>>Hi, welcome to this cube conversation here in Palo Alto, California. I'm John fur, host of the cube here at our headquarters on the west coast in Palo Alto, California. Got a great news guest here. Matt McGill, Wayne managing director of Madrona venture group is here with me on the big news and drone raising their record 690 million fund and partnering with their innovative founders. Matt, thanks for coming on and, and talking about the news and congratulations on the dry powder. >>Well, Hey, thanks so much, John. Appreciate you having me on the show. >>Well, great news here. Oley validation. We're in a new market. Everyone's talking about the new normal, we're talking about a recession inflation, but yet we've been reporting that this is kind of the first generation that cloud hyperscale economic scale and technical benefits have kind of hit any kind of economic downturn. If you go back to to 2008, our last downturn, the cloud really hasn't hit that tipping point. Now the innovation, as we've been reporting with our startup showcases and looking at the results from the hyperscalers, this funding news is kind of validation that the tech society intersection is working. You guys just get to the news 430 million in the Madrona fund nine and 200. And I think 60 million acceleration fund three, which means you're gonna go stay with your roots with seed early stage and then have some rocket fuel for kind of the accelerated expansion growth side of it. Not like late stage growth, but like scaling growth. This is kind of the news. Is that right? >>That's right. You know, we, we've had a long time strategy over 25 years here in Seattle of being early, early stage. You know, it's like our friends at Amazon like to say is, well, we're there at day one and we wanna help build companies for the long run for over 25 years. We've been doing that in Seattle. And I think one of the things we've realized, I mean, this is, these funds are the largest funds ever raised by a Seattle based venture capital firm and that's notable in and of itself. But we think that's the reason is because Seattle has continued to innovate in areas like consumer internet software cloud, of course, where the cloud capital of the world and increasingly the applications of machine learning. And so with all that combination, we believe there's a ton more companies to be built here in the Pacific Northwest and in Seattle in particular. And then through our acceleration fund where we're investing in companies anywhere in the country, in fact, anywhere in the world, those are the kinds of companies that want to have the Seattle point of view. They don't understand how to work with Amazon and AWS. They don't understand how to work with Microsoft and we have some unique relationships in those places and we think we can help them succeed in doing that. >>You know, it's notable that you guys in particular have been very close with Jeff Bayo Andy Jesse, and the success of ABUS as well as Microsoft. So, you know, Seattle has become cloud city. Everyone kind of knows that from a cloud perspective, obviously Microsoft's roots have been there for a long, long time. You go back, I mean, August capital, early days, funding Microsoft. You remember those days not to date myself, but you know, Microsoft kind of went up there and kind of established it a Amazon there as well. Now you got Google here, you got Facebook in the valley. You guys are now also coming down. This funding comes on the heels of you appointing a new managing director here in Palo Alto. This is now the migration of Madrona coming into the valley. Is that right? Is that what we're seeing? >>Well, I think what we're trying to do is bring the things that we know uniquely from Seattle and the companies here down to Silicon valley. We've got a terrific partner in Karama Hend, Andrew he's somebody that we have worked together with over the years, co-investing in companies. So we knew him really well. It was a bit opportunistic for us, but what we're hearing over and over again is a lot of these companies based in the valley, based in other parts of the country, they don't know really how to best work with the Microsoft and Amazons are understand the services that they offer. And, you know, we have that capability. We have those relationships. We wanna bring that to bear and helping build great companies. >>What is your expectation on the Silicon valley presence here? You can kind of give a hint here kind of a gateway to Seattle, but you got a lot of developers here. We just reported this morning that MEA just open source pie, torch to the Linux foundation again, and Mary material kind of trend we are seeing open source now has become there's no debate anymore has become the software industry. There's no more issue around that. This is real. I >>Think that's right. I mean, you know, once, you know, Satya became CEO, Microsoft, and they started embracing open source, you know, that was gonna be the last big tech holdout. We think open source is very interesting in terms of what it can produce and create in terms of next generation, innovative innovation. It's great to see companies like Facebook like Uber and others that have had a long track record of open source capabilities. But what we're also seeing is you need to build businesses around that, that a lot of enterprises don't wanna buy just the open source and stitch it together themselves. They want somebody to do it with them. And whether that's the way that, you know, companies like MongoDB have built that out over time or that's, you know, or elastic or, you know, companies like opt ML and our portfolio, or even the big cloud, you know, hyperscalers, you know, they are increasingly embracing open source and building finished services, managed services on top of it. So that's a big wave that we've been investing in for a number of years now and are highly confident gonna >>Continue. You know, I've been a big fan of Pacific Northwest for a while. You know, love going up there and talking to the folks at Microsoft and Amazon and AWS, but there's been a big trend in venture capital where a lot of the, the later stage folks, including private equity have come in, you seen tiger global even tiger global alumni, that the Cubs they call them, you know, they're coming down and playing in the early state and the results haven't been that good. You guys have had a track record in your success. Again, a hundred percent of your institutional investors have honed up with you on this two fund strategy of close to 700 million. What's this formula says, why aren't they winning what's is it, they don't have the ecosystem? Is it they're spraying and praying without a lot of discipline? What's the dynamic between the folks like Madrona, the Neas of the world who kind of come in and Sequoia who kind of do it right, right. Come in. And they get it done in the right way. The early stage. I just say the private equity folks, >>You know, I think that early stage venture is a local business. It is a geographically proximate business when you're helping incredible founders, try to really dial in that early founder market fit. This is before you even get to product market fit. And, and so the, the team building that goes on the talking to potential customers, the ITER iterating on business strategy, this is a roll up your sleeves kind of thing. It's not a financial transaction. And so what you're trying to do is have a presence and an understanding, a prepared mind of one of the big themes and the kinds of founders that with, you know, our encouragement and our help can go build lasting companies. Now, when you get to a, a, a later stage, you know, you get to that growth stage. It is generally more of a financial, you know, kind of engineering sort of proposition. And there's some folks that are great at that. What we do is we support these companies all the way through. We reserve enough capital to be with them at the seed stage, the series B stage the, you know, the crossover round before you go public, all of those sorts of things. And we love partnering with some of these other people, but there's a lot of heavy lifting at the early, early stages of a business. And it's, it's not, I think a model that everybody's architected to do >>Well, you know, trust becomes a big factor in all this. You kind of, when you talk about like that, I hear you speaking. It makes me think of like trusted advisor meets money, not so much telling people what to do. You guys have had a good track record and, and being added value, not values from track. And sometimes that values from track is getting in the way of the entrepreneur by, you know, running the certain meetings, driving board meetings and driving the agenda that you see to see that trend where people try too hard and that a force function, the entrepreneur we're living in a world now where everyone's talking to each other, you got, you know, there's no more glass door it's everyone's on Twitter, right? So you can see some move, someone trying to control the supply chain of talent by term sheet, overvaluing them. >>You guys are, have a different strategy. You guys have a network I've noticed that Madrona has attracted them high end talent coming outta Microsoft outta AWS season, season, senior talent. I won't say, you know, senior citizens, but you know, people have done things scaled up businesses, as well as attract young talent. Can you share with our audience that dynamic of the, the seasoned veterans, the systems thinkers, the ones who have been there done that built software, built teams to the new young entrepreneurs coming in, what's the dynamic, like, how do you guys look at at those networks? How do you nurture them? Could you share your, your strategy on how you're gonna pull all this together, going forward? >>You know, we, we think a lot about building the innovation ecosystem, like a phrase around here that you hear a lot is the bigger pie theory. How do we build the bigger pie? If we're focusing on building the bigger pie, there'll be plenty of that pie for Madrona Madrona companies. And in that mindset says, okay, how are we gonna invest in the innovation ecosystem? And then actually to use a term, you know, one of our founders who unfortunately passed away this year, Tom Aber, he had just written a book called flywheel. And I think it embodies this mindset that we have of how do you create that flywheel within a community? And of course, interestingly enough, I think Tom both learned and contributed to that. He was on the board of Amazon for almost 20 years in helping build some of the flywheels at Amazon. >>So that's what we carry forward. And we know that there's a lot of value in experiential learning. And so we've been fortunate to have some folks, you know, that have worked at some of those, you know, kind iconic companies, join us and find that they really love this company building journey. We've also got some terrific younger folks that have, you know, some very fresh perspectives and a lot of, a lot of creativity. And they're bringing that together with our team overall. And you know, what we really are trying to do at the end of the day is find incredible founders who wanna build something lasting, insignificant, and provide our kind of our time, our best ideas, our, our perspective. And of course our capital to help them be >>Successful. I love the ecosystem play. I think that's a human capital game too. I like the way you guys are thinking about that. I do wanna get your reaction, cause I know you're close to Amazon and Microsoft, but mainly Jeff Bezos as well. You mentioned your, your partner who passed away was on the board. A lot of great props on and tributes online. I saw that, I know I didn't know him at all. So I really can't comment, but I did notice that Bezos and, and jazz in particular were complimentary. And recently I just saw Bezos comment on Twitter about the, you know, the Lord of the rings movie. They're putting out the series and he says, you gotta have a team. That's kinda like rebels. I'm paraphrasing, cuz these folks never done a movie like this before. So they're, they're getting good props and reviews in this new world order where entrepreneurs gotta do things different. >>What's the one thing that you think entrepreneurs need to do different to make this next startup journey different and successful because the world is different. There's not a lot of press to relate to Andy Jassey even on stage last week in, in, in LA was kind of, he's not really revealing. He's on his talking points, message, the press aren't out there and big numbers anymore. And you got a lot of different go-to market strategies, omnichannel, social different ways to communicate to customers. Yeah. So product market fit is becomes big. So how do you see this new flywheel emerging for those entrepreneurs have to go out there, roll up their sleeves and make it happen. And what kind of resources do you think they need to be successful? What are you guys advocating? >>Well, you know, what's really interesting about that question is I've heard Jeff say many times that when people ask him, what's gotta be different. He, he reminds them to think about what's not gonna change. And he usually starts to then talk about things like price, convenience, and selection. Customer's never gonna want a higher price, less convenience, smaller selection. And so when you build on some of those principles of, what's not gonna change, it's easier for you to understand what could be changing as it relates to the differences. One of the biggest differences, I don't think any of us have fully figured out yet is what does it mean to be productive in a hybrid work mode? We happen to believe that it's still gonna have a kernel of people that are geographically close, that are part of the founding and building in the early stages of a company. >>And, and it's an and equation that they're going to also have people that are distributed around the country, perhaps around the world that are some of the best talent that they attract to their team. The other thing that I think coming back to what remains the same is being hyper focused on a certain customer and a certain problem that you're passionate about solving. And that's really what we look for when we look for this founder market fit. And it can be a lot of different things from the next generation water bottle to a better way to handle deep learning models and get 'em deployed in the cloud. If you've got that passion and you've got some inkling of the skill of how to build a better solution, that's never gonna go away. That's gonna be enduring, but exactly how you do that as a team in a hybrid world, I think that's gonna be different. >>Yeah. One thing that's not changing is that your investor, makeup's not changing a hundred percent of your existing institutional investors have signed back on with you guys and your oversubscribed, lot of demand. What is your flywheel success formula? Why is Tron is so successful? Can you share some feedback from your investors? What are they saying? Why are they re-upping share some inside baseball or anecdotal praise? >>Well, I think it's very kind to you to frame it that way. I mean, you know, it does for investors come back to performance. You know, these are university endowments and foundations that have a responsibility to, to generate great returns. And we understand that and we're very aligned with that. I think to be specific in the last couple years, they appreciated that we were also not holding onto our, our stocks forever, that we actually made some thoughtful decisions to sell some shares of companies like Smartsheet and snowflake and accolade in others, and actually distribute capital back to them when things were looking really, really good. But I think the thing, other thing that's very important here is that we've created a flywheel with our core strategy being Seattle based and then going out from there to try to find the best founders, build great companies with them, roll up our sleeves in a productive way and help them for the long term, which now leads to multiple generations of people, you know, at those companies. And beyond that we wanna be, you know, partner with and back again. And so you create this flywheel by having success with people in doing it in a respectful. And as you said earlier, a trusted way, >>What's the message for the Silicon valley crowd, obviously bay area, Silicon valley, Palo Alto office, and the center of it. Obviously you got them hybrid workforce hybrid venture model developing what's the goals. What's the message for Silicon valley? >>Well, our message for folks in Silicon valley is the same. It's always been, we we're excited to partner with them largely up here again, cause this is still our home base, but there'll be a, you know, select number of opportunities where we'll get a chance to partner together down in Silicon valley. And we think we bring something different with that deep understanding of cloud computing, that deep understanding of applied machine learning. And of course, some of our unique relationships up here that can be additive to what the they've already done. And some of them are just great partners and have built, you know, help build some really incredible companies over >>The years. Matt, I really appreciate you taking the time for this interview, given them big news. I guess the question on everyone's mind, certainly the entrepreneur's mind is how do I get some of that cash you have and put it into work for my opportunity. One what's the investment thesis can take a minute to put the plug in for the firm. What are you looking to invest in? What's the thesis? What kind of entrepreneurs you're looking for? I know fund one is seed fund nine is seed to, to a and B and the second one is beyond B and beyond for growth. What's the pitch. What's the pitch. >>Yeah. Well you can, you can think of us as you know, any stage from pre-seed to series seed. You know, we'll make a new investment in companies in all of those stages. You know, I think that, you know, the, the core pitch, you know, to us is, you know, your passion for the, for the problem that you're trying to, trying to get solved. And we're of course, very excited about that. And you know, at, at, at the end of the day, you know, if you want somebody that has a distinct point of view on the market that is based up here and can roll up their sleeves and work alongside you. We're, we're, we're the ones that are more than happy to do that. Proven track record of doing that for 25 plus years. And there's so much innovation ahead. There's so many opportunities to disrupt to pioneer, and we're excited to be a part of working with great founders to do that. >>Well, great stuff. We'll see you ATS reinvent coming up shortly and your annual get together. You always have your crew down there and, and team engaging with some of the cloud players as well. And looking forward to seeing how the Palo Alto team expands out. And Matt, thanks for coming on the cube. Appreciate your time. >>Thanks very much, John. Appreciate you having me look forward to seeing you at reinvent. >>Okay. Matt, Matt here with Madrona venture group, he's the partner managing partner Madrona group raises 690 million to fund nine and, and, and again, and big funds for accelerated growth fund. Three lot of dry powder. Again, entrepreneurship in technology is scaling. It's not going down. It's continuing to accelerate into this next generation super cloud multi-cloud hybrid cloud world steady state. This is the cubes coverage. I'm John for Silicon angle and host of the cube. Thanks for watching.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hi everyone. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020 virtual. We are the cube virtual I'm John ferry, your host with my coach, Dave Alante for keynote analysis from Swami's machine learning, all things, data huge. Instead of announcements, the first ever machine learning keynote at a re-invent Dave. Great to see you. Thanks Johnny. And from Boston, I'm here in Palo Alto. We're doing the cube remote cube virtual. Great to see you. >>Yeah, good to be here, John, as always. Wall-to-wall love it. So, so, John, um, how about I give you my, my key highlights from the, uh, from the keynote today, I had, I had four kind of curated takeaways. So the first is that AWS is, is really trying to simplify machine learning and use machine intelligence into all applications. And if you think about it, it's good news for organizations because they're not the become machine learning experts have invent machine learning. They can buy it from Amazon. I think the second is they're trying to simplify the data pipeline. The data pipeline today is characterized by a series of hyper specialized individuals. It engineers, data scientists, quality engineers, analysts, developers. These are folks that are largely live in their own swim lane. Uh, and while they collaborate, uh, there's still a fairly linear and complicated data pipeline, uh, that, that a business person or a data product builder has to go through Amazon making some moves to the front of simplify that they're expanding data access to the line of business. I think that's a key point. Is there, there increasingly as people build data products and data services that can monetize, you know, for their business, either cut costs or generate revenue, they can expand that into line of business where there's there's domain context. And I think the last thing is this theme that we talked about the other day, John of extending Amazon, AWS to the edge that we saw that as well in a number of machine learning tools that, uh, Swami talked about. >>Yeah, it was great by the way, we're live here, uh, in Palo Alto in Boston covering the analysis, tons of content on the cube, check out the cube.net and also check out at reinvent. There's a cube section as there's some links to so on demand videos with all the content we've had. Dave, I got to say one of the things that's apparent to me, and this came out of my one-on-one with Andy Jassy and Andy Jassy talked about in his keynote is he kind of teased out this idea of training versus a more value add machine learning. And you saw that today in today's announcement. To me, the big revelation was that the training aspect of machine learning, um, is what can be automated away. And it's under a lot of controversy around it. Recently, a Google paper came out and the person was essentially kind of, kind of let go for this. >>But the idea of doing these training algorithms, some are saying is causes more harm to the environment than it does good because of all the compute power it takes. So you start to see the positioning of training, which can be automated away and served up with, you know, high powered ships and that's, they consider that undifferentiated heavy lifting. In my opinion, they didn't say that, but that's clearly what I see coming out of this announcement. The other thing that I saw Dave that's notable is you saw them clearly taking a three lane approach to this machine, learning the advanced builders, the advanced coders and the developers, and then database and data analysts, three swim lanes of personas of target audience. Clearly that is in line with SageMaker and the embedded stuff. So two big revelations, more horsepower required to process training and modeling. Okay. And to the expansion of the personas that are going to be using machine learning. So clearly this is a, to me, a big trend wave that we're seeing that validates some of the startups and I'll see their SageMaker and some of their products. >>Well, as I was saying at the top, I think Amazon's really trying, working hard on simplifying the whole process. And you mentioned training and, and a lot of times people are starting from scratch when they have to train models and retrain models. And so what they're doing is they're trying to create reusable components, uh, and allow people to, as you pointed out to automate and streamline some of that heavy lifting, uh, and as well, they talked a lot about, uh, doing, doing AI inferencing at the edge. And you're seeing, you know, they, they, uh, Swami talked about several foundational premises and the first being a foundation of frameworks. And you think about that at the, at the lowest level of their S their ML stack. They've got, you know, GPU's different processors, inferential, all these alternative processes, processors, not just the, the Xav six. And so these are very expensive resources and Swami talked a lot about, uh, and his colleagues talked a lot about, well, a lot of times the alternative processor is sitting there, you know, waiting, waiting, waiting. And so they're really trying to drive efficiency and speed. They talked a lot about compressing the time that it takes to, to run these, these models, uh, from, from sometimes weeks down to days, sometimes days down to hours and minutes. >>Yeah. Let's, let's unpack these four areas. Let's stay on the firm foundation because that's their core competency infrastructure as a service. Clearly they're laying that down. You put the processors, but what's interesting is the TensorFlow 92% of tensor flows on Amazon. The other thing is that pie torch surprisingly is back up there, um, with massive adoption and the numbers on pie torch literally is on fire. I was coming in and joke on Twitter. Um, we, a PI torch is telling because that means that TensorFlow is originally part of Google is getting, is getting a little bit diluted with other frameworks, and then you've got MX net, some other things out there. So the fact that you've got PI torch 91% and then TensorFlow 92% on 80 bucks is a huge validation. That means that the majority of most machine learning development and deep learning is happening on AWS. Um, >>Yeah, cloud-based, by the way, just to clarify, that's the 90% of cloud-based cloud, uh, TensorFlow runs on and 91% of cloud-based PI torch runs on ADM is amazingly massive numbers. >>Yeah. And I think that the, the processor has to show that it's not trivial to do the machine learning, but, you know, that's where the infrared internship came in. That's kind of where they want to go lay down that foundation. And they had Tanium, they had trainee, um, they had, um, infrared chow was the chip. And then, you know, just true, you know, distributed training training on SageMaker. So you got the chip and then you've got Sage makers, the middleware games, almost like a machine learning stack. That's what they're putting out there >>And how bad a Gowdy, which was, which is, which is a patrol also for training, which is an Intel based chip. Uh, so that was kind of interesting. So a lot of new chips and, and specialized just, we've been talking about this for awhile, particularly as you get to the edge and do AI inferencing, you need, uh, you know, a different approach than we're used to with the general purpose microbes. >>So what gets your take on tenant? Number two? So tenant number one, clearly infrastructure, a lot of announcements we'll go through those, review them at the end, but tenant number two, that Swami put out there was creating the shortest path to success for builders or machine learning builders. And I think here you lays out the complexity, Dave butts, mostly around methodology, and, you know, the value activities required to execute. And again, this points to the complexity problem that they have. What's your take on this? >>Yeah. Well you think about, again, I'm talking about the pipeline, you collect data, you just data, you prepare that data, you analyze that data. You, you, you make sure that it's it's high quality and then you start the training and then you're iterating. And so they really trying to automate as much as possible and simplify as much as possible. What I really liked about that segment of foundation, number two, if you will, is the example, the customer example of the speaker from the NFL, you know, talked about, uh, you know, the AWS stats that we see in the commercials, uh, next gen stats. Uh, and, and she talked about the ways in which they've, well, we all know they've, they've rearchitected helmets. Uh, they've been, it's really a very much database. It was interesting to see they had the spectrum of the helmets that were, you know, the safest, most safe to the least safe and how they've migrated everybody in the NFL to those that they, she started a 24%. >>It was interesting how she wanted a 24% reduction in reported concussions. You know, you got to give the benefit of the doubt and assume some of that's through, through the data. But you know, some of that could be like, you know, Julian Edelman popping up off the ground. When, you know, we had a concussion, he doesn't want to come out of the game with the new protocol, but no doubt, they're collecting more data on this stuff, and it's not just head injuries. And she talked about ankle injuries, knee injuries. So all this comes from training models and reducing the time it takes to actually go from raw data to insights. >>Yeah. I mean, I think the NFL is a great example. You and I both know how hard it is to get the NFL to come on and do an interview. They're very coy. They don't really put their name on anything much because of the value of the NFL, this a meaningful partnership. You had the, the person onstage virtually really going into some real detail around the depth of the partnership. So to me, it's real, first of all, I love stat cast 11, anything to do with what they do with the stats is phenomenal at this point. So the real world example, Dave, that you starting to see sports as one metaphor, healthcare, and others are going to see those coming in to me, totally a tale sign that Amazon's continued to lead. The thing that got my attention was is that it is an IOT problem, and there's no reason why they shouldn't get to it. I mean, some say that, Oh, concussion, NFL is just covering their butt. They don't have to, this is actually really working. So you got the tech, why not use it? And they are. So that, to me, that's impressive. And I think that's, again, a digital transformation sign that, that, you know, in the NFL is doing it. It's real. Um, because it's just easier. >>I think, look, I think, I think it's easy to criticize the NFL, but the re the reality is, is there anything old days? It was like, Hey, you get your bell rung and get back out there. That's just the way it was a football players, you know, but Ted Johnson was one of the first and, you know, bill Bellacheck was, was, you know, the guy who sent him back out there with a concussion, but, but he was very much outspoken. You've got to give the NFL credit. Uh, it didn't just ignore the problem. Yeah. Maybe it, it took a little while, but you know, these things take some time because, you know, it's generally was generally accepted, you know, back in the day that, okay, Hey, you'd get right back out there, but, but the NFL has made big investments there. And you can say, you got to give him, give him props for that. And especially given that they're collecting all this data. That to me is the most interesting angle here is letting the data inform the actions. >>And next step, after the NFL, they had this data prep data Wrangler news, that they're now integrating snowflakes, Databricks, Mongo DB, into SageMaker, which is a theme there of Redshift S3 and Lake formation into not the other way around. So again, you've been following this pretty closely, uh, specifically the snowflake recent IPO and their success. Um, this is an ecosystem play for Amazon. What does it mean? >>Well, a couple of things, as we, as you well know, John, when you first called me up, I was in Dallas and I flew into New York and an ice storm to get to the one of the early Duke worlds. You know, and back then it was all batch. The big data was this big batch job. And today you want to combine that batch. There's still a lot of need for batch, but when people want real time inferencing and AWS is bringing that together and they're bringing in multiple data sources, you mentioned Databricks and snowflake Mongo. These are three platforms that are doing very well in the market and holding a lot of data in AWS and saying, okay, Hey, we want to be the brain in the middle. You can import data from any of those sources. And I'm sure they're going to add more over time. Uh, and so they talked about 300 pre-configured data transformations, uh, that now come with stage maker of SageMaker studio with essentially, I've talked about this a lot. It's essentially abstracting away the, it complexity, the whole it operations piece. I mean, it's the same old theme that AWS is just pointing. It's its platform and its cloud at non undifferentiated, heavy lifting. And it's moving it up the stack now into the data life cycle and data pipeline, which is one of the biggest blockers to monetizing data. >>Expand on that more. What does that actually mean? I'm an it person translate that into it. Speak. Yeah. >>So today, if you're, if you're a business person and you want, you want the answers, right, and you want say to adjust a new data source, so let's say you want to build a new, new product. Um, let me give an example. Let's say you're like a Spotify, make it up. And, and you do music today, but let's say you want to add, you know, movies, or you want to add podcasts and you want to start monetizing that you want to, you want to identify, who's watching what you want to create new metadata. Well, you need new data sources. So what you do as a business person that wants to create that new data product, let's say for podcasts, you have to knock on the door, get to the front of the data pipeline line and say, okay, Hey, can you please add this data source? >>And then everybody else down the line has to get in line and Hey, this becomes a new data source. And it's this linear process where very specialized individuals have to do their part. And then at the other end, you know, it comes to self-serve capability that somebody can use to either build dashboards or build a data product. In a lot of that middle part is our operational details around deploying infrastructure, deploying, you know, training machine learning models that a lot of Python coding. Yeah. There's SQL queries that have to be done. So a lot of very highly specialized activities, what Amazon is doing, my takeaway is they're really streamlining a lot of those activities, removing what they always call the non undifferentiated, heavy lifting abstracting away that it complexity to me, this is a real positive sign, because it's all about the technology serving the business, as opposed to historically, it's the business begging the technology department to please help me. The technology department obviously evolving from, you know, the, the glass house, if you will, to this new data, data pipeline data, life cycle. >>Yeah. I mean, it's classic agility to take down those. I mean, it's undifferentiated, I guess, but if it actually works, just create a differentiated product. So, but it's just log it's that it's, you can debate that kind of aspect of it, but I hear what you're saying, just get rid of it and make it simpler. Um, the impact of machine learning is Dave is one came out clear on this, uh, SageMaker clarify announcement, which is a bias decision algorithm. They had an expert, uh, nationally CFUs presented essentially how they're dealing with the, the, the bias piece of it. I thought that was very interesting. What'd you think? >>Well, so humans are biased and so humans build models or models are inherently biased. And so I thought it was, you know, this is a huge problem to big problems in artificial intelligence. One is the inherent bias in the models. And the second is the lack of transparency that, you know, they call it the black box problem, like, okay, I know there was an answer there, but how did it get to that answer and how do I trace it back? Uh, and so Amazon is really trying to attack those, uh, with, with, with clarify. I wasn't sure if it was clarity or clarified, I think it's clarity clarify, um, a lot of entirely certain how it works. So we really have to dig more into that, but it's essentially identifying situations where there is bias flagging those, and then, you know, I believe making recommendations as to how it can be stamped. >>Nope. Yeah. And also some other news deep profiling for debugger. So you could make a debugger, which is a deep profile on neural network training, um, which is very cool again on that same theme of profiling. The other thing that I found >>That remind me, John, if I may interrupt there reminded me of like grammar corrections and, you know, when you're typing, it's like, you know, bug code corrections and automated debugging, try this. >>It wasn't like a better debugger come on. We, first of all, it should be bug free code, but, um, you know, there's always biases of the data is critical. Um, the other news I thought was interesting and then Amazon's claiming this is the first SageMaker pipelines for purpose-built CIC D uh, for machine learning, bringing machine learning into a developer construct. And I think this started bringing in this idea of the edge manager where you have, you know, and they call it the about machine, uh, uh, SageMaker store storing your functions of this idea of managing and monitoring machine learning modules effectively is on the edge. And, and through the development process is interesting and really targeting that developer, Dave, >>Yeah, applying CIC D to the machine learning and machine intelligence has always been very challenging because again, there's so many piece parts. And so, you know, I said it the other day, it's like a lot of the innovations that Amazon comes out with are things that have problems that have come up given the pace of innovation that they're putting forth. And, and it's like the customers drinking from a fire hose. We've talked about this at previous reinvents and the, and the customers keep up with the pace of Amazon. So I see this as Amazon trying to reduce friction, you know, across its entire stack. Most, for example, >>Let me lay it out. A slide ahead, build machine learning, gurus developers, and then database and data analysts, clearly database developers and data analysts are on their radar. This is not the first time we've heard that. But we, as the kind of it is the first time we're starting to see products materialized where you have machine learning for databases, data warehouse, and data lakes, and then BI tools. So again, three different segments, the databases, the data warehouse and data lakes, and then the BI tools, three areas of machine learning, innovation, where you're seeing some product news, your, your take on this natural evolution. >>Well, well, it's what I'm saying up front is that the good news for, for, for our customers is you don't have to be a Google or Amazon or Facebook to be a super expert at AI. Uh, companies like Amazon are going to be providing products that you can then apply to your business. And, and it's allowed you to infuse AI across your entire application portfolio. Amazon Redshift ML was another, um, example of them, abstracting complexity. They're taking, they're taking S3 Redshift and SageMaker complexity and abstracting that and presenting it to the data analysts. So that, that, that individual can worry about, you know, again, getting to the insights, it's injecting ML into the database much in the same way, frankly, the big query has done that. And so that's a huge, huge positive. When you talk to customers, they, they love the fact that when, when ML can be embedded into the, into the database and it simplifies, uh, that, that all that, uh, uh, uh, complexity, they absolutely love it because they can focus on more important things. >>Clearly I'm this tenant, and this is part of the keynote. They were laying out all their announcements, quick excitement and ML insights out of the box, quick, quick site cue available in preview all the announcements. And then they moved on to the next, the fourth tenant day solving real problems end to end, kind of reminds me of the theme we heard at Dell technology worlds last year end to end it. So we are starting to see the, the, the land grab my opinion, Amazon really going after, beyond I, as in pass, they talked about contact content, contact centers, Kendra, uh, lookout for metrics, and that'll maintain men. Then Matt would came on, talk about all the massive disruption on the, in the industries. And he said, literally machine learning will disrupt every industry. They spent a lot of time on that and they went into the computer vision at the edge, which I'm a big fan of. I just loved that product. Clearly, every innovation, I mean, every vertical Dave is up for grabs. That's the key. Dr. Matt would message. >>Yeah. I mean, I totally agree. I mean, I see that machine intelligence as a top layer of, you know, the S the stack. And as I said, it's going to be infused into all areas. It's not some kind of separate thing, you know, like, Coobernetti's, we think it's some separate thing. It's not, it's going to be embedded everywhere. And I really like Amazon's edge strategy. It's this, you, you are the first to sort of write about it and your keynote preview, Andy Jassy said, we see, we see, we want to bring AWS to the edge. And we see data center as just another edge node. And so what they're doing is they're bringing SDKs. They've got a package of sensors. They're bringing appliances. I've said many, many times the developers are going to be, you know, the linchpin to the edge. And so Amazon is bringing its entire, you know, data plane is control plane, it's API APIs to the edge and giving builders or slash developers, the ability to innovate. And I really liked the strategy versus, Hey, here's a box it's, it's got an x86 processor inside on a, throw it over the edge, give it a cool name that has edge in it. And here you go, >>That sounds call it hyper edge. You know, I mean, the thing that's true is the data aspect at the edge. I mean, everything's got a database data warehouse and data lakes are involved in everything. And then, and some sort of BI or tools to get the data and work with the data or the data analyst, data feeds, machine learning, critical piece to all this, Dave, I mean, this is like databases used to be boring, like boring field. Like, you know, if you were a database, I have a degree in a database design, one of my degrees who do science degrees back then no one really cared. If you were a database person. Now it's like, man data, everything. This is a whole new field. This is an opportunity. But also, I mean, are there enough people out there to do all this? >>Well, it's a great point. And I think this is why Amazon is trying to extract some of the abstract. Some of the complexity I sat in on a private session around databases today and listened to a number of customers. And I will say this, you know, some of it I think was NDA. So I can't, I can't say too much, but I will say this Amazon's philosophy of the database. And you address this in your conversation with Andy Jassy across its entire portfolio is to have really, really fine grain access to the deep level API APIs across all their services. And he said, he said this to you. We don't necessarily want to be the abstraction layer per se, because when the market changes, that's harder for us to change. We want to have that fine-grained access. And so you're seeing that with database, whether it's, you know, no sequel, sequel, you know, the, the Aurora the different flavors of Aurora dynamo, DV, uh, red shift, uh, you know, already S on and on and on. There's just a number of data stores. And you're seeing, for instance, Oracle take a completely different approach. Yes, they have my SQL cause they know got that with the sun acquisition. But, but this is they're really about put, is putting as much capability into a single database as possible. Oh, you only need one database only different philosophy. >>Yeah. And then obviously a health Lake. And then that was pretty much the end of the, the announcements big impact to health care. Again, the theme of horizontal data, vertical specialization with data science and software playing out in real time. >>Yeah. Well, so I have asked this question many times in the cube, when is it that machines will be able to make better diagnoses than doctors and you know, that day is coming. If it's not here, uh, you know, I think helped like is really interesting. I've got an interview later on with one of the practitioners in that space. And so, you know, healthcare is something that is an industry that's ripe for disruption. It really hasn't been disruption disrupted. It's a very high, high risk obviously industry. Uh, but look at healthcare as we all know, it's too expensive. It's too slow. It's too cumbersome. It's too long sometimes to get to a diagnosis or be seen, Amazon's trying to attack with its partners, all of those problems. >>Well, Dave, let's, let's summarize our take on Amazon keynote with machine learning, I'll say pretty historic in the sense that there was so much content in first keynote last year with Andy Jassy, he spent like 75 minutes. He told me on machine learning, they had to kind of create their own category Swami, who we interviewed many times on the cube was awesome. But a lot of still a lot more stuff, more, 215 announcements this year, machine learning more capabilities than ever before. Um, moving faster, solving real problems, targeting the builders, um, fraud platform set of things is the Amazon cadence. What's your analysis of the keynote? >>Well, so I think a couple of things, one is, you know, we've said for a while now that the new innovation cocktail is cloud plus data, plus AI, it's really data machine intelligence or AI applied to that data. And the scale at cloud Amazon Naylor obviously has nailed the cloud infrastructure. It's got the data. That's why database is so important and it's gotta be a leader in machine intelligence. And you're seeing this in the, in the spending data, you know, with our partner ETR, you see that, uh, that AI and ML in terms of spending momentum is, is at the highest or, or at the highest, along with automation, uh, and containers. And so in. Why is that? It's because everybody is trying to infuse AI into their application portfolios. They're trying to automate as much as possible. They're trying to get insights that, that the systems can take action on. >>And, and, and actually it's really augmented intelligence in a big way, but, but really driving insights, speeding that time to insight and Amazon, they have to be a leader there that it's Amazon it's, it's, it's Google, it's the Facebook's, it's obviously Microsoft, you know, IBM's Tron trying to get in there. They were kind of first with, with Watson, but with they're far behind, I think, uh, the, the hyper hyper scale guys. Uh, but, but I guess like the key point is you're going to be buying this. Most companies are going to be buying this, not building it. And that's good news for organizations. >>Yeah. I mean, you get 80% there with the product. Why not go that way? The alternative is try to find some machine learning people to build it. They're hard to find. Um, so the seeing the scale of kind of replicating machine learning expertise with SageMaker, then ultimately into databases and tools, and then ultimately built into applications. I think, you know, this is the thing that I think they, my opinion is that Amazon continues to move up the stack, uh, with their capabilities. And I think machine learning is interesting because it's a whole new set of it's kind of its own little monster building block. That's just not one thing it's going to be super important. I think it's going to have an impact on the startup scene and innovation is going, gonna have an impact on incumbent companies that are currently leaders that are under threat from new entrance entering the business. >>So I think it's going to be a very entrepreneurial opportunity. And I think it's going to be interesting to see is how machine learning plays that role. Is it a defining feature that's core to the intellectual property, or is it enabling new intellectual property? So to me, I just don't see how that's going to fall yet. I would bet that today intellectual property will be built on top of Amazon's machine learning, where the new algorithms and the new things will be built separately. If you compete head to head with that scale, you could be on the wrong side of history. Again, this is a bet that the startups and the venture capitals will have to make is who's going to end up being on the right wave here. Because if you make the wrong design choice, you can have a very complex environment with IOT or whatever your app serving. If you can narrow it down and get a wedge in the marketplace, if you're a company, um, I think that's going to be an advantage. This could be great just to see how the impact of the ecosystem this will be. >>Well, I think something you said just now it gives a clue. You talked about, you know, the, the difficulty of finding the skills. And I think that's a big part of what Amazon and others who were innovating in machine learning are trying to do is the gap between those that are qualified to actually do this stuff. The data scientists, the quality engineers, the data engineers, et cetera. And so companies, you know, the last 10 years went out and tried to hire these people. They couldn't find them, they tried to train them. So it's taking too long. And now that I think they're looking toward machine intelligence to really solve that problem, because that scales, as we, as we know, outsourcing to services companies and just, you know, hardcore heavy lifting, does it doesn't scale that well, >>Well, you know what, give me some machine learning, give it to me faster. I want to take the 80% there and allow us to build certainly on the media cloud and the cube virtual that we're doing. Again, every vertical is going to impact a Dave. Great to see you, uh, great stuff. So far week two. So, you know, we're cube live, we're live covering the keynotes tomorrow. We'll be covering the keynotes for the public sector day. That should be chock-full action. That environment is going to impact the most by COVID a lot of innovation, a lot of coverage. I'm John Ferrari. And with Dave Alante, thanks for watching.

>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, >>Hi. We are the Cube live covering AWS reinvent 2020. I'm Lisa Martin, and I've got one of our cube alumni back with me. Mike Miller is here. General manager of A W s AI Devices at AWS. Mike, welcome back to the Cube. >>Hi, Lisa. Thank you so much for having me. It's really great to join you all again at this virtual reinvent. >>Yes, I think last year you were on set. We have always had to. That's at reinvent. And you you had the deep race, your car, and so we're obviously socially distance here. But talk to me about deepracer. What's going on? Some of the things that have gone on the last year that you're excited >>about. Yeah, I'd love to tell. Tell you a little bit about what's been happening. We've had a tremendous year. Obviously, Cove. It has restricted our ability to have our in person races. Eso we've really gone gone gangbusters with our virtual league. So we have monthly races for competitors that culminate in the championship. Um, at reinvent. So this year we've got over 100 competitors who have qualified and who are racing virtually with us this year at reinvent. They're participating in a series of knockout rounds that are being broadcast live on twitch over the next week. That will whittle the group down to AH Group of 32 which will have a Siris of single elimination brackets leading to eight finalists who will race Grand Prix style five laps, eight cars on the track at the same time and will crown the champion at the closing keynote on December 15th this year. >>Exciting? So you're bringing a reinforcement, learning together with with sports that so many of us have been missing during the pandemic. We talked to me a little bit about some of the things that air that you've improved with Deep Racer and some of the things that are coming next year. Yeah, >>absolutely so, First of all, Deep Racer not only has been interesting for individuals to participate in the league, but we continue to see great traction and adoption amongst big customers on dare, using Deep Racer for hands on learning for machine learning, and many of them are turning to Deep Racer to train their workforce in machine learning. So over 150 customers from the likes of Capital One Moody's, Accenture, DBS Bank, JPMorgan Chase, BMW and Toyota have held Deep Racer events for their workforces. And in fact, three of those customers Accenture, DBS Bank and J. P. Morgan Chase have each trained over 1000 employees in their organization because they're just super excited. And they find that deep racers away to drive that excitement and engagement across their customers. We even have Capital one expanded this to their families, so Capital One ran a deep raise. Their Kids Cup, a family friendly virtual competition this past year were over. 250 Children and 200 families got to get hands on with machine learning. >>So I envisioned some. You know, this being a big facilitator during the pandemic when there's been this massive shift to remote work has have you seen an uptick in it for companies that talking about training need to be ableto higher? Many, many more people remotely but also train them? Is deep Racer facilitator of that? Yeah, >>absolutely. Deep Racer has ah core component of the experience, which is all virtualized. So we have, ah, console and integration with other AWS services so that racers can participate using a three d racing simulator. They can actually see their car driving around a track in a three D world simulation. Um, we're also selling the physical devices. So you know, if participants want to get the one of those devices and translate what they've done in the virtual world to the real world, they can start doing that. And in fact, just this past year, we made our deep race or car available for purchase internationally through the Amazon Com website to help facilitate that. >>So how maney deep racers air out there? I'm just curious. >>Oh, thousands. Um, you know, And there what? What we've seen is some companies will purchase you, know them in bulk and use them for their internal leagues. Just like you know, JP Morgan Chase on DBS Bank. These folks have their own kind of tracks and racers that they'll use to facilitate both in person as well as the virtual racing. >>I'm curious with this shift to remote that we mentioned a minute ago. How are you seeing deepracer as a facilitator of engagement. You mentioned engagement. And that's one of the biggest challenges that so Maney teams develops. Processes have without being co located with each other deep Brister help with that. I mean, from an engagement perspective, I think >>so. What we've seen is that Deep Racer is just fun to get your hands on. And we really lower the learning curve for machine learning. And in particular, this branch called reinforcement Learning, which is where you train this agent through trial and error toe, learn how to do a new, complex task. Um, and what we've seen is that customers who have introduced Deep Racer, um, as an event for their employees have seen ah, very wide variety of employees. Skill sets, um, kind of get engaged. So you've got not just the hardcore deep data scientists or the M L engineers. You've got Web front end programmers. You even have some non technical folks who want to get their hands dirty. Onda learn about machine learning and Deep Racer really is a nice, gradual introduction to doing that. You can get engaged with it with very little kind of coding knowledge at all. >>So talk to me about some of the new services. And let's look at some specific use case customer use cases with each service. Yeah, >>absolutely. So just to set the context. You know, Amazon's got hundreds. A ws has hundreds of thousands of customers doing machine learning on AWS. No customers of all sizes are embedding machine learning into their no core business processes. And one of the things that we always do it Amazon is We're listening to customers. You know, 90 to 95% of our road maps are driven by customer feedback. And so, as we've been talking to these industrial manufacturing customers, they've been telling us, Hey, we've got data. We've got these processes that are happening in our industrial sites. Um, and we just need some help connecting the dots like, how do we really most effectively use machine learning to improve our processes in these industrial and manufacturing sites? And so we've come up with these five services. They're focused on industrial manufacturing customers, uh, two of the services air focused around, um, predictive maintenance and, uh, the other three services air focused on computer vision. Um, and so let's start with the predictive maintenance side. So we announced Amazon Monitor On and Amazon look out for equipment. So these services both enable predictive maintenance powered by machine learning in a way that doesn't require the customer to have any machine learning expertise. So Mono Tron is an end to end machine learning system with sensors, gateway and an ML service that can detect anomalies and predict when industrial equipment will require maintenance. I've actually got a couple examples here of the sensors in the gateway, so this is Amazon monitor on these little sensors. This little guy is a vibration and temperature sensor that's battery operated, and wireless connects to the gateway, which then transfers the data up to the M L Service in the cloud. And what happens is, um, the sensors can be connected to any rotating machinery like pump. Pour a fan or a compressor, and they will send data up to the machine learning cloud service, which will detect anomalies or sort of irregular kind of sensor readings and then alert via a mobile app. Just a tech or a maintenance technician at an industrial site to go have a look at their equipment and do some preventative maintenance. So um, it's super extreme line to end to end and easy for, you know, a company that has no machine learning expertise to take advantage of >>really helping them get on board quite quickly. Yeah, >>absolutely. It's simple tea set up. There's really very little configuration. It's just a matter of placing the sensors, pairing them up with the mobile app and you're off and running. >>Excellent. I like easy. So some of the other use cases? Yeah, absolutely. >>So So we've seen. So Amazon fulfillment centers actually have, um, enormous amounts of equipment you can imagine, you know, the size of an Amazon fulfillment center. 28 football fields, long miles of conveyor belts and Amazon fulfillment centers have started to use Amazon monitor on, uh, to monitor some of their conveyor belts. And we've got a filament center in Germany that has started using these 1000 sensors, and they've already been able to, you know, do predictive maintenance and prevent downtime, which is super costly, you know, for businesses, we've also got customers like Fender, you know, who makes guitars and amplifiers and musical equipment. Here in the US, they're adopting Amazon monitor on for their industrial machinery, um, to help prevent downtime, which again can cost them a great deal as they kind of hand manufacture these high end guitars. Then there's Amazon. Look out for equipment, which is one step further from Amazon monitor on Amazon. Look out for equipment. Um provides a way for customers to send their own sensor data to AWS in order to build and train a model that returns predictions for detecting abnormal equipment behavior. So here we have a customer, for example, like GP uh, E P s in South Korea, or I'm sorry, g S E P s in South Korea there in industrial conglomerate, and they've been collecting their own data. So they have their own sensors from industrial equipment for a decade. And they've been using just kind of rule basic rules based systems to try to gain insight into that data. Well, now they're using Amazon, look out for equipment to take all of their existing sensor data, have Amazon for equipment, automatically generate machine learning models on, then process the sensor data to know when they're abnormalities or when some predictive maintenance needs to occur. >>So you've got the capabilities of working with with customers and industry that that don't have any ML training to those that do have been using sensors. So really, everybody has an opportunity here to leverage this new Amazon technology, not only for predicted, but one of the things I'm hearing is contact list, being able to understand what's going on without having to have someone physically there unless there is an issue in contact. This is not one of the words of 2020 but I think it probably should be. >>Yeah, absolutely. And in fact, that that was some of the genesis of some of the next industrial services that we announced that are based on computer vision. What we saw on what we heard when talking to these customers is they have what we call human inspection processes or manual inspection processes that are required today for everything from, you know, monitoring you like workplace safety, too, you know, quality of goods coming off of a machinery line or monitoring their yard and sort of their, you know, truck entry and exit on their looking for computer vision toe automate a lot of these tasks. And so we just announced a couple new services that use computer vision to do that to automate these once previously manual inspection tasks. So let's start with a W A. W s Panorama uses computer vision toe improve those operations and workplace safety. AWS Panorama is, uh, comes in two flavors. There's an appliance, which is, ah, box like this. Um, it basically can go get installed on your network, and it will automatically discover and start processing the video feeds from existing cameras. So there's no additional capital expense to take a W s panorama and have it apply computer vision to the cameras that you've already got deployed, you know, So customers are are seeing that, um, you know, computer vision is valuable, but the reason they want to do this at the edge and put this computer vision on site is because sometimes they need to make very low Leighton see decisions where if you have, like a fast moving industrial process, you can use computer vision. But I don't really want to incur the cost of sending data to the cloud and back. I need to make a split second decision, so we need machine learning that happens on premise. Sometimes they don't want to stream high bandwidth video. Or they just don't have the bandwidth to get this video back to the cloud and sometimes their data governance or privacy restrictions that restrict the company's ability to send images or video from their site, um, off site to the cloud. And so this is why Panorama takes this machine learning and makes it happen right here on the edge for customers. So we've got customers like Cargill who uses or who is going to use Panorama to improve their yard management. They wanna use computer vision to detect the size of trucks that drive into their granaries and then automatically assign them to an appropriately sized loading dock. You've got a customer like Siemens Mobility who you know, works with municipalities on, you know, traffic on by other transport solutions. They're going to use AWS Panorama to take advantage of those existing kind of traffic cameras and build machine learning models that can, you know, improve congestion, allocate curbside space, optimize parking. We've also got retail customers. For instance, Parkland is a Canadian fuel station, um, and retailer, you know, like a little quick stop, and they want to use Panorama to do things like count the people coming in and out of their stores and do heat maps like, Where are people visiting my store so I can optimize retail promotions and product placement? >>That's fantastic. The number of use cases is just, I imagine if we had more time like you could keep going and going. But thank you so much for not only sharing what's going on with Deep Racer and the innovations, but also for show until even though we weren't in person at reinvent this year, Great to have you back on the Cube. Mike. We appreciate your time. Yeah, thanks, Lisa, for having me. I appreciate it for Mike Miller. I'm Lisa Martin. You're watching the cubes Live coverage of aws reinvent 2020.

>>covering the space and cybersecurity symposium 2020 hosted by Cal poly. Hold on. Welcome to this special presentation with Cal poly hosting the space and cybersecurity symposium, 2020 virtual, um, John for your host with the cube and Silicon angle here in our Palo Alto studios with our remote guests, we couldn't be there in person, but we're going to be here remotely. Got a great session and a panel for one hour topic preparing students for the jobs of today and tomorrow, but a great lineup. Bill Britain, Lieutenant Colonel from the us air force, retired vice president for information technology and CIO and the director of the California cyber security Institute for Cal poly bill. Thanks for joining us, dr. Amy Fisher, who's the Dean of the college of engineering at Cal poly and trunk fam professor and researcher at the U S air force Academy. Folks, thanks for joining me today. >>Our pleasure got a great, great panel. This is one of my favorite topics preparing students for the next generation, the jobs for today and tomorrow. We've got an hour. I'd love you guys to start with an opening statement, to kick things off a bill. We'll start with you. Well, I'm really pleased to be, to start on this. Um, as the director for the cybersecurity Institute and the CIO at Cal poly, it's really a fun, exciting job because as a Polytechnic technology, as such a forefront in what we're doing, and we've had a, a wonderful opportunity being 40 miles from Vandenberg air force base to really look at the nexus of space and cyber security. And if you add into that, uh, both commercial government and civil space and cybersecurity, this is an expanding wide open time for cyber and space. In that role that we have with the cyber security Institute, we partner with elements of the state and the university. >>And we try to really add value above our academic level, which is some of the highest in the nation and to really merge down and go a little lower and start younger. So we actually are running the week prior to this showing a cybersecurity competition for high schools or middle schools in the state of California, that competition this year is based on a scenario around hacking of a commercial satellite and the forensics of the payload that was hacked and the networks associated with it. This is going to be done using products like Wireshark autopsy and other tools that will give those high school students. What we hope is a huge desire to follow up and go into cyber and cyber space and space and follow that career path. And either come to Cal poly or some other institution that's going to let them really expand their horizons in cybersecurity and space for the future >>Of our nation. >>Bill, thanks for that intro, by the way, it's gonna give you props for an amazing team and job you guys are doing at Cal poly, that Dex hub and the efforts you guys are having with your challenge. Congratulations on that great work. Thank you >>Star team. It's absolutely amazing. You find that much talent in one location. And I think Amy is going to tell you she's got the same amount of talent in her staff. So it's, it's a great place to be. >>Amy flasher. You guys have a great organization down there, amazing curriculum, grazing people, great community, your opening statement. >>Hello everybody. It's really great to be a part of this panel on behalf of the Cal poly college of engineering here at Cal poly, we really take preparing students for the jobs of today and tomorrow completely seriously. And we claim that our students really graduate. So they're ready day one for their first real job, but that means that in getting them to that point, we have to help them get valuable and meaningful job experience before they graduate, but through our curriculum and through multiple internship or summer research opportunities. So we focus our curriculum on what we call a learn by doing philosophy. And this means that we have a combination of practical experience and learn by doing both in and out of the classroom. And we find that to be really critical for preparing students for the workforce here at Cal poly, we have more than 6,000 engineering students. >>We're one of the largest undergraduate engineering schools in the country. Um, and us news ranks us the eighth best undergraduate engineering program in the, in the country and the top ranked state school. We're really, really proud that we offer this impactful hands on engineering education that really exceeds that of virtually all private universities while reaching a wider audience of students. We offer 14 degree programs and really we're talking today about cyber and space. And I think most of those degree programs can really make an impact in the space and cybersecurity economy. And this includes not only things like Aero and cyber directly, but also electrical engineering, mechanical engineering, computer engineering, materials, engineering, even manufacturing, civil and biomedical engineering. As there's a lot of infrastructure needs that go into supporting launch capabilities. Our aerospace program graduates hundreds of aerospace engineers, and most of them are working right here in California. >>I'm with many of our corporate partners, including Northrop Grumman, Lockheed, Boeing, Raytheon space, X, Virgin, galactic JPL, and so many other places where we have Cal poly engineer's impacting the space economy. Our cybersecurity focus is found mainly in our computer science and software engineering programs. And it's really a rapidly growing interest among our students. Computer science is our most popular major and industry interest and partnerships are integrated into our curriculum. And we do that oftentimes through support from industry. So we have partnerships with Northrop Grumman for professorship and a cyber lab and from PG and E for critical infrastructure, cybersecurity lab, and professorship. And we think that industry partnerships like these are really critical to preparing students for the future as the field's evolving so quickly and making sure we adapt our facilities and our curriculum to stay in line with what we're seeing in industry is incredibly important. >>In our aerospace program, we have an educational partnership with the air force research labs. That's allowing us to install new high performance computing capabilities and a space environments lab. That's going to enhance our satellite design capabilities. And if we talk about satellite design, Cal poly is the founding home of the cube sat program, which pioneered small satellite capabilities. And we remain the worldwide leader in maintaining the cube set standard. And our student program has launched more cube sets than any other program. So here again, we have this learn by doing experience every year for dozens of aerospace, electrical, computer science, mechanical engineering students, and other student activities that we think are just as important include ethical hacking through our white hat club, Cal poly space systems, which does really, really big rocket launches and our support program for women in both of these fields like wish, which is women in software and hardware. >>Now, you know, really trying to bring in a wide variety of people into these fields is incredibly important and outreach and support to those demographics. Traditionally underrepresented in these fields is going to be really critical to future success. So by drawing on the lived experiences by people with different types of backgrounds, while we develop the type of culture and environment where all of us can get to the best solution. So in terms of bringing people into the field, we see that research shows, we need to reach kids when they're in late elementary and middle schools to really overcome that cultural bias that works against diversity in our fields. And you heard bill talking about the cyber cybersec, the California cybersecurity institutes a year late cyber challenge. There's a lot of other people who are working to bring in a wider variety of, uh, of people into the field, like girl Scouts, which has introduced dozens of new badges over the past few years, including a whole cybersecurity series of badges and a concert with Palo Alto networks. So we have our work cut out for us, but we know what we need to do. And if we're really committed to prep properly preparing the workforce for today and tomorrow, I think our future is going to be bright. I'm looking forward to our discussion today. >>Yeah, you got a flashy for great, great comment, opening statement and congratulations. You got the right formula down there, the right mindset, and you got a lot of talent and community as well. Thank thank you for that opening statement. Next step from Colorado Springs, trunk fam, who's a professor and researcher. The us air force Academy is doing a lot of research around the areas that are most important for the intersection of space and technology trunk. >>Good afternoon, first electric and Cal poli for the opportunity. And today I want to go briefly about cyber security in S application. Whenever we talk about cyber security, the impression is got yes, a new phew that is really highly complex involving a lot of technical area. But in reality, in my personal opinion, it is in be complex because involve many disciplines. The first thing we think about is computer engineering and computer networking, but it's also involving communication sociology, law practice. And this practice of cyber security goes in on the info computer expert, but it's also info everybody else who has a computing device that is connected to the internet. And this participation is obviously every body in today's environment. When we think about the internet, we know that is a good source of information, but come with the convenience of information that we can access. >>We are constantly faced in being from the internet. Some of them, we might be aware of some of them we might not be aware of. For example, when we search on the internet, a lot of time, our browser will be saved and gotten this site is not trusted. So we will be more careful. What about the sites that we trusted? We know getting those salad chicken sites, but they're not a hundred percent good at proof. What happened? It was all side, uh, attack by hacker. And then they will be a silent source that we might not be aware of. So in the reality, we need to be more practicing the, um, cyber security from our SIBO point of view and not from a technical point of view. When we talk about space application, we should know that all the hardware, a computer based tool by computer system and therefore the hardware and the software must go through some certification process so that they can be record that air with the flight. >>What the, when we know that in the certification process is focusing on the functionality of the hardware and software, but one aspect that is explicitly and implicitly required is the security of those components. And we know that those components have to be connected with the ground control station and be communication is through the air, through the layby or signal. So anybody who has access to those communication regular signal will be able to control the space system that we put up there. And we certainly do not want our system to be hijacked by a third party. >>I'm not going to aspect of cybersecurity is we try to design the space system in a very strong manner. So it's almost impossible to hack in, but what about some August week system that might be connected to so strong system? For example, the spare system will be connected to the ground control station and on the ground control station, we have the human controller in those people have cell phone. They are allowed to use cell phones for communication, but at the same time, they are connected to the internet, to the cell phone and their cell phone might be connected to the computer that control the flight software and hardware. So what I want to say is that we try to build strong system and we protected them, but there will be some weaker system that we could not intended, but exists to be connected to our strong system. And those are the points that hacker will be trying to attack. If we know how to control the access to those points, we will be having a much better system for the space system. And when we see the cybersecurity that is requiring the participation everywhere, it's important to Merck that there is a source of opportunity for students to engage the workforce. To concede the obviously student in engineering can focus their knowledge and expertise to provide technological solution, to protect the system that we view. But we also >>Have students in business who can focus to write a business plan to reach the market. We also have student in law who can focus policy governing the cyber security. And we also have student in education who can focus the expert. She should be saying how to teach cyber security practice and students can focus the effort to implement security measures and it implies job opportunity. >>Thank you trunk for those great comments, great technology opportunities, but interesting as well as the theme that we're seeing across the entire symposium and in the virtual hallways that we're hearing conversations and you pointed out some of them, dr. Fleischer did as well. And bill, you mentioned it. It's not one thing. It's not just technology, it's different skills. And, um, Amy, you mentioned that computer science is the hottest degree, but you have the hottest aerospace program in the world. I mean, so all of this is kind of balancing it's interdisciplinary. It's a structural change before we get into some of the, um, how they prepare the students. Can you guys talk about some of the structural changes that are modern now in preparing, um, in these opportunities because societal impact is a law potentially impact it's, it's how we educate there's no cross-discipline skillsets. It's not just get the degree, see out in the field bill, you want to start. >>Well, what's really fun about this job is, is that in the air force, uh, I worked in the space and missile business and what we saw was a heavy reliance on checklist format, security procedures, analog systems, and what we're seeing now in our world, both in the government and the commercial side, uh, is a move to a digital environment. And the digital environment is a very quick and adaptive environment. And it's going to require a digital understanding. Matter of fact, um, the, uh, under secretary of the air force for acquisition, uh, rev recently referenced the need to understand the digital environment and how that's affecting acquisition. So as, as both Amy, um, and trunk said, even business students are now in the >>Cybersecurity business. And, and so, again, what we're seeing is, is the change. Now, another phenomenon that we're seeing in the space world is there's just so much data. Uh, one of the ways that we addressed that in the past was to look at high performance computing. It was a lot stricter control over how that worked, but now what we're seeing these adaptation of cloud cloud technologies in space support, space, data, command, and control. Uh, and so what we see is a modern space engineer who asked to understand digital, has to understand cloud and has to understand the context of all those with a cyber environment. That's really changing the forefront of what is a space engineer, what is a digital engineer and what does a future engineer, both commercial or government? So I think the opportunity for all of these things is really good, particularly for a Polytechnic air force Academy and others that are focusing on a more, uh, widened experiential level of cloud and engineering and other capabilities. >>And I'll tell you the part that as the CIO, I have to remind everybody, all this stuff works for the it stuff. So you've got to understand how your it infrastructures are tied and working together. Um, as we noted earlier, one of the things is, is that these are all relays from point the point, and that architecture is part of your cybersecurity architecture. So again, every component has now become a cyber aware cyber knowledgeable, and in what we'd like to call as a cyber cognizant citizen, where they have to understand the context, patients chip software, that the Fleischer talk about your perspective, because you mentioned some of the things that computer science. Remember when I'm in the eighties, when I got my computer science degree, they call the software engineers, and then you became software developers. And then, so again, engineering is the theme. If you're engineering a system, there's now software involved, um, and there's also business engineering business models. So talk about some of your comments was, you mentioned, computer science is hot. You got the aerospace, you've got these multidisciplines you got definitely diversity as well. It brings more perspectives in as well. Your thoughts on these structural interdisciplinary things. >>I think this is, this is really key to making sure that students are prepared to work in the workforce is looking at the, the blurring between fields no longer are you just a computer scientist, no longer are you just an aerospace engineer? You really have to have an expertise where you can work with people across disciplines. All of these, all of these fields are just working with each other in ways we haven't seen before. And bill brought up data, you know, data science is something that's cross cutting across all of our fields. So we want engineers that have the disciplinary expertise so that they can go deep into these fields, but we want them to be able to communicate with each and to be able to communicate across disciplines and to be able to work in teams that are across disciplines. You can no longer just work with other computer scientists or just work with other aerospace engineers. >>There's no part of engineering that is siloed anymore. So that's how we're changing. You have to be able to work across those, those disciplines. And as you, as Tron pointed out, you know, ethics has to come into this. So you can no longer try to fully separate what we would traditionally have called the, the liberal arts and say, well, that's over there in general education. No ethics is an important part of what we're doing and how we integrate that into our curriculum. So it was communication. So is working on public policy and seeing where all of these different aspects tied together to make the impact that we want to have in the world. So it, you no longer can work solo in these fields. >>Great point. And bill also mentioned the cloud. One thing about the cloud that showed us as horizontal scalability has created a lot of value and certainly data is now horizontal Trung. You mentioned some of the things about cryptography for the kids out there. I mean, you can look at the pathway for career. You can do a lot of tech and, but you don't have to go deep. Sometimes you can go, you can go as deep as you want, but there's so much more there. Um, what technology do you see, how it's going to help students in your opinion? >>Well, I'm a professor in computer science, so I'd like to talk out a little bit about computer programming. Now we, uh, working in complex project. So most of the time we design a system from scratch. We view it from different components and the components that we have either we get it from or some time we get it from the internet in the open source environment, it's fun to get the source code and then work to our own application. So now when we are looking at a Logie, when we talk about encryption, for example, we can easily get the source code from the internet. And the question is, is safe to use those source code. And my, my, my question is maybe not. So I always encourage my students to learn how to write source score distribution, where that I learned a long time ago before I allow them to use the open source environment. And one of the things that they have to be careful, especially with encryption is be quote that might be hidden in the, in the source, get the download here, some of the source. >>So open source, it's a wonderful place to be, but it's also that we have to be aware of >>Great point before we get into some of the common one quick thing for each of you like to get your comments on, you know, the there's been a big movement on growth mindset, which has been a great, I'm a big believer in having a growth mindset and learning and all that good stuff. But now that when you talk about some of these things that we're mentioning about systems, there's, there's an, there's a new trend around a systems mindset, because if everything's now a system distributed systems, now you have space in cyber security, you have to understand the consequences of changes. And you mentioned some of that Trung in changes in the source code. Could you guys share your quick opinions on the, the idea of systems thinking, is that a mindset that people should be looking at? Because it used to be just one thing, Oh, you're a systems guy or galley. There you go. You're done. Now. It seems to be in social media and data. Everything seems to be systems. What's your take dr. Fleischer, we'll start with you. >>Uh, I'd say it's a, it's another way of looking at, um, not being just so deep in your discipline. You have to understand what the impact of the decisions that you're making have on a much broader, uh, system. And so I think it's important for all of our students to get some exposure to that systems level thinking and looking at the greater impact of the decision that they're making. Now, the issue is where do you set the systems boundary, right? And you can set the systems boundary very close in and concentrate on an aspect of a design, or you can continually move that system boundary out and see, where do you hit the intersections of engineering and science along with ethics and public policy and the greater society. And I think that's where some of the interesting work is going to be. And I think at least exposing students and letting them know that they're going to have to make some of these considerations as they move throughout their career is going to be vital as we move into the future. Bill. What's your thoughts? >>Um, I absolutely agree with Amy and I think there's a context here that reverse engineering, um, and forensics analysis and forensics engineering are becoming more critical than ever, uh, the ability to look at what you have designed in a system and then tear it apart and look at it for gaps and holes and problem sets, or when you're given some software that's already been pre developed, checking it to make sure it is, is really going to do what it says it's going to do. That forensics ability becomes more and more a skillset that also you need the verbal skills to explain what it is you're doing and what you found. So the communication side, the systems analysis, >>The forensics analysis side, >>These are all things that are part of that system >>Approach that I think you could spend hours on. And we still haven't really done great job on it. So it's a, it's. One of my fortes is the really the whole analysis side of forensics and it reverse engineering >>Try and real quick systems thinking. >>Well, I'd like to share with you my experience. When I worked in the space patient program at NASA, we had two different approaches. One is a down approach where we design it from the system general point of view, where we put components to complex system. But at the same time, we have the bottom up approach where we have Ken Chile who spent time and effort the individual component. And they have to be expert in those Chinese component. That might be general component the gallery. And in the space station program, we bring together the welcome up engineer, who designed everything in detail in the system manager who manage the system design from the top down. And we meet in the middle and took the idea with compromise a lot of differences. Then we can leave a display station that we are operating to be okay, >>Great insight. And that's the whole teamwork collaboration that, that was mentioning. Thanks so much for that insight. I wanted to get that out there because I know myself as a, as a parent, I'm always trying to think about what's best for my kids in their friends, as they grow up into the workforce. I know educators and leaders in industry would love to know some of the best practices around some of the structural changes. So thanks for that insight, but this topics about students and helping them prepare. Uh, so we heard, you know, be, be multiple discipline, broaden your horizons, think like systems top down, bottom up, work together as a team and follow the data. So I got to ask you guys, there's a huge amount of job openings in cybersecurity. It's well documented and certainly at the intersection of space and cyber, it's only gonna get bigger, right? You're going to see more and more demand for new types of jobs. How do we get high school and college students interested in security as a career at the flagship? We'll start with you in this one. >>I would say really one of the best ways to get students interested in the career is to show them the impact that it's going to have. There's definitely always going to be students who are going to want to do the technology for the technology sake, but that will limit you to a narrow set of students. And by showing that the greater impact that these types of careers are going to have on the types of problems that you're going to be able to solve and the impact you're going to be able to have on the world, around you, that's the word that we really need to get out. And a wide variety of students really respond to these messages. So I think it's really kind of reaching out at the, uh, the elementary, the middle school level, and really kind of getting this idea that you can make a big difference, a big positive difference in the field with some of these careers is going to be really critical. >>Real question, follow up. What do you think is the best entry point? You mentioned middle squad in here, elementary school. This comes, there's a lot of discussions around pipelining and we're going to get into women in tech and under-represented matters later, but you know, is it too early or what's the, what's your feeling on this? >>My feeling is the earlier we can normalize it the better the, uh, if you can normalize an interest in, in computers and technology and building an elementary school, that's absolutely critical. But the dropoff point that we're seeing is between what I would call like late elementary and early middle school. Um, and just kind of as an anecdote, I, for years ran an outreach program for girl Scouts in grades four and five and grade six, seven, and eight. And we had a hundred slots in each program. And every year the program would sell out for girls in grades four and five, and every year we'd have spots remaining in grades six, seven, and eight. And that's literally where the drop-off is occurring between that late elementary and that middle school range. So that's the area that we need to target to make sure we keep those young women involved and interested as we move forward. >>Bill, how are we going to get these kids interested in security? You mentioned a few programs you got. Yeah. I mean, who wants to, who wouldn't want to be a white hat hacker? I mean, yeah, that sounds exciting. Yeah. Great questions. Let's start with some basic principles though. Is let me ask you a question, John, a name for me, one white hat, good person hacker. The name who works in the space industry and is an exemplar for students to look up to, um, you, um, Oh man. I'm hearing really. I can't, I can't, I can't, I can't imagine because the answer we normally get is the cricket sound. So we don't have individuals we've identified in those areas for them to look up to. I was going to be snarky and say, most white hackers won't even use their real name, but, um, there's a, there's an aura around their anonymity here. >>So, so again, the real question is, is how do we get them engaged and keep them engaged? And that's what Amy was pointing out too. Exactly the engagement and sticking with it. So one of the things that we're trying to do through our competition on the state level and other elements is providing connections. We call them ambassadors. These are people in the business who can contact the students that are in the game or in that, uh, challenge environment and let them interact and let them talk about what they do and what they're doing in life would give them a challenging game format. Um, a lot of computer based training, um, capture the flag stuff is great, but if you can make it hands on, if you can make it a learn by doing experiment, if you can make it am personally involved and see the benefit as a result of doing that challenge and then talk to the people who do that on a daily basis, that's how you get them involved. >>The second part is as part of what we're doing is, is we're involving partnership companies in the development of the teams. So this year's competition that we're running has 82 teams from across the state of California, uh, of those 82 teams at six students team, middle school, high school, and many of those have company partners. And these are practitioners in cybersecurity who are working with those students to participate. It's it's that adult connectivity, it's that visualization. Um, so at the competition this year, um, we have the founder of Def con red flag is a participant to talk to the students. We have Vince surf as who is of course, very well known for something called the internet to participate. It's really getting the students to understand who's in this. Who can I look up to and how do I stay engaged with them? >>There's definitely a celebrity aspect of it. I will agree. I mean, the influencer aspect here with knowledge is key. Can you talk about, um, these ambassadors and, and, and how far along are you on that program? First of all, the challenge stuff is anything gamification wise. We've seen that with hackathons is just really works well. Grades, bonding, people who create together kinda get sticky and get very high community aspect to it. Talking about this ambassador thing. What does that industry is that academic >>Absolutely partners that we've identified? Um, some of which, and I won't hit all of them. So I'm sure I'll short changes, but, uh, Palo Alto, Cisco, um, Splunk, um, many of the companies in California and what we've done is identified, uh, schools, uh, to participate in the challenge that may not have a strong STEM program or have any cyber program. And the idea of the company is they look for their employees who are in those school districts to partner with the schools to help provide outreach. It could be as simple as a couple hours a week, or it's a team support captain or it's providing computers and other devices to use. Uh, and so again, it's really about a constant connectivity and, uh, trying to help where some schools may not have the staff or support units in an area to really provide them what they need for connectivity. What that does gives us an opportunity to not just focus on it once a year, but throughout the year. So for the competition, all the teams that are participating have been receiving, um, training and educational opportunities in the game of education side, since they signed up to participate. So there's a website, there's learning materials, there's materials provided by certain vendor companies like Wireshark and others. So it's a continuum of opportunity for the, >>You know, I've seen just the re randomly, just going to random thought, you know, robotics clubs are moving den closer into that middle school area, in fact Fleischer. And certainly in high schools, it's almost like a varsity sport. E-sports is another one. My son just combined made the JV at the college Dean, you know, it's big and it's up and serious. Right. And, um, it's fun. This is the aspect of fun. It's hands on. This is part of the culture down there you learn by doing, is there like a group? Is it like, um, is it like a club? I mean, how do you guys organize these bottoms up organically interest topics? >>So, so here in the college of engineering, uh, when we talk about learning by doing, we have learned by doing both in the classroom and out of the classroom. And if we look at the, these types of, out of the classroom activities, we have over 80 clubs working on all different aspects of many of these are bottom up. The students have decided what they want to work on and have organized themselves around that. And then they get the leadership opportunities. The more experienced students train in the less experienced students. And it continues to build from year after year after year with them even doing aspects of strategic planning from year to year for some of these competitions. So, yeah, it's an absolutely great experience. And we don't define for them how their learned by doing experiences should be, we want them to define it. And I think the really cool thing about that is they have the ownership and they have the interest and they can come up with new clubs year after year to see which direction they want to take it. And, you know, we will help support those clubs as old clubs fade out and new clubs come in >>Trunk real quick. Before we go on the next, uh, talk track, what, what do you recommend for, um, middle school, high school or even elementary? Um, a little bit of coding Minecraft. I mean, what, how do you get them hooked on the fun and the dopamine of, uh, technology and cybersecurity? What's your, what's your take on that? >>On, on this aspect, I like to share with you my experience as a junior high and high school student in Texas, the university of Texas in Austin organized a competition for every high school in Texas. If we phew from poetry to mathematics, to science, computer engineering, but it's not about with university of Texas. The university of Texas is on the serving SSN for the final competition that we divide the competition to be strict and then regional, and then spit at each level, we have local university and colleges volunteering to host it competition and make it fun. >>Also students with private enterprises to raise funding for scholarship. So students who see the competition they get exposed to so they can see different option. They also get a scholarship when they attend university in college. So I've seen the combination in competition aspect would be a good thing to be >>Got the engagement, the aspiration scholarship, you know, and you mentioned a volunteer. I think one of the things I'll observe is you guys are kind of hitting this as community. I mean, the story of Steve jobs and was, was building the Mac, they call it bill Hewlett up in Palo Alto. It was in the phone book and they scoured some parts from them. That's community. This is kind of what you're getting at. So this is kind of the formula we're seeing. So the next question I really want to get into is the women in technology, STEM, underrepresented minorities, how do we get them on cybersecurity career path? Is there a best practices there, bill, we'll start with you? >>Well, I think it's really interesting. First thing I want to add is if I could have just a clarification, what's really cool that the competition that we have and we're running, it's run by student from Cal poly. Uh, so, you know, Amy referenced the clubs and other activities. So many of the, uh, organizers and developers of the competition that we're running are the students, but not just from engineering. So we actually have theater and liberal arts majors and technology for liberal arts majors who are part of the competition. And we use their areas of expertise, set design, and other things, uh, visualization of virtualization. Those are all part of how we then teach and educate cyber in our game effication and other areas. So they're all involved in their learning as well. So we have our students teaching other students. So we're really excited about that. And I think that's part of what leads to a mentoring aspect of what we're providing, where our students are mentoring the other students. And I think it's also something that's really important in the game. Um, the first year we held the game, we had several all girl teams and it was really interesting because a, they, they didn't really know if they could compete. I mean, this is their, their reference point. We don't know if they did better than anybody. I mean, they, they knocked the ball out >>Of the park. The second part then is building that confidence level that they can going back and telling their cohorts that, Hey, it's not this thing you can't do. It's something real that you can compete and win. And so again, it's building that comradery, that spirit, that knowledge that they can succeed. And I think that goes a long way and an Amy's programs and the reach out and the reach out that Cal poly does to schools to develop. Uh, I think that's what it really is going to take. It. It is going to take that village approach to really increase diversity and inclusivity for the community. >>That's the flusher. I'd love to get your thoughts. You mentioned, um, your, your outreach program and the dropoff, some of those data, uh, you're deeply involved in this. You're passionate about it. What's your thoughts on this career path opportunity for STEM? >>Yeah, I think STEM is an incredible career path opportunity for so many people. There's so many interesting problems that we can solve, particularly in cyber and in space systems. And I think we have to meet the kids where they are and kind of show them, you know, what the exciting part is about it, right. But, you know, bill was, was alluding to this. And when he was talking about, you know, trying to name somebody that you can can point to. And I think having those visible people where you can see yourself in that is, is absolutely critical and those mentors and that mentorship program. So we use a lot of our students going out into California, middle schools and elementary schools. And you want to see somebody that's like you, somebody that came from your background and was able to do this. So a lot of times we have students from our national society of black engineers or a society of Hispanic professional engineers or our society of women engineers. >>We have over a thousand members, a thousand student members in our society of women engineers who were doing these outreach programs. But like I also said, it's hitting them at the lower levels too. And girl Scouts is actually distinguishing themselves as one of the leading STEM advocates in the country. And like I said, they developed all these cybersecurity badges, starting in kindergarten. There's a cybersecurity badge for kindergarten and first graders. And it goes all the way up through late high school, the same thing with space systems. And they did the space systems in partnership with NASA. They did the cybersecurity and partnership with Palo Alto networks. And what you do is you want to build these, these skills that the girls are developing. And like bill said, work in and girl led teams where they can do it. And if they're doing it from kindergarten on, it just becomes normal. And they never think, well, this is not for me. And they see the older girls who are doing it and they see a very clear path leading them into these careers. >>Yeah. It's interesting. You used the word normalization earlier. That's exactly what it is. It's life, you get life skills and a new kind of badge. Why wouldn't learn how to be a white, white hat hacker, or have fun or learn new skills just in, in the, in the grind of your fun day. Super exciting. Okay. Trung your thoughts on this. I mean, you have a diverse diversity. It brings perspective to the table in cybersecurity because you have to think like the other, the adversary, you got to be the white headed hippie, a white hat, unless you know how black hat thinks. So there's a lot of needs here for more, more, more points of view. How are we going to get people trained on this from under represented minorities and women? What's your thoughts? >>Well, as a member of, I took a professional society of directed pool in the electronic engineer. You have the, uh, we participate in the engineering week. We'll be ploy our members to local junior high school and high school to talk about our project, to promote the discovery of engineering. But at the same time, we also participate in the science fair that we scaled up flex. As the squad organizing our engineer will be mentoring students, number one, to help them with the part check, but number two, to help us identify talents so that we can recruit them further into the field of STEM. One of the participation that week was the competition of the, what they call future CV. We're still going, we'll be doing a CT on a computer simulation. And in recent year we promote ops smart CV where CT will be connected the individual houses to be added in through the internet. >>And we want to bring awareness of cybersecurity into competition. So we deploy engineer to supervise the people, the students who participate in the competition, we bring awareness, not in the technical be challenged level, but in what we've called the compound level. So speargun will be able to know what is, why to provide cyber security for the smart city that they are building. And at the same time, we were able to identify talent, especially talent in the minority and in the room. And so that we can recruit them more actively. And we also raise money for scholarship. We believe that scholarship is the best way to get students to continue education in Epic college level. So with scholarship, it's very easy to recruit them, to give you and then push them to go further into the cyber security Eylea. >>Yeah. I mean, you know, I see a lot of the parents like, Oh, my kid's going to go join the soccer team, >>Private lessons, and maybe look at a scholarship >>Someday. Well, they only do have scholarships anyway. I mean, this is if they spent that time doing other things, it's just, again, this is a new lifestyle, like the girl Scouts. And this is where I want to get into this whole silo breaking down because Amy, you brought this up and bill, you were talking about as well, you've got multiple stakeholders here with this event. You got, you know, public, you got private and you've got educators. It's the intersection of all of them. It's again, that those, if those silos break down the confluence of those three stakeholders have to work together. So let's, let's talk about that. Educators. You guys are educating young minds, you're interfacing with private institutions and now the public. What about educators? What can they do to make cyber better? Cause there's no real manual. I mean, it's not like this court is a body of work of how to educate cybersecurity is maybe it's more recent, it's cutting edge, best practices, but still it's an, it's an evolving playbook. What's your thoughts for educators, bill? We'll start with you. >>Well, I don't really, I'm going to turn it off. >>I would say, I would say as, as educators, it's really important for us to stay on top of how the field is evolving, right? So what we want to do is we want to promote these tight connections between educators and our faculty and, um, applied research in industry and with industry partnerships. And I think that's how we're going to make sure that we're educating students in the best way. And you're talking about that inner, that confluence of the three different areas. And I think you have to keep those communication lines open to make sure that the information on where the field is going and what we need to concentrate on is flowing down into our educational process. And that, that works in both ways that, you know, we can talk as educators and we can be telling industry what we're working on and what are types of skills our students have and working with them to get the opportunities for our students to work in industry and develop those skills along the way as well. >>And I think it's just all part of this is really looking at, at what's going to be happening and how do we get people talking to each other and the same thing with looking at public policy and bringing that into our education and into these real hands on experiences. And that's how you really cement this type of knowledge with students, not by not by talking to them and not by showing them, but letting them do it. It's this learn by doing and building the resiliency that it takes when you learn by doing. And sometimes you learn by failing, but you just up and you keep going. >>And these are important skills that you develop along the way >>You mentioned, um, um, sharing too. That's the key collaborating and sharing knowledge. It's an open, open world and everyone's collaborating feel private public partnerships. I mean, there's a real private companies. You mentioned Palo Alto networks and others. There's a real intersection there there's, they're motivated. They could, the scholarship opportunities, trunk points to that. What is the public private educator view there? How do companies get involved? What's the benefit for them? >>Well, that's what a lot of the universities are doing is to bring in as part of either their cyber centers or institutes, people who are really focused on developing and furthering those public private partnerships. That's really what my role is in all these things is to take us to a different level in those areas, uh, not to take away from the academic side, but to add additional opportunities for both sides. Remember in a public private partnership, all entities have to have some gain in the process. Now, what I think is really interesting is the timing on particularly this subject space and cyber security. This has been an absolute banner year for space. The Stanhope of space force, the launch of commercial partnership, leaving commercial platforms, delivering astronauts to the space station, recovering them and bringing back the ability of a commercial satellite platform to be launched a commercial platforms that not only launch, but return back to where they're launched from. >>These are things that are stirring the hearts of the American citizens, the kids, again, they're getting interested, they're seeing this and getting enthused. So we have to seize upon that and we have to find a way to connect that public private partnerships is the answer for that. It's not one segment that can handle it all. It's all of them combined together. If you look at space, space is going to be about commercial. It's going to be about civil moving from one side of the earth, to the other via space. And it's about government. And what's really cool for us. All those things are in our backyard. Yeah. That's where that public private comes together. The government's involved, the private sector is involved. The educators are involved and we're all looking at the same things and trying to figure out like this forum, what works best to go to the future. >>You know, if people are bored and they want to look for an exciting challenge, he couldn't have laid it out any clearer. It's the most exciting discipline. It hits everything. I mean, we just talk about space. GPS is everything we do is well tested. Do with satellites. >>I have to tell you a story on that, right? We have a very unique GPS story right in our backyard. So our sheriff is the son of the father of GPS for the air force. So you can't get better than that when it comes to being connected to all those platforms. So we, we really want to say, you know, this is so exciting for all of us because >>It gives everybody a job for a long time. >>You know, the kids that don't think tick toxic, exciting, wait til they see what's going on here with you guys, this program, trunk final word on this from the public side, you're at the air force. You're doing research. Are you guys opening it up? Are you integrating into the private and educational sectors? How do you see that formula playing out? And what's the best practice for students and preparing them? >>I think it's the same in athlete university CP in the engineering program will require our students to be final project before graduation. And in this kind of project, we send them out to work in the private industry. The private company got sponsor. Then they get the benefit of having an intern working for them and they get the benefit of reviewing the students as the prospective employee in the future. So it's good for the student to gain practical experience working in this program. Some, some kind of, we call that a core program, some kind, we call that a capstone program and the company will accept the students on a trial PRCS, giving them some assignment and then pay them a little bit of money. So it's good for the student to earn some extra money, to have some experience that they can put on their resume when they apply for the final of the job. >>So the collaboration between university and private sector is really important. We, when I joined a faculty, normally they already exist that connection. It came from. Normally it came from the Dean of engineering who would whine and dine with companies. We work relationship and sign up women, but it's approach to do a good performance so that we can be credibility to continue the relationship with those company and the students that we selected to send to those company. We have to make sure that they will represent the university. Well, they will go a good job and they will make a good impression. >>Thank you very much for great insight, trunk, bill, Amy, amazing topic. I'd like to end this session with each of you to make a statement on the importance of cybersecurity to space. We'll go Trung bill and Amy Truong, the importance of cybersecurity space statement. >>We know that it's affecting components that we are using and we are connecting to. And normally we use them for personal purpose. But when we connect to the important system that the government public company put into space, so it's really important to practice cyber security and a lot of time, it's very easy to know concept. We have to be careful, but in reality, we tend to forget to partnership the way we forget how to ride safely. And with driving a car, we have a program called defensive driving that requires every two or three years to get. We can get discount. >>We are providing the cyber security practice, not to tell people about the technology, but to remind them not practicing cybersecurity. And it's a requirement for every one of us, bill, the importance of cyber security to space. It's not just about young people. It's about all of us as we grow and we change as I referenced it, you know, we're changing from an analog world to a digital world. Those of us who have been in the business and have hair that looks like mine. We need to be just as cognizant about cybersecurity practice as the young people, we need to understand how it affects our lives and particularly in space, because we're going to be talking about people, moving people to space, moving payloads, data, transfer all of those things. And so there's a whole workforce that needs to be retrained or upskilled in cyber that's out there. So the opportunity is ever expensive for all of us, Amy, the importance of cybersecurity space, >>Uh, and the, the emphasis of cybersecurity is space. Just simply, can't be over emphasized. There are so many aspects that are going to have to be considered as systems get ever more complex. And as we pointed out, we're putting people's lives at stake here. This is incredibly, incredibly complicated and incredibly impactful, and actually really exciting the opportunities that are here for students and the workforce of the future to really make an enormous impact on the world around us. And I hope we're able to get that message out to students, to children >>Today. But these are my really interesting fields that you need to consider. >>Thank you very much. I'm John foray with the cube and the importance of cybersecurity and space is the future of the world's all going to happen in and around space with technology, people and society. Thank you to Cal poly. And thank you for watching the Cypress of computer security and space symposium 2020.

(dramatic music) >> Hey everyone, welcome to the special CUBEConversations here in Palo Alto, CA theCUBE Studios. I'm John Furrier, host of theCUBE, founder of SiliconANGLE Media. We are here for some exclusive news around security audits, blockchain smart contracts, and a hot new startup Amber Data we have the Chief Operating Officer Tongtong Gong who's here, Chief Operating Officer of Amber Data, great to see you! You guys, I've interviewed Shawn Douglass, the CEO, founder, before and he was really getting the technology going. Amazing progress, we have some exclusive discoveries here, welcome to theCUBE! >> Thank you, thank you, thanks for having me here. It's awesome, we've done so much in the past couple weeks, and really excited to announce that we have taken security audits, automated that to be able to provide automated at scale security audits for all the smart contracts, Ethereum, through our platform. >> This has been a huge problem, we've been covering it for the past year, with video but also in the blogs, Ethereum specifically has been the developer chain of choice, people are using Ethereum, programming on it, and that's where a lot of the DApps, decentralized apps, which we think there's going to be a tsunami of, we're a bit bullish on it, but the problem is that everyone went in and rushed with these ICO's, and they didn't think about, "Hey we better make sure our token generating event works" because they've got to do a smart contract on that, and then ultimately these marketplaces that will be emerging from these apps through the communities will be a lot of smart contracts, as the transaction of choice. This is what is the benefit of token economics. The problem is, security. The security audits have been a pain in the butt, they've been expensive, and there's been a time lag in getting it done. So you've got a time factor, too slow, too expensive, and it was last minute. >> Right. >> This has been a huge problem. Are you saying that you solved that problem? >> Yeah, kind of! So give you some stats. There are about 7.8 million to 8 million smart contracts on the chain today. On average, there's about 500-600 smart contracts get deployed every day into Mainnet Ethereum. What we've done, we talked to a lot of security teams that's in this space, and at the end of the day everybody use the same tools, set of tools, to preform security audits. What we have done, is we have programatically did that so we can run security audits on every smart contract on the chain. So we launched this feature last Friday, what we did is we picked the top 2000 smart contracts, based on transaction values-- >> On the Mainnet? >> On the Mainnet. And we preformed the audits on those, and last night, yeah three days later, we preformed all 8000 smart contracts that's been created and deployed in the past 90 days. So the top 2000 active ones, and the 8000 recently deployed ones, we preformed security audits on those. >> So this is pretty incredible, so I want to make sure I get this right. If this is the case, this is the first ever automation, or devOPS like approach to smart contract audits and security. So let me just kind of slow down if you don't mind. Today, most people will go in and manually look at code reviews or use some tooling to do that, and then they get a report. Businesses have been doing that, OSHO does that, many more do it, and they're bringing tools to the market, they are too, but I don't think anyone's actually automated at volume. So you're saying, you're automating, ingesting data from the chain-- >> Mhm. We analyze the bytecode as well as the source code to identify vulnerabilities and issues, things like integer overflow into the code, and we actually assign custom, we have our own scoring system to score basically the vulnerability exposure of the smart contract. >> Okay so I want to kind of push back on that because I'm skeptical. So, you do byte review-- >> Bytecode >> Bytecode and source code review, and then it's a black box and you type up a report, or you actually flag the code itself? Do you service it automatically? Does that happen automatically? Take me through what you do manually, and what happens with the computers. What is automated? >> Everything's automated. So we integrated the tools that every expert uses in this space today, to run the security audits on the smart contract and the bytecode and then we flag the particular source code and function calls that's flagged with the issue-- >> That's in the code itself? >> That's in the code itself, in that service, through our website, through our console, and you can actually see it. You can search on any smart contract and the console dashboard will show you the real time live streaming events of your smart contract function calls, as well as the vulnerability-- >> This is amazing. So this means that you can save a lot of time, love this feature, this is exciting. This is actually the first news I've ever hear of this, so I want to make sure I get it right. How many contracts can you do? How fast does it take? So you mentioned you've ingested last week, stuff off the chain, how many contracts was that? >> We did last week, 2000 and then up to last night, we finished 8000 smart contract scans. We're continuing to do that for every smart contract on the chain. >> How fast is this, because I remember back when I was learning how to code for the first time, back in the old days, you had to press a button, you'd have a compiler, and you'd get a bug in the line, syntax error, there it is. That's the normal kind of old school computer science. Syntax, compiler, interpreter, whatever you want to call it. It sounds like you're doing something similar, the same kind of speed. It's code review, analysis to the contracts, security through the tools... How fast is it? I mean, how long does it take to do a review of one smart contract, for instance? >> Actually, I don't know that. I would say minutes. >> Not days? >> Not days. No. Minutes. >> So it's not like it goes out, hourglass... Check your email it'll be there? It happens pretty much on the fly? >> It happens pretty much on the fly, real time. >> So how many contracts can you guys do in a day? >> We've done 8000 in three days, so... A lot! (both laughing) And we have ten machines running right now as we're speaking-- >> So you throw some clout at it, scale up-- >> Exactly, scale up. >> Scaling out is easy to do, you just go... >> Our goal is to basically make it very easy for developers to understand the state and health of their smart contract and then they can go find consultants, experts to fix those vulnerabilities and issues. >> Yeah, this is going to be a rising tide. I think, rising tide floats all boats when you have these emerging markets. You move to the next problem, and you do. Jeff Frick always says that in theCUBE and he's right! You take away security, you're now enabling this tool for these consultants to actually add more value. >> Exactly. >> Is that the focus? Do you guys even know who's going to use this tool yet? Obviously, this is a game changer. I mean, if I'm a data scientist I love this. Also I'm a trader, I might want the data, I'm a risk management, audit compliance person? I mean...you guys-- >> Yeah! At Amber Data our mission has always been providing, enabling infrastructure, enabling tool sets to allow developers, to allow operators, to allow the industry, to allow businesses to adopt blockchain, that's always been our mission and we have built the splunk, you know like search, a feature for blockchain, we have built APM, we have built dissimilar Mixpanel... It's all about providing access to data and to information, to allow everyone to have a better understanding, better transparency into the state and health of the blockchain, the state and health of their smart contracts. So that's you know, in line with-- >> So talk about the scoring thing, because okay, I love this automation I think that that's a game changer. So congratulations, this is the first I've heard of it, and I think this might be the first news in the industry out there. How does it work beyond that? What else do you guys do? Are you ingesting, are you adding overlays to it? What is the focus next? I mean, you're ingesting it, you're doing some security audits... Where does it go from there? >> So, we're actually working with the Web3 Data Foundation. So the Web3 Data Foundation is building a decentralized data marketplace to allow everyone in the ecosystem to list, subscribe, consume, distribute, monetize data assets that's generated by the blockchain and data that's on blockchain. >> So what's the URL for that? Web3... >> Web3data.org >> Three the number or three... >> Three the number, yeah. >> So web3 number data dot org? >> Yeah. >> Okay and is that an open community? Is it a foundation? >> Yes it's a nonprofit foundation, and I believe they're launching a token, Web3 Data token, and Amber Data's working with the Web3 Data foundation as a launch partner to utilize the data ingestion pipeline we have built and to serve up all the data for everyone to have access to it. >> Okay so what's your business model at Amber Data? Are you going to have your own token? Are you going to use the foundation as the token holding place? Can you just take us through the relationship of Amber Data with the foundation? I mean, I get the foundation but what you're doing here is essentially you're building IT operations into the blockchain and scaling things with automation, which certainly is only going to get better with more compute and A.I and other cool things, so I love that. How do you make money? Is it a token model? Is it just, classic, you get paid? What's the relationship? Is the foundation issuing tokens, do you have your own token? Take us through that. >> So the Web3 Data Foundation is the one issuing the token. We are the launch partner, so we are using the bulk diagnostic data ingestion pipeline that we can ingest all the data, and we're building together, building the data marketplace using smart contracts, to enable everyone to list, curate, consume, distribute, monetize the data. You think about it, right? Data on blockchain is just a fraction of the data out there. And as staff development, going on, as a trading application going on, there's a lot of data that's going to be generated by blockchain as well, and those datas aren't getting captured, analyzed, and utilized today. I think today, as a trader, investor, or as a developer, people don't have access to this data, to have data driven decisions, to help them continuously improve. Whether it's application or investment decisions. So the data marketplace will enable everyone to be able to have that access. >> And also it might enable more faster solution of decentralized applications-- >> Exactly. >> Which, Fred Kruger and I were talking on Twitter, I mean Facebook, about this, that we think that's the killer app, it's going to be the tsunami of apps coming. But all these chain problems are out there, so it's a little bit of a resetting going on in the industry. Obviously you see that with some of the pricing and funding and everything, but for the most part we see a big market coming. So I've got to ask you, the obvious question from there is, which chains are you supporting? You mentioned Mainnet which is great for Ethereum-- >> Yeah today we're supporting Ethereum Mainnet, and Rinkeby, the test net. We also support Aion's Mainnet and test net. We also support Stellar, we're working on EOS and TRON as well, so we have open sourced our data collector to allow community to contribute to that and we'll use Web3 Data Token to incentivize the community to contribute, to verify, to enrich the data. >> So I've got to ask you the security question, maybe this might be for more the nerds and the geeks, delving down in the product level, but maybe you can get it. Security is huge, so I'm skeptical. You're doing scoring, can you be hacked? What's the security answer to that? Like, whoa if she's controlling the score, I might want to spoof the code and take over and say it's okay, ya know? >> The code we get is actually on the chain, it's the code that you put on the chain, so good luck spoofing the data on the blockchain. >> That's the whole point of block chain, that's already answered. That's a dumb question, I got that. I always ask dumb questions. Alright, so what's next for you guys? How big are you guys, what's the story? I've been following you guys on Twitter and Telegram, you've been traveling a lot. What's the update on the company, what's the status? >> So we are, as a launch partner for Web3 Data Foundation, right now there's a token sale, we're in the middle of closing our presale. It's a soft offering, and we're building and expanding the team as we're speaking. >> How much are you raising on the staff, can you talk about that? >> No. >> No? Okay you don't have to say the number. Just be careful, it gets hard to raise too much. So the foundation, and you guys. Okay, I want to ask you a personal question, we love women in blockchain, I wear the "Satoshi is female" shirt as much as I can... How did you get into this? Because there's a lot of women coming into blockchain, more than people are advertising. I'm seeing a lot more women in tech, certainly a lot more women in crypto. Blockchain and crypto, you guys are doing almost a cloud devOPs serious venture here. How did you get into this, what's your story? >> I've always been a cloud girl. I started my career building Yatuzi computing, enterprise grid computing. I was 23 years old and working for Axiom in a data center in Arkansas, and I'm the only one that wears high heel6s in data center, and get stuck in a vent you know? That's my background, so it's not a far stretch to understand blockchain and the usefulness of it if you talk about distribute computing, distribute storage. So it's very natural for me, from a technology perspective, get into this space. On a personal note, I really believe in decentralization, and I believe the change it's going to make to our lives and to our offspring's lives in the future. >> It's real, you think? >> It's real. It's here to stay. >> So what's your vision of blockchain? What are people not getting? Obviously there's a lot of scams out there that have kind of tainted on the ICO side, but what are people missing? When you talk to people, you have kind of like, "Oh I get it" and people kind of of like "I don't really see that" ? What's the main thing that they're missing, what's missing? >> I think it's missing that killer Dapp to get people to realize "Oh it's actually easy to use". I don't have to think about the inner workings, and it just works. My mom still lives in Beijing, I talk to her on Skype all the time, she's not worrying about TCPIP, she's not worrying about, how is this phone call getting encrypted or not encrypted? What's my network bandwidth? She just use the phone and call me, like I'm right next to her. I think as we develop building the apps, people don't think about that they're using blockchain, they just use it. >> It's like explaining it to a parent or someone who's not technical.. "Hey how does the internet work? Can't I just "type a keyword in to the browser or a search engine?" Instead today, it's more like "Hey, you know how BGP works?" and "You know how packets move around?" It's so hard to explain, so it's got to be easier. >> Way easier, yeah. >> Totally agree, totally agree. Well Tongtong, thank you for coming on theCUBE, appreciate it, great update, exclusive news. Automation, bringing cloud computing and utility computing, real geeky stuff to the table here. This is theCUBE Conversation and I'm John Furrier. Amber Data COO, Tongtong Gong here, inside theCUBE. Thanks for watching. (dramatic music)