overall you know theme yeah and you know and let me say this if you could adjust because a lot of people might be skeptical that i can actually have security and at the same time not constrict my organizational agility that's old you know not an ore it's an end how do you actually do that if you could address both of those that would be great definitely so for dell trusted infrastructure cyber resiliency is a key component of that and just as i mentioned you know uh air gap isolation it really started with you know power protect cyber recovery you know that was the solution more than three years ago we launched and that was first in the industry which paved way to you know kind of data isolation being a core element of data management and uh for data infrastructure and since then we have implemented these technologies within different storage platforms as well so that customers have the flexibility depending on their data landscape they can approach they can do the right data isolation architecture right either natively from the storage platform or consolidate things into the backup platform and isolate from there and and the other key thing we focus in trusted infrastructure dell infra dell trusted infrastructure is you know the goal of simplifying security for the customers so one good example here is uh you know being able to respond to these cyber threats or indicators of compromise is one thing but an i.t security team may not be looking at the dashboard of the storage systems constantly right storage administration admins may be looking at it so how can we build this intelligence and provide this upstream platforms so that they have a single pane of glass to understand security landscape across applications across networks firewalls as well as storage infrastructure and in compute infrastructure so that's one of the key ways where how we are helping simplify the um kind of the ability to uh respond ability to detect and respond these threads uh in real time for security teams and you mentioned you know about zero trust and how it's a balance of you know not uh kind of restricting users or put heavy burden on you know multi-factor authentication and so on and this really starts with you know what we're doing is provide all the tools you know when it comes to advanced authentication uh supporting external identity management systems multi-factor authentication encryption all these things are intrinsically built into these platforms now the question is the customers are actually one of the key steps is to identify uh what are the most critical parts of their business or what are the applications uh that the most critical business operations depend on and similarly identify uh mission critical data where part of your response plan where it cannot be compromised where you need to have a way to recover once you do this identification then the level of security can be really determined uh by uh by the security teams by the infrastructure teams and you know another you know intelligence that gives a lot of flexibility uh for for even developers to do this is today we have apis um that so you can not only track these alerts at the data infrastructure level but you can use our apis to take concrete actions like blocking a certain user or increasing the level of authentication based on the threat level that has been perceived at the application layer or at the network layer so there is a lot of flexibility that is built into this by design so that depending on the criticality of the data criticality of the application number of users affected these decisions have to be made from time to time and it's as you mentioned it's it's a balance right and sometimes you know if if an organization had a recent attack you know the level of awareness is very high against cyber attacks so for a time you know these these settings may be a bit difficult to deal with but then it's a decision that has to be made by security teams as well got it so you're surfacing what may be hidden kpis that are being buried inside for instance the storage system through apis upstream into a dashboard so that somebody could you know dig into the storage tunnel extract that data and then somehow you know populate that dashboard you're saying you're automating that that that workflow that's a great example and you may have others but is that the correct understanding absolutely and it's a two-way integration let's say a detector an attack has been detected at a completely different layer right in the application layer or at a firewall we can respond to those as well so it's a two-way integration we can cascade things up as well as respond to threats that have been detected elsewhere um uh through the api that's great all right hey api for power skill is the best example for that uh excellent so thank you appreciate that give us the last word put a bow on this and and bring this segment home please absolutely so a dell storage portfolio um using advanced data isolation um with air gap having machine learning based algorithms to detect uh indicators of compromise and having rigor mechanisms with granular snapshots being able to recover data and restore applications to maintain business continuity is what we deliver to customers uh and these are areas where a lot of innovation is happening a lot of product focus as well as you know if you look at the professional services all the way from engineering to professional services the way we build these systems the way we we configure and architect these systems um cyber security and protection is a key focus uh for all these activities and dell.com securities is where you can learn a lot about these initiatives that's great thank you you know at the recent uh reinforce uh event in in boston we heard a lot uh from aws about you know detent and response and devops and machine learning and some really cool stuff we heard a little bit about ransomware but i'm glad you brought up air gaps because we heard virtually nothing in the keynotes about air gaps that's an example of where you know this the cso has to pick up from where the cloud leaves off but that was in front and so number one and number two we didn't hear a ton about how the cloud is making the life of the cso simpler and that's really my takeaway is is in part anyway your job and companies like dell so paris i really appreciate the insights thank you for coming on thecube thank you very much dave it's always great to be in these uh conversations all right keep it right there we'll be right back with rob emsley to talk about data protection strategies and what's in the dell portfolio you're watching thecube data is the currency of the global economy it has value to your organization and cyber criminals in the age of ransomware attacks companies need secure and resilient it infrastructure to safeguard their data from aggressive cyber attacks [Music] as part of the dell technologies infrastructure portfolio powerstor and powermax combine storage innovation with advanced security that adheres to stringent government regulations and corporate compliance requirements security starts with multi-factor authentication enabling only authorized admins to access your system using assigned roles tamper-proof audit logs track system usage and changes so it admins can identify suspicious activity and act with snapshot policies you can quickly automate the protection and recovery process for your data powermax secure snapshots cannot be deleted by any user prior to the retention time expiration dell technologies also make sure your data at rest stays safe with power store and powermax data encryption protects your flash drive media from unauthorized access if it's removed from the data center while adhering to stringent fips 140-2 security requirements cloud iq brings together predictive analytics anomaly detection and machine learning with proactive policy-based security assessments monitoring and alerting the result intelligent insights that help you maintain the security health status of your storage environment and if a security breach does occur power protect cyber recovery isolates critical data identifies suspicious activity and accelerates data recovery using the automated data copy feature unchangeable data is duplicated in a secure digital vault then an operational air gap isolates the vault from the production and backup environments [Music] architected with security in mind dell emc power store and powermax provides storage innovation so your data is always available and always secure wherever and whenever you need it [Music] welcome back to a blueprint for trusted infrastructure we're here with rob emsley who's the director of product marketing for data protection and cyber security rob good to see a new role yeah good to be back dave good to see you yeah it's been a while since we chatted last and you know one of the changes in in my world is that i've expanded my responsibilities beyond data protection marketing to also focus on uh cyber security marketing specifically for our infrastructure solutions group so certainly that's you know something that really has driven us to you know to come and have this conversation with you today so data protection obviously has become an increasingly important component of the cyber security space i i don't think necessarily of you know traditional backup and recovery as security it's to me it's an adjacency i know some companies have said oh yeah now we're a security company they're kind of chasing the valuation for sure bubble um dell's interesting because you you have you know data protection in the form of backup and recovery and data management but you also have security you know direct security capability so you're sort of bringing those two worlds together and it sounds like your responsibility is to to connect those those dots is that right absolutely yeah i mean i think that uh the reality is is that security is a a multi-layer discipline um i think the the days of thinking that it's one uh or another um technology that you can use or process that you can use to make your organization secure uh are long gone i mean certainly um you actually correct if you think about the backup and recovery space i mean people have been doing that for years you know certainly backup and recovery is all about the recovery it's all about getting yourself back up and running when bad things happen and one of the realities unfortunately today is that one of the worst things that can happen is cyber attacks you know ransomware malware are all things that are top of mind for all organizations today and that's why you see a lot of technology and a lot of innovation going into the backup and recovery space because if you have a copy a good copy of your data then that is really the the first place you go to recover from a cyber attack and that's why it's so important the reality is is that unfortunately the cyber criminals keep on getting smarter i don't know how it happens but one of the things that is happening is that the days of them just going after your production data are no longer the only challenge that you have they go after your your backup data as well so over the last half a decade dell technologies with its backup and recovery portfolio has introduced the concept of isolated cyber recovery vaults and that is really the you know we've had many conversations about that over the years um and that's really a big tenant of what we do in the data protection portfolio so this idea of of cyber security resilience that definition is evolving what does it mean to you yeah i think the the analyst team over at gartner they wrote a very insightful paper called you will be hacked embrace the breach and the whole basis of this analysis is so much money has been spent on prevention is that what's out of balance is the amount of budget that companies have spent on cyber resilience and cyber resilience is based upon the premise that you will be hacked you have to embrace that fact and be ready and prepared to bring yourself back into business you know and that's really where cyber resiliency is very very different than cyber security and prevention you know and i think that balance of get your security disciplines well-funded get your defenses as good as you can get them but make sure that if the inevitable happens and you find yourself compromised that you have a great recovery plan and certainly a great recovery plan is really the basis of any good solid data protection backup and recovery uh philosophy so if i had to do a swot analysis we don't have to do the wot but let's focus on the s um what would you say are dell's strengths in this you know cyber security space as it relates to data protection um one is we've been doing it a long time you know we talk a lot about dell's data protection being proven and modern you know certainly the experience that we've had over literally three decades of providing enterprise scale data protection solutions to our customers has really allowed us to have a lot of insight into what works and what doesn't as i mentioned to you one of the unique differentiators of our solution is the cyber recovery vaulting solution that we introduced a little over five years ago five six years parapatek cyber recovery is something which has become a unique capability for customers to adopt uh on top of their investment in dell technologies data protection you know the the unique elements of our solution already threefold and it's we call them the three eyes it's isolation it's immutability and it's intelligence and the the isolation part is really so important because you need to reduce the attack surface of your good known copies of data you know you need to put it in a location that the bad actors can't get to it and that really is the the the the essence of a cyber recovery vault interestingly enough you're starting to see the market throw out that word um you know from many other places but really it comes down to having a real discipline that you don't allow the security of your cyber recovery vault to be compromised insofar as allowing it to be controlled from outside of the vault you know allowing it to be controlled by your backup application our cyber recovery vaulting technology is independent of the backup infrastructure it uses it but it controls its own security and that is so so important it's like having a vault that the only way to open it is from the inside you know and think about that if you think about you know volts in banks or volts in your home normally you have a keypad on the outside think of our cyber recovery vault as having its security controlled from inside of the vault so nobody can get in nothing can get in unless it's already in and if it's already in then it's trusted exactly yeah exactly yeah so isolation is the key and then you mentioned immutability is the second piece yeah so immutability is is also something which has been around for a long time people talk about uh backup immunoability or immutable backup copies so immutability is just the the the additional um technology that allows the data that's inside of the vault to be unchangeable you know but again that immutability you know your mileage varies you know when you look across the uh the different offers that are out there in the market especially in the backup industry you make a very valid point earlier that the backup vendors in the market seems to be security washing their marketing messages i mean everybody is leaning into the ever-present danger of cyber security not a bad thing but the reality is is that you have to have the technology to back it up you know quite literally yeah no pun intended and then actually pun intended now what about the intelligence piece of it uh that's that's ai ml where does that fit for sure so the intelligence piece is delivered by um a solution called cybersense and cybersense for us is what really gives you the confidence that what you have in your cyber recovery vault is a good clean copy of data so it's looking at the backup copies that get driven into the cyber vault and it's looking for anomalies so it's not looking for signatures of malware you know that's what your antivirus software does that's what your endpoint protection software does that's on the prevention side of the equation but what we're looking for is we're looking to ensure that the data that you need when all hell breaks loose is good and that when you get a request to restore and recover your business you go right let's go and do it and you don't have any concern that what you have in the vault has been compromised so cyber sense is really a unique analytic solution in the market based upon the fact that it isn't looking at cursory indicators of of um of of of malware infection or or ransomware introduction it's doing full content analytics you know looking at you know has the data um in any way changed has it suddenly become encrypted has it suddenly become different to how it was in the previous scan so that anomaly detection is very very different it's looking for um you know like different characteristics that really are an indicator that something is going on and of course if it sees it you immediately get flagged but the good news is is that you always have in the vault the previous copy of good known data which now becomes your restore point so we're talking to rob emsley about how data protection fits into what dell calls dti dell trusted infrastructure and and i want to come back rob to this notion of and not or because i think a lot of people are skeptical like how can i have great security and not introduce friction into my organization is that an automation play how does dell tackle that problem i mean i think a lot of it is across our infrastructure is is security has to be built in i mean intrinsic security within our servers within our storage devices uh within our elements of our backup infrastructure i mean security multi-factor authentication you know elements that make the overall infrastructure secure you know we have capabilities that you know allow us to identify whether or not configurations have changed you know we'll probably be talking about that a little bit more to you later in the segment but the the essence is is um security is not a bolt-on it has to be part of the overall infrastructure and that's so true um certainly in the data protection space give us the the bottom line on on how you see dell's key differentiators maybe you could talk about dell of course always talks about its portfolio but but why should customers you know lead in to dell in in this whole cyber resilience space um you know staying on the data protection space as i mentioned the the the work we've been doing um to introduce this cyber resiliency solution for data protection is in our opinion as good as it gets you know the you know you've spoken to a number of our of our best customers whether it be bob bender from founders federal or more recently at delton allergies world you spoke to tony bryson from the town of gilbert and these are customers that we've had for many years that have implemented cyber recovery vaults and at the end of the day they can now sleep at night you know that's really the the peace of mind that they have is that the insurance that a data protection from dell cyber recovery vault a parapatex cyber recovery solution gives them you know really allows them to you know just have the assurance that they don't have to pay a ransom if they have a an insider threat issue and you know all the way down to data deletion is they know that what's in the cyber recovery vault is good and ready for them to recover from great well rob congratulations on the new scope of responsibility i like how you know your organization is expanding as the threat surface is expanding as we said data protection becoming an adjacency to security not security in and of itself a key component of a comprehensive security strategy rob emsley thank you for coming back in the cube good to see you again you too dave thanks all right in a moment i'll be back to wrap up a blueprint for trusted infrastructure you're watching the cube every day it seems there's a new headline about the devastating financial impacts or trust that's lost due to ransomware or other sophisticated cyber attacks but with our help dell technologies customers are taking action by becoming more cyber resilient and deterring attacks so they can greet students daily with a smile they're ensuring that a range of essential government services remain available 24 7 to citizens wherever they're needed from swiftly dispatching public safety personnel or sending an inspector to sign off on a homeowner's dream to protecting restoring and sustaining our precious natural resources for future generations with ever-changing cyber attacks targeting organizations in every industry our cyber resiliency solutions are right on the money providing the security and controls you need we help customers protect and isolate critical data from ransomware and other cyber threats delivering the highest data integrity to keep your doors open and ensuring that hospitals and healthcare providers have access to the data they need so patients get life-saving treatment without fail if a cyber incident does occur our intelligence analytics and responsive team are in a class by themselves helping you reliably recover your data and applications so you can quickly get your organization back up and running with dell technologies behind you you can stay ahead of cybercrime safeguarding your business and your customers vital information learn more about how dell technology's cyber resiliency solutions can provide true peace of mind for you the adversary is highly capable motivated and well equipped and is not standing still your job is to partner with technology vendors and increase the cost of the bad guys getting to your data so that their roi is reduced and they go elsewhere the growing issues around cyber security will continue to drive forward thinking in cyber resilience we heard today that it is actually possible to achieve infrastructure security while at the same time minimizing friction to enable organizations to move quickly in their digital transformations a xero trust framework must include vendor r d and innovation that builds security designs it into infrastructure products and services from the start not as a bolt-on but as a fundamental ingredient of the cloud hybrid cloud private cloud to edge operational model the bottom line is if you can't trust your infrastructure your security posture is weakened remember this program is available on demand in its entirety at thecube.net and the individual interviews are also available and you can go to dell security solutions landing page for for more information go to dell.com security solutions that's dell.com security solutions this is dave vellante thecube thanks for watching a blueprint for trusted infrastructure made possible by dell we'll see you next time

(upbeat music) >> Welcome back everyone to Supercloud22, I'm John Furrier, host of theCUBE here in Palo Alto. For this next ecosystem's segment we have Muddu Sudhakar, who is the co-founder and CEO of Aisera, a friend of theCUBE, Cube alumni, serial entrepreneur, multiple exits, been on multiple times with great commentary. Muddu, thank you for coming on, and supporting our- >> Also thank you for having me, John. >> Yeah, thank you. Great handshake there, I love to do it. One, I wanted you here because, two reasons, one is, congratulations on your new funding. >> Thank you. >> For $90 million, Series D funding. >> Series D funding. >> So, huge validation in this market. >> It is. >> You have been experienced software so, it's a real testament to your team. But also, you're kind of in the Supercloud vortex. This new wave that Supercloud is part of is, I call it the pretext to what's coming with multi-clouds. It is the next level. >> I see. >> Structural change and we have been reporting on it, Dave and I, and we are being challenged. So, we decided to open it up. >> Very good, I would love it. >> And have a conversation rather than waiting eight months to prove that we are right. Which, we are right, but that is a long story. >> You're always right. (both laughs) >> What do you think of Supercloud, that's going on? What is the big trend? Because its public cloud is great, so there is no conflict there. >> Right. >> It's got great business, it's integrated, IaaS, to SaaS, PaaS, all in the beginning, or the middle. All that is called good. Now you have on-premise high rate cloud. >> Right. >> Edge is right around the corner. Exploding in new capabilities. So, complexity is still here. >> That's right, I think, you nailed it. We talk about hybrid cloud, and multi cloud. Supercloud is kind of elevates the message even better. Because you still have to leave for some of our clouds, public clouds. There will be some of our clouds, still running on the Edge. That's where, the Edge cloud comes in. Some will still be on-prem. So, the Supercloud as a concept is beyond hybrid and multi cloud. To me, I will run some of our cloud on Amazon. Some could be on Aisera, some could be running only on Edge, right? >> Mm hm >> And we still have, what we call remote executors. Some leaders of service now. You have, what we call the mid-server, is what I think it was called. Where you put in a small code and run it. >> Yeah. >> So, I think all those things will be running on-prem environment and VMware cloud, et cetera. >> And if you look back at, I think it has been five years now, maybe four or five years since Andy Jassy at reInvent announced Outposts. Think that was the moment in time that Dave and I took this pause back and said "Okay, that's Amazon." who listens to their customers. Acknowledging Hybrid. >> Right. >> Then we saw the rise of Snowflakes, the Databricks, specialty clouds. You start to see people who are building on top of AWS. But at MongoDB, it is a database, now they are a full blown, large scale data platform. These companies took advantage of the public cloud to build, as Jerry Chen calls it, "Castles in the cloud." >> Right. >> That seems to be happening in all areas. What do you think about that? >> Right, so what is driving the cloud? To me, we talk about machine learning in AI, right? Versus clouded options. We used to call it lift and shift. The outposts and lift and shift. Initially this was to get the data into the cloud. I think if you see, the vendor that I like the most, is, I'm not picking any favorite but, Microsoft Azure, they're thinking like your Supercloud, right? Amazon is other things, but Azure is a lot more because they run on-prem. They are also on Azure CloudFront, Amazon CloudFront. So I think, Azure and Amazon are doing a lot more in the area of Supercloud. What is really helping is the machine learning environment, needs Superclouds. Because I will be running some on the Edge, some compute, some will be running on the public cloud, some could be running on my data center. So, I think the Supercloud is really suited for AI and automation really well. >> Yeah, it is a good point about Microsoft, too. And I think Microsoft's existing install base saved Azure. >> Okay. >> They brought Office 365, Sequel Server, cause their customers weren't leaving Microsoft. They had the productivity thing nailed down as well as the ability to catch up >> That's right. >> To AWS. So, natural extension to on-premise with Microsoft. >> I think... >> Tell us- >> Your Supercloud is what Microsoft did. Right? Azure. If you think of, like, they had an Office 365, their SharePoint, their Dynamics, taking all of those properties, running on the Azure. And still giving the migration path into a data center. Is Supercloud. So, the early days Supercloud came from Azure. >> Well, that's a good point, we will certainly debate that. I will also say that Snowflake built on AWS. >> That's right. >> Okay, and became a super powerhouse with the data business. As did Databricks. >> That's right. >> Then went to Azure >> That's right. >> So, you're seeing kind of the Playbook. >> Right. >> Go fast on Cloud Native, the native cloud. Get that fly wheel going, then get going, somewhere else. >> It is, and to that point I think you and me are talking, right? If you are to start at one cloud and go to another cloud, the amount of work as a vendor for us to use for implement. Today, like we use all three clouds, including the Gov Cloud. It's a lot of work. So, what will happen, the next toolkit we use? Even services like Elastic. People will not, the word commoditize, is not the word, but people will create an abstraction layer, even for S3. >> Explain that, explain that in detail. So, elastic? What do you mean by that? >> Yeah, so what that means is today, Elasticsearch, if you do an Elasticsearch on Amazon, if I go to Azure, I don't want enter another Elasticsearch layer. Ideally I want us to write an abstracted search layer. So, that when I move my services into a different cloud I don't want to re-compute and re-calculate everything. That's a lot of work. Particularly once you have a production customer, if I were to shift the workloads, even to the point of infrastructure, take S3, if I read infrastructure to S3 and tomorrow I go to Azure. Azure will have its own objects store. I don't want to re-validate that. So what will happen is digital component, Kubernetes is already there, we want storage, we want network layer, we want VPM services, elastic as well as all fundamental stuff, including MongoDB, should be abstracted to run. On the Superclouds. >> Okay, well that is a little bit of a unicorn fantasy. But let's break that down. >> Sure. >> Do you think that's possible? >> It is. Because I think, if I am on MongoDB, I should be able to give a horizontal layer to MongoDB that is optimized for all three of them. I don't want MongoDB. >> First of all, everyone will buy that. >> Sure. >> I'm skeptical that that's possible. Given where we are at right now. So, you're saying that a vendor will provide an abstraction layer. >> No, I'm saying that either MongoDB, itself will do it, or a third party layer will come as a service which will abstract all this layer so that we will write to an AP layer. >> So what do you guys doing? How do you handle multiple clouds? You guys are taking that burden on, because it makes sense, you should build the abstraction layer. Not rely on a third party vendor right? >> We are doing it because there is no third party available offer it. But if you offer a third party tomorrow, I will use that as a Supercloud service. >> If they're 100% reliable? >> That's right. That's exactly it. >> They have to do the work. >> They have to do the work because if today I am doing it because no one else is offering it- >> Okay so what people might not know is that you are an angel investor as well as an entrepreneur been very successful, so you're rich, you have a lot of money. If I were a startup and I said, Muddu, I want to build this abstraction layer. What would be funding advice that you would give me as an entrepreneur? As a company to do that? >> I would do it like an Apigee that Google acquired, you should create an Apigee-like layer, for infrastructure upfront services, I think that is a very good option. >> And you think that is viable? >> It is very much viable. >> Would that be part of Supercloud architecture, in your opinion? >> It is. Right? And that will abstract all the clouds to some level. Like it is like Kubernetes abstract, so that if I am running on Kubernetes I can transfer to any cloud. >> Yeah >> But that should go from computer into other infrastructures. >> It's seems to me, Muddu, and I want to get your thoughts about this whole Supercloud defacto standard opportunity. It feels like we are waiting for a moment where there is some sort of defacto unification, whether it is in the distraction layer, or a standards body. There is no W3C here going on. I mean, W3C was for web consortium, for world wide web. The Supercloud seems to be having the same impact the web had. Transformative, disruptive, re-factoring business operations. Is there a standardized body or an opportunity for a defacto? Like Kubernetes was a great example of a unification around something for orchestration. Is there a better version in the Supercloud model where we need a standard? >> Yes and no. The reason is because by the time you come to standard, take time to look what happened. First, we started with VMs, then became Docker and Containers then we came to Kubernetes. So it goes through a journey. I think the next few years will be stood on SuperCloud let's make customers happy, let's make enough services going, and then the standards will come. Standards will be almost 2-3 years later. So I don't think standards should happen right now. Right now, all we need is, we need enough start ups to create the super layer abstraction, with the goal in mind of AI automation. The reason, AI is because AI needs to be able to run that. Automated because running a work flow is, I can either run a workflow in the cloud services, I can run it on on-prem, I can run it on database, so you have two good applications, take AI and automation with Supercloud and make enough enough noise on that make enough applications, then the standards will come. >> On this project we have been with SuperCloud these past day we have heard a lot of people talking. The themes that developers are okay, they are doing great. Open source is booming. >> Yes >> Cloud Native's got major traction. Developers are going fast and they love it, shifting left, all these great things. They're putting a lot of data, DevOps and the security teams, they're the ones who are leveling up. We are hearing a lot of conversations around how they can be faster. What is your view on this as relative to that Supercloud nirvana getting there? How are DevOps and security teams leveling up to devs? >> A couple of things. I think that in the world of DevSecOps and security ops. The reason security is important, right? Given what is going on, but you don't need to do security the manual way. I think that whole new operation that you and me talked about, AI ops should happen. Where the AI ops is for service operation, for performance, for incident or for security. Nobody thinks of AI security. So, the DevOps people should think more world of AI ops, so that I can predict, prevent things before they happen. Then the security will be much better. So AI ops with Supercloud will probably be that nirvana. But that is what should happen. >> In the AI side of things, what you guys are doing, what are you learning, on scale, relative to data? Is there, you said machine learning needs data, it needs scale operation. What's your view on the automation piece of all this? >> I think to me, the data is the single, underrated, unsung kind of hero in the whole machine learning. Everyone talks about AI and machine learning algorithms. Algorithms are as important, but even more important is data. Lack of data I can't do algorithms. So my advice to customers is don't lose your data. That is why I see, Frank, my old boss, setting everything up into the data cloud, in Snowflake. Data is so important, store the data, analyze the data. Data is the new AI. You and me talk so many times- >> Yeah >> It's underrated, people are not anticipating how important it is. But the data is coming from logs, events, whether there is knowledge documents, any data in any form. I think keep the data, analyze the data, data patterns, and then things like SuperCloud can really take advantage of that. >> So, in the Supercloud equation one of the things that has come up is that the native clouds do great. Their IaaS to SaaS is interactions that solve a lot of problems. There is integration that is good. >> Right. >> Now when you go off cloud, you get regions, get latency issues- >> Right >> You have more complexity. So what's the trade off in the Supercloud journey, if you had to guess? And just thinking out loud here, what would be some of the architectural trade offs of how you do it, what's the sequence? What's the order of operations to get Superclouding going? >> Yeah, very good questions here. I think once you start going from the public cloud, the clouds there scale to lets say, even a regional data center onto an Edge, latency will kick in. The lack of computer function will kick in. So there I think everything should become asynchronous, right? You will run the application in a limited environment. You should anticipate for small memories, small compute, long latencies, but still following should happen. So some operations should become the old-school following, like, it's like the email. I send an email, it's an asynchronous thing, I made a sponsor, I think most of message passing should go back to the old-school architectures They should become asynchronous where thing can rely. I think, as long as algorithms can take that into Edge, I think that Superclouds can really bridge between the public cloud to the edge. >> Muddu, thanks for coming, we really appreciate your insights here. You've always been a great friend, great commentator. If you weren't the CEO and a famous angel investor, we would certainly love to have you as a theCUBE analyst, here on theCUBE. >> I am always available for you. (John laughs) >> When you retire, you can come back. Final point, we've got time left. We'll give you a chance to talk about the company. I'm really intrigued by the success of your ninety million dollar financing realm because we are in a climate where people aren't getting those kinds of investments. It's usually down-rounds. >> Okay >> 409 adjustments, people are struggling. You got an up-round and you got a big number. Why the success? What is going on with the company? Why are you guys getting such great validation? Goldman Sachs, Thoma Bravo, Zoom, these are big names, these are the next gen winners. >> It is. >> Why are they picking you? Why are they investing in you? >> I think it is not one thing, it is many things. First all, I think it is a four-year journey for us where we are right now. So, the company started late 2017. It is getting the right customers, partners, employees, team members. So it is a lot hard work went in. So a lot of thanks to the Aisera community for where we are. Why customers and where we are? Look, fundamentally there is a problem to solve. Like, what Aisera is trying to solve is can we automate customer service? Whether internal employees, external customer support. Do it for IT, HR, sales, marketing, all the way to ops. Like you talk about DevSecOps, I don't want thousands of tune ups for ops. If I can make that job better, >> Yeah >> I want to, any job I want to automate. I call it, elevate the human, right? >> Yeah. >> And that's the reason- >> 'Cause you're saying people have to learn specialty tools, and there are consequences to that. >> Right, and to me, people should focus on more important tasks and use AI as a tool to automate those things right? It's like thinking of offering Apple City as Alexa as a service, that is how we are trying to offer customer service, like, right? And if it can do that consistently, and reduce costs, cost is a big reason why customers like us a lot, we have eliminated the cost in this down economy, I will amplify our message even more, right? I am going to take a bite out of their expense. Whether it is tool expense, it's on resources. Second, is user productivity And finally, experience. People want experience. >> Final question, folks out there, first of all, what do you think about Supercloud? And if someone asks you what is this Supercloud thing? How would you answer? >> Supercloud, is, to me, beyond multi cloud and hybrid cloud. It is to bridge applications that are build in Supercloud can run on all clouds seamlessly. You don't need to compile them, re-clear them. Supercloud is one place to build, develop, and deploy. >> Great, Muddu. Thank you for coming on. Supercloud22 here breaking it down with the ecosystem commentary, we have a lot of people coming to the small group of experts in our network, bringing you in open conversation around the future of cloud computing and applications globally. And again, it is all about the next generation cloud. This is theCUBE, thanks for watching. (upbeat music)