>> from Seattle Washington. It's the Q covering AWS. Imagine nonprofit brought to you by Amazon Web >> service is >> Hey, welcome back already. Jeffrey here with the Cube were in >> the waterfront, actually in Seattle, Washington. It's an absolutely gorgeous August day. We're here for the AWS. Imagine nonprofit event. It's the fourth year they've had. It is the first year's been kind of open to the public. It was invitation only. And we're excited to be here for our first time. Our >> guest is here for his first time, too. And >> we're excited to sit down with side L. Nash. He is the vice president. And of I t and C i o for the National Council on Aging. Say great to see you. >> Thank you. Good to see you. Yeah. So, first >> off, just kind of impressions on the event So far. Really good keynotes this morning. And they got a full two days planned for you. >> Yes, it was an excellent good note. Keynote speaks to the speech this morning and, uh, started off talking about impact and how nonprofit organizations make it make a difference in the world. >> Right. So National Council of Aging, the population is aging Maur Every day they keep sending me my my card in the mail that keep pretending I'm not old enough to get. But >> don't try to pretend exactly they are >> double AARP. Thank you very much for the car, but, um, there's a lot of unique challenges with as the population continues to get holding. What are some of your organisation's priorities? How do you address this kind of growing population in our society? >> So I'll share with you some statistics on aging. So there are about 72,000,060 and older adults in the U. S. 70 >> 1,000,000 to three on its growing >> and growing. It will be 92,000,000 in 2030. So it's a growing larger segment of the population. People are living longer, saving less about but half of those so are 60 plus have saving off about $30,000 about 80% off 60 plus have about maybe to chronic disease conditions. So people are living longer, saving less money, and obviously with that, there are a lot of challenges, and this is where we step in. So we step in. Our mission is to help people age healthier and wealthier, try to make sure that they planned correctly for their savings. And they plan correctly also for their convention there chronic diseases and managing their health in general. And so for that, we have a lot off just products, actually that help older adults figuring out there how to live in older and healthy life. One of them is our flagship product, helping people get access to ah, federal, state and local government benefits. It's called benefits. Checkup is the largest system decision support system in the country that helps older adults figuring out how what benefits take all 54 and how to apply. And we walked them through that whole process. >> So it's also not necessarily the most technically astute population, either, especially today seniors who didn't grow up his digital natives like a lot of the kids are today. And >> as you said, your your guys >> objective number one is economic security. Maybe not necessarily number one, but top of the list and then healthy living. And they don't have the benefit of of time for therefore one case and stuff to grow. So these air pretty unique challenges. How are you helping him? And then you know we're here in eight of us. What role has eight of us played in helping you reach your your constituent? >> Clear? You're asking a lot of questions in one. So let me try and answer them one by one. So let's take a >> look at the aging population, especially the older adults. 70 plus those who actually don't have. Ah, I don't know. They're not necessity technology savvy, but they have Ah, they have cell phone. It's over. 73% of them have cell phones and some have smartphones. S o. We looked at the different ways of trying to reach out to them. And one of the things that we experimented with is looking at an SMS texting pilot. So we actually started that pilot and was very successful. And well, now we're rolling out into a full production system. It's a we found out that it's a great channel. It's very simple asking simple questions. Did you apply yes or no? Just answer us if you were to do one or two. So tell us give us a very simple answer and we found that the engagement rates are way above the average industry. People tend to respond to text messages for better than actually telling them. Hey, there's the mobile app. Go download my mobile already So that's one aspect of it on the AWS Sod off it. So when I joined and see away about a year and 1/2 ago, we were in Private Cloud and in that situation we had a lot of single point of failures and disaster recovery was in bad shape. And so we realized that we needed to move into a new and more robust environment, one that solved the single all the risks that we had from disaster recovery. Single point of failures to also being able to innovate quickly and fast. And so we looked that we started the ah migration process to the cloud and we ended up on AWS back in February. This year would move 95% of our assets to the cloud to AWS Cloud and we medicated the two major risks. The single point of failure is disaster recovery and so on. And with that, we also have a lot of other tools that are out of the box that we're using right now with the AWS platform. >> That's great So, um, I want it back up to the S, the best comic cause That's really interesting. So how do you find your customers? How do you get people get engaged? Obviously, art center the card in the mail. You know, there's a lot of organizations that that we get involved with. How do you directly engage with your clients? >> So we do a lot of digital marketing, believe it or not. So we spent a lot off time money and energy into digital marketing on Facebook. So a good number of older adults are on Facebook. There's also a good percentage of them that are on YouTube. Unfortunately, older adults spend about 46 hours watching either TV or videos on the Web, those who have access to the Web. So that's one way we're trying to reach them. So these are our sort of marketing funnels. In addition to that, we have about close to 100 centers around the U. S. Where older those can actually go in and be helped and go walk through the process of applying for federal state local government benefits. And so we have. They're called benefits benefits centers. And so the those centers are open to the public. We also try to collaborate with different with different organizations around the country, through through whom we get older adults too engage with us and joined the benefits checkup program. And with that, we we ask people to our 10. So we take a very cautious and very respectful approach to data and privacy to ask people to opt in. And we tell them about how we're using the data. We encrypt the data address. We take very caring very good care of it. We don't share it outside of organization. So we have our own internal data privacy principles. So we take this matter very seriously again. Our objectives always the hope older adults live a better, healthier and wealthier life, >> right? I just love that the older people are now using Facebook and SMS like kids. >> 15 years ago, they moved on >> to other platforms. Thank goodness for the old folks keeping the Facebook and, uh >> so let's shift gears. A little >> bit of talk about your transformation in your movement to the cloud. How big of an effort was that? How long did it take? And, you know, hasn't really opened up the innovation because there's clearly cost savings. And as you talked about a single point of failure and kind of mitigating the negatives, but as well as we've seen over and over again, really, the benefits from from Cloud are really that innovation and delivering service is faster. So how's your experience? >> That's exactly right. So So let me talk a little bit about the traditional transformation. So about, I would say, year over year ago, we started our digital transformation initiative. It's really focused on customers, we call it, knowing our customers as individuals with individual needs. Traditionally organizations like ours looked at older adults. In the perspective, off percentages averages, on average is is how old they are on average, in this is their income. On average, this is their health. But in reality, every older adult is an individual that has specific and individual needs, and we need to really take a look at that and caters to those very specific needs that they actually changed over time. So the transformation really enabled it. We needed to move to a cloud where we can have products immediately that we can spend off and use a I machine learning products and so on. And so I'm gonna go back and talking more about our a digital transformation and the perspectives off it. So our objective long term is to build was recalling the the aging Well, aye aye. Engine. It's basically imagine an older adult waking up in the morning and trying to decide what are the top best three things for me to do. Stop the actions for me to do to improve my life. And we wanna help that older adult make those decisions easily and quickly through a frictionless interactions. Frictionless. Conversational. Aye, aye. Speaking to an Alexa like voice enabled smart speaker asking Alexa, what should I do today? Alexis, respond. The weather is nice out there. Call your friend. Go for a walk. Call your doctor, get the lab results and so on. And check your benefits on benefits. Check up and figure out and improve your life. So the idea is to really get the person to actively and the actively using technology and simple, frictionless way to be able to make those decisions that improve their lives. So for us to do this kind of work to build the aging. Well, Aye, aye. Engine. It is impossible without being on a cloud like >> a w. Interesting. So, uh, first time I've heard about Lexus since we've been here. A lot of talk about Lex at the education conference a couple of weeks back. So is Alexa. Pretty key piece of your strategy going forward, you really see voice as a different type of communication. You mentioned. That's a message. Just kind of old, but really effective. How do you see Alexa playing >> so absolutely so voice enabled communication channels. So we look at it as actually we look at our communication with older adults. We look at it as an Army channel communication. Every person have their own preference of the way they interact with technology. Some people prefer SMS. Others like to speak to Alexa. Others like to go through the web and so on. Some are on Facebook or YouTube, etcetera. So each we have our own choices. And that's exactly why we need to look at the older adults as individuals with their individual needs. And then our job is to deliver those to deliver all products through those different channels individually. So delivering the right product with the right customer at the right time and through the right channels. So lax is one of the channels it is. It's not the only channel or the voice channel I would call it is not the only channels. What we found out is that older adults find Alexis is very engaging. It reduces social isolation. It helps with the many other tests, especially for those who are visually inferred. The the complexity. The challenge for older adults is setting it up, so that's what we're trying to look at. Ways of trying to packages will be package so that it is possible for the older adult to plug it in and be able to use it. The other thing that we discovered, we probably need to look at family caregivers as the customer segment of the customer target that we would work with really enable looks, um, >> interesting. Let's see, it seems like a natural fit once you get kind of the tone and the and the comfort worked out, and I would imagine you're writing all types of specific things for to do and types of activities for Alexa to do for the specific needs of this older generation, >> so yeah. So we started >> a very small proof of concept project with Alexa trying to engage an experiment for me, everything that we do has to bring in value. And I need to also make sure that we are when we deliver a product or customers. That product actually delivers that value and engages the customers. So we know that there are there is the value in there were also working with partners on delivering this voice channel. So I know that we have, as a non profit organization with our, you know, a limited resource is. And so we look at partners as a way to enable those votes channels on the different channels that we have >> exciting, exciting times. And I look forward to watching that innovation pulls out at a high rate of speed. So thanks for taking a few minutes and safe travels home. >> Okay. Thank you, Seed. I'm Jeff. You're watching the keyboard aws. Imagine >> in Seattle. Thanks for watching. We'll see you next time

>> Announcer: Live, from Atlanta, Georgia it's theCUBE covering IFS World Conference 2018. Brought to you by IFS. (upbeat pop music) >> Rebecca: Welcome back to theCUBE's live coverage of IFS World Conference 2018 here in Atlanta, Georgia, I'm your host Rebecca Knight, along with my co-host Jeff Frick. What you're seeing right now are the hordes of people here at IFS World, and we are here to talk to our two guests Sumair Dutta, who is an analyst at the Service Council, and Mark Brewer, who's the Global Industry Director of Service at IFS. Thanks so much for joining us! >> Well thanks for having us. >> Thanks for having us, yeah absolutely. >> Jeff: How did we draw you away from the cappuccino machine of there? The-- >> I know, I know. >> Jeff: The baristas are workin' hard. >> Rebecca: It's very buzzy, it's very exciting. So talk to us a little bit about FSM six which is, which is going to be released at the end of this year. What's new, what's exciting? Right, so IFS field service management six, you know I think the first point to make is that we already have what is recognized as the most complete solution in field service management, so this release was really focused on improving the user experience. It's a common theme throughout this conference, I think you'll hear. So how do, not only our customers, but our customers customers, their partners, all stakeholders get the most intuitive user experience out of this solution, to derive the value that encapsulated in there. I think another thing that goes along with that is, we're moving to a zero customization goal. That is to say, you'll still have, you know, ultra levels of configurability in this product from a workflow perspective, from a user interface perspective, from a business rule perspective. But instead of doing that through customization, you're doing that through configuration. Which means, you can sit on an evergreen model, you're not restricted on your upgrades. You can start on the latest and greatest, in other words, our customers are able to encapsulate an excellent experience and their business rules all within a standard offering. >> Field service management you don't really necessarily think of as inside the ERP suite, right? Kind of a hang off, I dunno what the right term is. So how does that make this offering different within all the offerings that IFS has? >> Mark: Yeah, it's a great question. So one of the uniques about the IFS FSM solution, is that it is complimentary to whatever back-end systems you already have, so let's say you have something from one of our peers in the business, so you're not runnin' IFS apps for your ERP, you're runnin' somethin' else, that doesn't matter. You can still derive best in class field service management from the FSM solution, it integrates with pretty much any other back-end. So if you truly are a service focused operation of which many companies, you know, 70% of their revenue derive from service, you can get that, if you like best-in-class service capability, without throwin' anythin' out that you're already got. And that's why FSM is somewhat different to the majority of other portfolio applications at IFS. >> So talk a little bit about how closely you work with your customers, I mean that is a real point of pride for IFS is how customer focused, how customer centered you are. So describe your process, in how you collaborate with customers, in terms of what they want the end product to look like. >> Really glad you asked that question, and it's incredibly timely for this event because, when the event closes on Thursday, we actually have a day dedicated to what we call our customer advisory council. So we have 12 of our strategic field service customers gathering in Atlanta to effectively help plan the roadmap. So we're not talkin' about tomorrows feature functions, we're talkin' about a three to five year strategy from their business. So this is not, if you like, the users asking for features, it's actually C-level, and executive management level from our customers that are actually giving us insights into where their taking their service operation in the future. Not really a technology discussion, but a business strategy discussion. We can then take that away, that involves our R&D organization as well, by the way, we take that away, we augment that with our own roadmap goals, technology that is obviously, you know, within the field service space already, AI, AR, IOT, and so forth, and, you know, bringin' those three things together, that's how we ensure that we are building applications, not just for today, but for what's next, as per the conference tag-line, you know? So heavily customer centric. Just on Friday, all those customers in the room, tellin' us where they want to go. >> Sumair, what are you seeing as sort of trends in this field service market, and how are you seeing IFS responding to those? >> Yeah that's a great question. You know field service as a whole wasn't something that was talked about previously, and we see so much more interest in the field of field service and the overall equation of aftermarket service and support. You know, previously that was a bi-product of being a business, we have a product, we need to support it, so we need to maybe throw some resources, but let's do that at the lowest cost. Now you'll see more and more companies talk about you know, service as a profit center, that we need to make money as a service. And this is primarily being driven by three major themes, three major disruptors: you have technology of course, and all of the new tools, AR, AI, augmented reality, artificial intelligence, IOT, and that's driving companies to figure out what is there story in each one of those solutions? We're not all there, no one's solved the problem, but we're all trying to figure out where do those fit into the way we deliver service, and the way our customers consume service? Then you have in field service specifically, workforce related challenges, disruptions. In our research we find about 70% of companies are going to face a talent shortage in the next five to 10 years. And this is research that's done across manufacturing and other disciplines as well. So your capacity in the sense of the number of workers you have is not going up, and you have to bring in new workers, you have to attract new talent, but then you have that outgoing flow of workforce and knowledge that's leaving your organization. So you're trying to balance all of those needs. And then eventually customers are demanding more, and you might say what does that mean you know, in an industrial setting? Well we're all consumers in some form, and we have consumer experiences, you know, the Amazons of the world, the Ubers of the world, who give us an element of convenience and access to information. And that is beginning to translate more and more so into the B to B service environment. So as a service organization you're balancing: customer needs which are rising, you've got a talent pool or a labor pool that's probably declining, and all of these disruptive technologies that you have to incorporate into your business, and so as a company as IFS that has been very customer centric, IFS has done a lot around field service management to improve some of the workforce capacity challenges with their solutions around FSM, they are taking a greater stake in some of the customer engagement solutions, with the acquisitions of MPL systems, and potentially future acquisitions down the road, and then from a technology point of view, I like IFS' approach, they've been, they're not quick to jump to the end, to say, you know, here's our AI solution per se, but they're essentially trying to establish those steps for companies to get from point A to point B to point C, to say here's where analytics can help you, here's where mobility can help you, it's a little bit more of a pragmatic approach as opposed to a marketing first approach, and so IFS has done a really good job in terms of the workforce elements, the technology elements, and now moreso on the customer engagements side as well. >> You know what really strikes me, as we're having this conversation is: the way that customers engage with companies has changed dramatically, right? Certainly on a consumer side, and a business side, so much now the engagement is via an electronic interface. And I can see where the increasing importance of the field sales person in that truck is actually the face of the company, and probably quite often the only person that the customer actually engages. So that's a really different type of service level requirement not to meet an SLA delivery because of a contractual obligation, but in terms of actually being the face in the engagement, in the touch point of your company, with an actual customer. It's probably happening more and more in the field as a percentage of the total than it ever has. >> That's a great observation, you know we actually, many of our customers today regard that field engineer as the trusted advisor of the customer, he's the trusted advisor, you know. They don't see him as a salesperson, they see him as their, you know confidant, their trusted advisor. He's not only going to be the hero to fix their problem, he's actually going to tell them how they can prevent such a problem in the future, and he's also going to try and offer them, you know, I can fix this problem today, but actually if you bought into a wider service contract, you wouldn't need to care about how many visits, and how many parts you consume, it will actually cover you completely with a gold contract. He upsells and cross-sells at the same time as becoming the hero, so, perfect observation. >> So are they actively, you know, kind of retraining those folks to really start to think of themselves more as a kind of a customer engagement representative versus just a field service person? You see that in the real world? >> Most definitely, most definitely. Because more and more, we've got an educated buyer, the buyer's savvy, you know, he's done a lot of his own work before actually committing to a purchase these days, much the same in this space, so, you know, rather than be sold to, they want to be advised. And it's a different experience again, to use that phrase, from a technology perspective, you know, that mobile application that used to be about, here's your jobs, go visit these locations, it actually now is when you're there, maybe you should talk about this particular offering, we have a promotion on that particular unit, these customer uses X, Y, and Z features, talk to them about another incremental gain from that, so the intelligence has moved from just fix the problem, to actually become their trusted advisor, like I said. >> And I would add to that, it's not just about training, it's about hiring, so it's the profile of who you bring in as a field service technician, you're not only bringing someone who's technically savvy, or mechanically savvy, or digitally savvy, you're bringing someone who can communicate with customers, someone who can work as a team, so internally, your internal customers and then your external customers who can communicate, who can provide solutions, who can provide guidance. We've done some studies of field service engineers, and they say, you know, our work is 10% fixing things, and 90% of solving customer problems. So it's having that empathy, having that knowledge of what the customer's going through, and potentially what the customer might go through in the future and being able to preempt some of that with advice, potentially selling, potentially guiding your customers with information, and so it is a much more wholistic experience because they are the face of the organization. >> And as a consequence, expectations have raised in the customer base. They want more than just a fix. >> It's so wild right, this whole, this conversation about machines taking our jobs, and yet everyone that we talk to, there's not enough people to do the jobs, and so it just: A reinforces that we need the help, but B, more importantly, that it's the combination of a machine helping do the scheduling, helping decide where to go, helping to know what the opportunity is for that particular engagement with the person who's empathetic, has history with the company, history with the problems. That actually is a much better solution than either one, or the other. >> Yeah, to talk about artificial intelligence again, you know, we see, there's a couple of things: one it allows scaling of the human, if you will, not replacement of the human, we won't have sufficient, no skilled employees, secondly I think it all bends the human experience, because, I'll give you an example. We got to customer that's in, like the, breakdown recovery service for vehicles, in the US in fact, today a call center agent in their contact center they'll take a call, but the virtual assistant is actually listening to the conversation, kind of like a Siri in the background, and they pick up on phrases like tow-truck, automatically pops up on the agents screen, the nearest tow truck is 10 miles away, it's Steve, you know, in this location to the customer. They'll pick up on emergency, we can get there with a closer engineer, we can pull him off another job. That's actually going in the ear of the agent, and it's going on the screen of the agent, they are providin' a level of service, you know, that the customer is pretty, you know, impressed by, as a consequence. That's a great example of: it's not just the human experience, or the AI experience, it's a combined experience. >> It's the human empathy along with the automated knowledge that you're combining there, that's great. Well Mark and, thank you so much for joining us Mark and Sumair, I really appreciate it, it's been a great conversation. >> Sumair: Well thank you very much. >> Mark: Thank you, thanks. >> Jeff: Thanks! >> I'm Rebecca Knight for Jeff Frick, we'll have more from IFS World Conference just after this. (gentle dance beat)

Jeff Rick here with the q''-word in Milpitas California and Western Digital offices for the auto tech council autonomous vehicle meetup about 300 people we're looking at all these cool applications and a lot of cutting-edge technologies at the end of the day it's it's data dependent betas got to sit somewhere but really what's interesting here is that the data and more more the data is moving out to the edge and edge computing and nowhere is that more apparent than in autonomous vehicles Preet SIA [Music] [Applause] the technologies that Silicon Valley is famous for inventing cloud-based technology network technology artificial intelligence machine learning historically those may not have been important to a car maker in Detroit so well that's great we had to worry on our transmission and make these ratios better and that era is still with us but they've layered on this extremely important software based in technology based innovation that now is extremely important really autonomous vehicle to be made possible by just the immense amount of sensors that are being put in through the car not much different than as our smartphones or our phones evolved sensing your face gyroscopes GPS all the time things so there's the raw data itself that's coming off the sensors but the metadata is a whole nother level in a big level and even more important ladies the context my sensors are seeing something and then of course you used multiple sensors that's the sensor fusion between them of hey that's a person that's a deer oh don't worry that's a car moving alongside of us and he's staying in his Lane those are the types of decisions were making with this data masta context last was just about like mapping for autonomous videos which is amazing little subset there's been a tremendous amount of change in one year you know one thing I can say we're at the top it's critically important is we've had fatalities and that really shifts a conversation and and refocuses everybody on the issue is safety we're dealing with human life I mean so obviously it needs to be right 99.999 you know Plus pers read it's all about intelligent decisions and being to do that robustly across all type of operating conditions is paramount that's mission-critical slow motion high precision one to two centimeter accuracies to to be able to maneuver in parking lots be able to back up and driveways those are very very complex situations essentially these learning moments have to happen without the human fatality human cost they have to happen in software in simulations in a variety of the ways that don't put people in the public at risk people outside the vehicle haven't even chosen to adopt those risks and part of the things of getting safety is being much more efficient on the vehicle because you have to do a lot more software in order to be safe across multiple different kinds of examples of streets and locations because of this case notion these new kinds of cars new range of suppliers are coming into play we don't want piston rods anymore you want electric motors we need rare earth magnets to put in our electric motors and that's a whole new range for suppliers even before autonomous there are so many new systems in the car now that generated our consume data if you think about a full autonomous vehicle out there driving not two hours a day like we are driving today like 20 hours a day suddenly the storage requirements are very very different you see statistics aren't out there one gigabit per second two gigabits per second everyone's so scared of getting rid of any data right yet there's just tremendous data growth if we don't design the future storage solutions today what's gonna end up is that people are gonna pay much more for storage just to make it basically skates work the reality is that are we taking care of the grid locks that are affecting our city are we moving around enough people are we solving the problems of congestion I'll say no we took a bus and we divided the bus in section so you have a longer vehicle the peak time when it is high demand and shorter vehicle when there is very low demand when you're just a few passengers and the magic is that when those parts are connected one to another they shared internal space by the way all of that can be done autonomously right and we can suffer tomorrow because we can have a driver when we begin using the system and when the technology allow it has to be autonomous we're gonna run the utmost operating system that and the cost is even lower than a box in the roush human world were used to when somebody crashes the car they learn a valuable lesson and maybe the people around them learn to value lesson I'm gonna be more careful I'm not gonna have that drink when Adam Thomas car gets involved in any kind of an accident tremendous number of cars learned the lesson so as a fleet learning and that les is not just shared among one car it might be all Tesla's or all who burst that's a super good point the AV revolution will also require a revolution in the maintenance and sustenance of our road network not just the United States but everywhere in the world the quality of the roads made all the difference in the world for these vehicles to move around there are so many difficult problems to solve along this path that no company can really do it themselves right and of course you're seeking big companies investing billions of dollars but it's great because everybody's saying let's find people that specialize whether it's for sensors or computer or all the rest of those things get them in partner with them have everybody solved the right problem of their specialized and focused on the technology is coming along so fast it's just it's mind-boggling how quickly we are starting to attack these more difficult challenges and we'll get there but it's gonna take time like like anything right we're kind of hoping nobody goes out there and trips up to mess it up for the whole industry because we believe as a whole this will actually bring safety to the market right but a few missteps can create a backlash as Elon Musk puts it success is one of the possible outcomes right but not necessarily abilities but we're doing that right startups and large companies trying to solve not that thousands of problems but the millions and billions of problems that are gonna have to be solved to really get autonomous vehicles to their ultimate destination which is what we're all hoping for it's gonna save a lot of lives we're at the Auto Tech Council autonomous vehicle event in Milpitas California thanks for watching specialist [Music]

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's The Cube. Covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody, Jeff Frick here with the Cube. We're at Western Digital in Milpitas, California at the Auto Tech Council, Autonomous vehicle meetup, get-together, I'm exactly sure. There's 300 people, they get together every year around a lot of topics. Today is all about autonomous vehicles, and really, this whole ecosystem of startups and large companies trying to solve, as I was just corrected, not the thousands of problems but the millions and billions of problems that are going to have to be solved to really get autonomous vehicles to their ultimate destination, which is, what we're all hoping for, is just going to save a lot of lives, and that's really serious business. We're excited to have the guy that's kind of running the whole thing, Derek Curtain. He's the chairman of the Auto Tech Council. Derek, saw you last year, great to be back, thanks for having us. >> Well, thanks for having me back here to chat. >> So, what's really changed in the last year, kind of contextually, since we were here before? I think last year it was just about, like, mapping for autonomous vehicles. >> Yes. >> Which is an amazing little subset. >> There's been a tremendous amount of change in one year. One thing I can say right off the top that's critically important is, we've had fatalities. And that really shifts the conversation and refocuses everybody on the issue of safety. So, there's real vehicles out there driving real miles and we've had some problems crop up that the industry now has to re-double down in their efforts and really focus on stopping those, and reducing those. What's been really amazing about those fatalities is, everybody in the industry anticipated, 'oh' when somebody dies from these cars, there's going to be the governments, the people, there's going to be a backlash with pitchforks, and they'll throw the breaks on the whole effort. And so we're kind of hoping nobody goes out there and trips up to mess it up for the whole industry because we believe, as a whole, this'll actually bring safety to the market. But a few missteps can create a backlash. What's surprising is, we've had those fatalities, there's absolutely some issues revealed there that are critically important to address. But the backlash hasn't happened, so that's been a very interesting social aspect for the industry to try and digest and say, 'wow, we're pretty lucky.' and 'Why did that happen?' and 'Great!' to a certain extent. >> And, obviously, horrible for the poor people that passed away, but a little bit of a silver lining is that these are giant data collection machines. And so the ability to go back after the fact, to do a postmortem, you know, we've all seen the video of the poor gal going across the street in the dark and they got the data off the one, 101 87. So luckily, you know, we can learn from it, we can see what happened and try to move forward. >> Yeah, it is, obviously, a learning moment, which is absolutely not worth the price we pay. So, essentially, these learning moments have to happen without the human fatalities and the human cost. They have to happen in software and simulations in a variety of ways that don't put people in the public at risk. People outside the vehicle, who haven't even chosen to adopt those risks. So it's a terrible cost and one too high to pay. And that's the sad reality of the whole situation. On the other hand, if you want to say silver lining, well, there is no fatalities in a silver lining but the upside about a fatality in the self-driving world is that in the human world we're used to, when somebody crashes a car they learn a valuable lesson, and maybe the people around them learned a valuable lesson. 'I'm going to be more careful, I'm not going to have that drink.' When an autonomous car gets involved in any kind of an accident, a tremendous number of cars learn the lesson. So it's a fleet learning and that lesson is not just shared among one car, it might be all Teslas or all Ubers. But something this serious and this magnitude, those lessons are shared throughout the industry. And so this extremely terrible event is something that actually will drive an improvement in performance throughout the industry. >> That's a really good, that's a super good point. Because it is not a good thing. But again, it's nice that we can at least see the video, we could call kind of make our judgment, we could see what the real conditions were, and it was a tough situation. What's striking to me, and it came up in one of the other keynotes is, on one hand is this whole trust issue of autonomous vehicles and Uber's a great example. Would you trust an autonomous vehicle? Or will you trust some guy you don't know to drive your daughter to the prom? I mean, it's a really interesting question. But now we're seeing, at least in the Tesla cases that have been highlighted, people are all in. They got a 100% trust. >> A little too much trust. >> They think level five, we're not even close to level five and they're reading or, you know, doing all sorts of interesting things in the car rather than using it as a driver assist technology. >> What you see there is that there's a wide range of customers, a wide range users and some of them are cautious, some of them will avoid the technology completely and some of them will abuse it and be over confident in the technology. In the case of Tesla, they've been able to point out in almost every one of their accidents where their autopilot is involved, they've been able to go through the logs and they've been able to exonerate themselves and say, 'listen, this was customer misbehavior. Not our problem. This was customer misbehavior.' And I'm a big fan, so I go, 'great!' They're right. But the problem is after a certain point, it doesn't matter who's fault it is if your tool can be used in a bad way that causes fatalities to the person in the car and, once again, to people outside the car who are innocent bystanders in this, if your car is a tool in that, you have reconsider the design of that tool and you have to reconsider how you can make this idiot proof or fail safe. And whether you can exonerate yourself by saying, 'the driver was doing something bad, the pedestrian was doing something bad,' is largely irrelevant. People should be able to make mistakes and the systems need to correct those mistakes. >> But, not to make excuses, but it's just ridiculous that people think they're driving a level five car. It's like, oh my goodness! Really. >> Yeah when growing up there was that story or the joke of somebody that had cruise control in the R.V. so they went in the back to fry up some bacon. And it was a running joke when I was a kid but you see now that people with level two autonomous cars are kind of taking that joke a little too far and making it real and we're not ready for that. >> They're not ready. One thing that did strike that is here today that Patty talked about, Patty Rob from Intel, is just with the lane detection and the forward-looking, what's the technical term? >> There's forward-looking radar for braking. >> For braking, the forward-looking radar. And the crazy high positive impact on fatalities just those two technologies are having today. >> Yeah and you see the Insurance Institute for Highway Safety and the entire insurance industry, is willing to lower your rates if you have some of these technologies built into your car because these forward-looking radars and lidars that are able to apply brakes in emergency situations, not only can they completely avoid an accident and save the insurer a lot of money and the driver's life and limb, but even if they don't prevent the accident, if they apply a brake where a human driver might not have or they put the break on one second before you, it could have a tremendous affect on the velocity of the impact and since the energy that's imparted in a collision is a function of the square of the velocity, if you have a small reduction of velocity, you could have a measurable impact on the energy that's delivered in that collision. And so just making it a little slower can really deliver a lot of safety improvements. >> Right, so want to give you a chance to give a little plug in terms of, kind of, what the Auto Tech Council does. 'Cause I think what's great with the automotive industry right, is clearly, you know, is born in the U.S. and in Detroit and obviously Japan and Europe those are big automotive presences. But there's so much innovation here and we're seeing them all set up these kind of innovation centers here in the Bay area, where there's Volkswagen or Ford and the list goes on and on. How is the, kind of, your mission of bringing those two worlds together? Working, what are some of the big hurdles you still have to go over? Any surprises, either positive or negative as this race towards autonomous vehicles seems to be just rolling down the track? >> Yeah, I think, you know, Silicone Valley historically a source of great innovation for technologies. And what's happened is that the technologies that Silicone Valley is famous for inventing, cloud-based technology and network technology, processing, artificial intelligence, which is machine learning, this all Silicone Valley stuff. Not to say that it isn't done anywhere else in the world, but we're really strong in it. And, historically, those may not have been important to a car maker in Detroit. And say, 'well that's great, but we had to worry about our transmission, and make these ratios better. And it's a softer transmission shift is what we're working on right now.' Well that era is still with us but they've layered on this extremely important software-based and technology-based innovation that now is extremely important. The car makers are looking at self-driving technologies, you know, the evolution of aid as technologies as extremely disruptive to their world. They're going to need to adopt like other competitors will. It'll shift the way people buy cars, the number of cars they buy and the way those cars are used. So they don't want to be laggards. No car maker in the world wants to come late to that party. So they want to either be extremely fast followers or be the leaders in this space. So to that they feel like well, 'we need to get a shoulder to shoulder with a lot of these innovation companies. Some of them are pre-existing, so you mentioned Patti Smith from Intel. Okay we want to get side by side with Intel who's based here in Silicone Valley. The ones that are just startups, you know? Outside I see a car right now from a company called Iris, they make driver monitoring software that monitors the state of the driver. This stuff's pretty important if your car is trading off control between the automated system and the driver, you need to know what the driver's state is. So that's startup is here in Silicone Valley, they want to be side by side and interacting with startups like that all the time. So as a result, the car companies, as you said, set up here in Silicone Valley. And we've basically formed a club around them and said, 'listen, that's great! We're going to be a club where the innovators can come and show their stuff and the car makers can come and kind of shop those wares. >> It's such crazy times because the innovation is on so many axis for this thing. Somebody used in the keynote care, or Case. So they're connected, they're autonomous, so the operation of them is changing, the ownership now, they're all shared, that's all changing. And then the propulsion in the motors are all going to electric and hybrid, that's all changing. So all of those factors are kind of flipping at the same time. >> Yeah, we just had a panel today and the subject was the changes in supply chain that Case is essentially going to bring. We said autonomy but electrification is a big part of that as well. And we have these historic supply chains that have been very, you know, everyone's going as far GM now, so GM will have these premier suppliers that give them their parts. Brake stores, motors that drive up and down the windows and stuff, and engine parts and such. And they stick year after year with the same suppliers 'cause they have good relationships and reliability and they meet their standards, their factories are co-located in the right places. But because of this Case notion and these new kinds of cars, new range of suppliers are coming into play. So that's great, we have suppliers for our piston rods, for example. Hey, they built a factory outside Detroit and in Lancing real near where we are. But we don't want piston rods anymore we want electric motors. We need rare earth magnets to put in our electric motors and that's a whole new range of suppliers. That supply either motors or the rare earth magnets or different kind of, you know, a switch that can transmit right amperage from your battery to your motor. So new suppliers but one of the things that panel turned up that was really interesting is, specifically, was, it's not just suppliers in these kind of brick and mortar, or mechanical spaces that car makers usually had. It's increasing the partners and suppliers in the technology space. So cloud, we need a cloud vendor or we got to build the cloud data center ourselves. We need a processing partner to sell us powerful processors. We can't use these small dedicated chips anymore, we need to have a central computer. So you see companies like Invidia and Intel going, 'oh, that's an opportunity for us we're keen to provide.' >> Right, exciting times. It looks like you're in the right place at the right time. >> It is exciting. >> Alright Derek, we got to leave it there. Congratulations, again, on another event and inserting yourself in a very disruptive and opportunistic filled industry. >> Yup, thanks a lot. >> He's Derek, I'm Jeff, you're watching The Cube from Western Digital Auto Tech Council event in Milpitas, California. Thanks for watching and see you next time. (electronic music)

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles, brought to you by Western Digital. >> Hey, welcome back here everybody. Jeff Frick here with theCUBE. We're in Milpitas, California at the Western Digital event. It's the Autotech Council Autonomous Vehicle event. About 300 people talking about all these really complicated issues around autonomous vehicles, a wide variety of start ups and enterprises and it's a really interesting space 'cause there's, as somebody said in the keynote there's literally thousands of problems to solve. But one of the angles is really on the public transportation side. Really excited to have a really innovative start up and welcome Emmanuele Spera. He is the CEO of NEXT Future Transportation. Emmanuele, welcome. >> Hi Jeff, thanks for inviting me out. >> Absolutely, so for the folks that aren't familiar, you can go to the website, there's a great demo video that you guys have this session. What are you guys building? >> We're building something very particular, because, so far, you see all those company presenting what is, well known as an autonomous car. So, let's build something that can let us read our newspaper while we are commuting, and very nice, lot of money that's been invested in that. But the reality is that how we... Are we taking care of the gridlocks that are affecting our city? Are we moving around enough people? Are we solving the problem of congestion? I'll say, no. Because it doesn't matter if we have an EV, an autonomous driving vehicle, or an SUV or a car, you still have congestion, you still need to have large number of car to move around people. >> Jeff: Right, right. So the only viable solution is to use buses. Buses has been there in the last hundred years and they are very expensive, actually the most expensive asset that cities and municipalities are using. So they using taxpayer money to pay those asset, and they are underutilized. Because you have a high demand in peak time, so people use buses, but on the rest of the day, when there are no peak time, there is very low usage rate. I'll say around 20, 25%. So take a look at those buses, they are empty all the time. So our solution is about modularizing this kind of transportation. So, literally, we took a bus and we divided the bus in section, so you have six module that are coupled together, are the same length and capacity of a standard city bus. But we do modularization. We can create a system which literally breathe, because we have longer vehicle in peak time when there is high demand, and shorter vehicle when there is very low demand when you have just a few passenger. The magic is that when those pods are connected, one to another, they share the internal space. By the way, all of that can be done autonomously. >> Jeff: Right. The coupling is already done autonomously, and we can start from tomorrow, because we can't have a driver when we begin using the system. When the technology allows us to be autonomous, we're going to run the autonomous operating system on that. >> Jeff: Right. This can be done autonomously now, in close environment when you don't have a mix traffic environment. But we demonstrated that this could be done. >> That's funny that you came at the problem from a bus, and breaking the bus into modular pieces. When I was prepping for our interview, and doing some research, I looked at it more as kind of a combination of a bunch of individual passenger vehicles that then create almost more like a train. But it's the same concept and it made me think of really kind of IP networks where, when you can bring them all together into an autonomous unit and they operate as one. Much more efficient. >> Emmanuele: Exactly. >> They don't need space in between. And then, really an interesting concept where that packet can kind of jump onto another network if it needs to go down another route. So the fact that these things can couple and uncouple, the fact the people can change units within the structure, you're really adding kind of a smart transportation that then can come together and really act like a city bus. Really fascinating way to look at that problem. >> Absolutely, it's simple, so. The technology to create that, if you look at those parts, seem like very far away, but we were able to create this now using off the shelf components. >> Jeff: Right. Literally, when you give people, passenger, an option, this kind of option, they're going to love it. >> Jeff: Right. Think about now when you need to go from point A to point B, you need to take a taxi, ride a bicycle, take new burr, to change and have an intermodal transportation to reach your destination and it's going to take a while to reach your destination. With this system, you just jump on another pod and you change your destination within the same system. >> Jeff: Right. It can all be controlled by an app that you carry or by screen that are on the pods, that tell you you need to go northbound to go on pod number one, you need to go eastbound go on pod number two. >> Jeff: Right. So the system is able to reorganize itself based on the user's needs, literally. >> So, we're here, we're sponsored by Western Digital, this is part of their whole Data Makes Possible program. From a data perspective and a AI perspective, how did you have to approach that problem a little bit differently and what were some of the challenges that enabled you to overcome, to create this unique solution? >> So, before, you were saying that we are all here at this conference and we'll need to solve, like, thousands of problem. We actually have to solve, like, millions of problem, billions of problem, I mean we are... And AI is the only way we can overcome such problem in some area. Obviously we need to take control of the basics, of the beginning of this journey. Clearly the AI will be amazing when the system is fully working and you can predict information, you can connect with the passenger, with the user of the system directly, and predict behavior, predict needs on the passenger side. And then also, you're going to use the AI to predict how the system is flowing, meaning how the vehicle are using the lane, if there are gridlock somewhere, so how you can, on the fly, reorganize the way those vehicles, those pods, are going to move around the city, to go over obstacle and reach a destination faster and ultimately, in our case, where is the best place to couple with another vehicle based on passenger destination and lengths of the journey. >> Right. So, Emmanuele, this isn't just a concept. You guys actually have working prototypes out in the field, so where, how many do you have deployed? What's your road map and hope for kind of a roll out or do you have, is it a partner strategy? What's your plan to scale? >> We had this concept, the company was made in 2015, we were showing this concept in 2016, the beginning of 2017. In one year, we were able to deliver to our first customer, which is the Dubai government. Last February, during the World Government Summit in Dubai, we showcase two full spec vehicle that were able to couple and uncouple autonomously and move around the venue. We had been testing them since January in Dubai, in a closed area or in particular events where we could showcase and have passengers on both and drive them for a small route. Clearly, our solution is not for OEM car maker. It's for municipalities that really need to solve a problem and have been stuck, literally, with the bus in the last hundred year. There have been no major innovation in bus industry. The only innovation I see now, there are electrifying buses, so now you have way more expensive assets which is still underutilized. >> Right. >> So I spend more and it's still no one use it. So what you are doing, we are going to provide fleets to municipalities and Dubai will be the first, especially since they're having their Dubai 2020 Exhibition. We can provide them with a fleet by that time. Think about that, 120 pods are the same as 20 buses. >> Right, right. That's your targeted first deploy, something like that? >> Yeah, exactly, and the cost is even lower than a bus. >> Alright. Well, Emmanuele, it's really cool technology. >> Emmanuele: Thank you, Jeff. >> I just love the innovation in terms of kind of slicing the problem in a slightly different way, being really innovative and partnering. As you said, you have not raised $100 million in all this craziness, and actually deploying, so. Really exciting story, thanks for sharing with it and we're excited to watch it unfold over the next couple of years. >> Absolutely, thank you, Jeff. >> Alright, he's Emmanuele, I'm Jeff. We're at the Autotech Council, part of Western Digital's Data Makes Possible. Thanks for watching, catch you next time. (techno music)

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's the Cube, covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back here ready, Jeff Frick here with the Cube. We're at Western Digital's office in Milpitas, California at the Autotech Council Autonomous Vehicle event. About 300 people talking about all the various problems that have to be overcome to make this thing kind of reach the vision that we all have in mind and get beyond the cute. Way more cars driving around and actually get to production fleet, so a lot of problems, a lot of opportunity, a lot of startups, and we're excited to have our next guest. He's Dave Tokic, the VP of Marketing and Strategic Partnerships from Algolux. Dave, great to see you. >> Great, thank you very much, glad to be here. >> Absolutely, so you guys are really focused on a very specific area, and that's about imaging and all the processing of imaging and the intelligence out of imaging and getting so much more out of those cameras that we see around all these autonomous vehicles. So, give us a little bit of the background. >> Absolutely, so, Algolux, we're totally focused on driving safety and autonomous vision. It's really about addressing the limitations today in imaging and computer vision systems for perceiving much more effectively and robustly the surrounding environment and the objects as well as enabling cameras to see more clearly. >> Right, and we've all seen the demo in our twitter feeds of the chihuahua and the blueberry muffin, right? This is not a simple equation, and somebody like Google and those types of companies have the benefit of everybody uploading their images, and they can run massive amounts of modeling around that. How do you guys do it in an autonomous vehicle, it's a dynamic situation, it's changing all the time, there's lots of different streets, different situations. So, what are some of the unique challenges, and how are you guys addressing those? >> Great, so, today, for both 8S systems and autonomous driving, the companies out there are focusing on really the simpler problems of being able to properly recognize an object or an obstacle in good conditions, fair weather in Arizona, or Mountain View or Tel Aviv, et cetera. But really the, we would live in the real world. There's bad weather, there is low light, there's lens issues, lens dirty, and so on. Being able to address those difficult issues is not really being done well today. There's difficulties in today's system architectures to be able to do that. We take a very different, novel approach to how we process and learn through deep learning the ability to do that much more robustly and much more accurately than today's systems. >> How much of that's done kind of in the car, how much of it's done where you're building your algorithms offline and then feeding them back into the car, how does that loop kind of work? >> Great question, so the objective for this, we're deploying on, is the intent to deploy on systems that are in the car, embedded, right? We're not looking to the cloud-based system where it's going to be processed in the cloud and the latency issues and so on that are a problem. Right now, it's focused on the embedded platform in the car, and we do training of the datasets, but we take a novel approach with training as well. We don't need as much training data because we augmented it with very specific synthetic data that understands the camera itself as well as taking in the difficult critical cases like low light and so on. >> Do you have your own dedicated camera or is it more of a software solution that you can use for lots of different types of inbound sensors? >> Yeah, what we have today is, we call it, CANA. It is a full end-to-end stack that starts from the sensor output, so say, an imaging sensor or a path to fusion like LIDAR, radar, et cetera, all the way up to the perception output that would then be used by the car to make a decision like emergency braking or turning or so on. So, we provided that full stack. >> So perception is a really interesting word to use in the context of a car, car visioning and computer vision cause it really implies a much higher level of understanding as to what's going, it really implies context, so how do you help it get beyond just identifying to starting to get perception so that you can make some decisions about actions. >> Got it, so yeah, it's all about intelligent decisions and being able to do that robustly across all types of operating conditions is paramount, it's mission critical. We've seen recent cases, Uber and Tesla and others, where they did not recognize the problem. That's where we start first with is to make sure that the information that goes up into the stack is as robust and accurate as possible and from there, it's about learning and sharing that information upstream to the control stacks of the car. >> It's weird cause we all saw the video from the Uber accident with the fatality of the gal unfortunately, and what was weird to me on that video is she came into the visible light, at least on the video we saw, very, very late. But ya got to think, right, visible light is a human eye thing, that's not a computer, that's not, ya know, there are so many other types of sensors, so when you think of vision, is it just visible light, or you guys work within that whole spectrum? >> Fantastic question, really the challenge with camera-based systems today, starting with cameras, is that the way the images are processed is meant to create a nice displayed image for you to view. There are definite limitations to that. The processing chain removes noise, removes, does deblurring, things of that nature, which removes data from that incoming image stream. We actually do perception prior to that image processing. We actually learn how to process for the particular task like seeing a pedestrian or bicyclist et cetera, and so that's from a camera perspective. It gives up quite the advantage of being able to see more that couldn't be perceived before. We're also doing the same for other sensing modalities such as LIDAR or radar and other sensing modalities. That allows us to take in different disparate sort of sensor streams and be able to learn the proper way of processing and integrating that information for higher perception accuracy using those multiple systems for sensor fusion. >> Right, I want to follow up on kind of what is sensor fusion because we hear and we see all these startups with their self-driving cars running around Menlo Park and Palo Alto all the time, and some people say we've got LIDAR, LIDAR's great, LIDAR's expensive, we're trying to do it with just cameras, cameras have limitations, but at the end of the day, then there's also all this data that comes off the cars are pretty complex data receiving vehicles as well, so in pulling it all together that must give you tremendous advantages in terms of relying on one or two or a more singular-type of input system. >> Absolutely, I think cameras will be ubiquitous, right? We know that OEMs and Tier-1s are focused heavily on camera-based systems with a tremendous amount of focus on other sensing modalities such as LIDARs as an example. Being able to kit out a car in a production fashion effectively and commercially, economically, is a challenge, but that'll, with volume, will reduce over time, but doing that integration of that today is a very manually intensive process. Each sensing mode has its own way of processing information and stitching that together, integrating, fusing that together is very difficult, so taking an approach where you learn through deep learning how to do that is a way of much more quickly getting that capability into the car and also providing higher accuracy as the merged data is combined for the particular task that you're trying to do. >> But will you system, at some point, kind of check in kind of like the Teslas, they check in at night, get the download, so that you can leverage some of the offline capabilities to do more learning, better learning, aggregate from multiple sources, those types of things? >> Right, so for us, the type of data that would be most interesting is really the escapes. The things where the car did not detect something or told the driver to pay attention or take the wheel and so on. Those are the corner cases where the system failed. Being able to accumulate those particular, I'll call it, snips of information, send that back and integrate that into the overall training process will continue to improve robustness. There's definitely a deployed model that goes out that's much more robust than what we've seen in the market today, and then there's the ongoing learning to then continue to improve the accuracy and robustness of the system. >> I think people so underestimate the amount of data that these cars are collecting in terms of just the way streets operate, the way pedestrians operate, but whether there's a incident or not, they're still gathering all that data and making judgements and identifying pedestrians, identifying bicyclists and capturing what they do, so hopefully, the predictiveness will be significantly better down the road. >> That's the expectation, but like numerous studies have said, there's a lot of data that's collected that's just sort of redundant data, so it's really about those corner cases where there was a struggle by the system to actually understand what was going on. >> So, just give us kind of where you are with Algolux, state of the company, number of people, where are ya on your lifespan? >> Algolux is the startup based in Montreal with offices in Palo Alto and Munich. We have about 26 people worldwide, most of them in Montreal, very engineering heavy these days, and we will continue to do so. We have some interesting forthcoming news that please keep an eye out for of accelerating what we're doing. I'll just hint it that way. The intent really is to expand the team to continue to productize what we've built and start to scale out, to engage more of the automotive companies we're working with. We are engaged today at the Tier-2, Tier-1, and OEM levels in automotive, and the technology is scalable across other markets as well. >> Pretty exciting, we look forward to watching, and you're giving it the challenges of real weather unlike the Mountain View guys who we don't really deal with real weather here. (laughing) >> There ya go. (laughing) Fair enough. >> All right Dave, well, thanks for taking a few minutes out of your day, and we, again, look forward to watching the story unfold. >> Excellent, thank you, Jeff. >> All right. >> All right, appreciate it. >> He's Dave, I'm Jeff, you're watching the Cube. We're are Western Digital in Milpitas at Autotech Council Autonomous Vehicle event. Thanks for watching, we'll catch ya next time.

>> Announcer: From Milpitas, California at the edge of Silicon Valley it's theCUBE. Covering autonomous vehicles. Brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in Milpitas, California at Western Digital offices for the Autotech Council Autonomous Vehicle Meetup. About 300 people, a lot of conversations about the not thousands but millions of problems that have to be solved before we get autonomous vehicles on the road. But there's so many angles to this whole story besides just kind of what you think of as just an Uber, a self driving taxi, or even a self driving car for your personal use and it's really a cool start up here that's actually celebrating their 100th round trip transaction. We're excited to have Daniel Laury. He's a CEO and Chief Product Officer of Udelv. Great to see ya. >> Nice to meet you Jeff. >> So you just came off your keynote presentation and you were showing a great highlight movie of your product, so tell the folks what are you guys all about. >> We're the first public road enabled autonomous driving delivery company. And this is, our aim is to cut the cost of last minute deliveries in half. And to make deliveries easier, more convenient for consumers, more ubiquitous, faster, and cheaper of course. >> So it's pretty interesting. So the use case that you're doing now is you're in San Mateo and you're delivering groceries from Draeger's to the neighborhood. >> Yes, we actually now have four customers. >> Jeff: Oh, you have four, okay. >> Yes, in the matter of a month. We gained three more after Draeger's. Draeger's was our first customer. We've been working with them for the last six months to find the, you know, the best cargo space, the way to organize the compartments and everything. And it's been a fantastic partnership. And so they were our first customer and we're doing deliveries for them almost on a daily basis. And then we added three customers. As people were seeing this orange vehicle in the streets, they started calling us and they say "Hey, can I do it?" So, now we have a florist out of Burlingame and a couple of restaurants as well. >> And how many of these vehicles do you have on the road? >> So for now we have one of them. We are getting our second one next week. There is a third one that is going to be ready in about four weeks from today and then we have a production ramp up from there. >> So what are some of the unique challenges in creating this vehicle and delivering the service that people probably never thought of. >> Right, and it's, in our case, first of all we're not a science project, we're a real business case. Probably one of the first ones in the autonomous driving world. And for us to solve this business case, it's not just about autonomous driving, it's also to have a best customer experience. And so we're not just doing autonomous driving. We're doing a bunch of things. We're building a cargo space, that's mechanical engineering that is adapted which is basically a system of compartments or luggage on wheels if you want. The second thing is we are building apps on the merchant side and the customer side. Third thing is on the autonomy side of things we are doing something that very few other companies are doing which is mastering the first and last hundred feet. Slow motion, high precision. One to two centimeter accuracies. To be able to maneuver in parking lots, be able to back up in driveways and things like that nobody else is doing really that kind of thing. And the last thing is which we're doing and we're probably one of the world's most advanced companies doing this is teleoperations. We have to be able to take control of the vehicle. First of all, monitor the fleet. And second take control of the vehicle in case of a special situation. And we're doing this with an ultra-low latency less than 200 milliseconds between the image we receive from the truck and what the command we're giving back which allows us to actually drive the vehicle in the streets as if it was a video game but it's the reality. >> Right, no we did a piece with Fan Amato. I don't know if you know Fan but we were doing kind of a general purpose. A version of that same capability. It's really, really amazing. >> Frankly I think that autonomous driving is going to need that capability for at least the next decade. >> So the last hundred feet is interesting. You know, I went to a Ford Smart Cities event a little while ago and they talked about kind of curb management because when you have all these kind of fleet vehicles getting people in and out, making deliveries in and out. Kind of the curb in that interchange of the curb is really a tricky thing. It take a lot of nuance, you know. Know when to double park. Can you double park, should you double park. Can you, as you said, get into a driveway. So when you, what your ideal scenario when you do do a grocery drop off, you try to get into the driveway? Get off the double parking situation? >> Yes, absolutely. This is a critical part of what we're doing. And parking lots are actually lawless places. You see cops everywhere but you don't see them in parking lots so you have people backing up from a spot, children pushing carts, pets, you name it. So those are very, very complex situations. Mastering those situations is super important for us because of course our vehicle is going to park in those parking lots to pick up the goods and potentially to deliver. So we developed an AI stack, Artificial Intelligence Stack that starts with a scene estimator. We estimate the scene to see where, what spots are available or if it's a driveway if you have cars parked on the curb and then be able to actually maneuver in that spot. >> Right, but you're writing off a lot. So, you're doing the apps, you're doing all the infrastructure with your partners, you're doing the complexity of the vehicle. And then you've got, you've got to worry about perishable goods, you're taking milk as well as warm stuff. So a lot to chew. How big is your team? Where are you in your development as a company? >> Yes, we're about 30 people right now. And we are going to grow this team quite significantly by probably double the size of the team this year. It's a very ambitious project. It's a very ambitious company and yes, as Elon Musk puts it, success is one of the possibilities. One of the possible outcomes. But not necessarily the likeliest. but we're doing that race, we're in that race. >> So, just before we wrap I want to talk a little bit about the human factors. Cuz a lot of conversation earlier in some of the keynotes about trust and no trust. On one hand people don't trust these things. They said that, you know, they show the survey. I don't trust them. On the other hand we see people in autonomous vehicles as if they were a level five, right? They're sleeping and doing all sorts of crazy stuff. When you engage with customers what are some of their reactions on kind of the trust or not trust? How do they respond to this truck driving up and they walk out and pull their groceries out? >> That's a great question. In our case we're in a very different situation than all the ride sharing and passenger vehicles because we don't, by definition we don't carry passengers. So they only interact with the truck in the sense that they have to retrieve their goods. That's the only thing they do. And so they look at this a lot more favorably than, because it doesn't, they don't have that sense of danger from the vehicle. It's actually more like a wow, this is so interesting. And now I'm getting my deliveries. I know it's going to be exactly 16 minutes. And I get my push notification four minutes before. It gets there and then it's a simple, very, very simple way of doing things. It also will be very, very convenient for returning goods. You will be able to summon the vehicle to your doorstep. You'll put into a locker. It goes for you to UPS. It takes a minute to do it. So people love the service. Their reaction has been overwhelmingly positive. And it's a far less dangerous thing to do than having passengers. >> Right, yeah the first time I saw the video of it I thought was Amazon Lock or which is such a convenient way to interact and so importantly as we move to smart cities because what you don't want is the proverbial sticker on your door that you missed a delivery, like aw rats. So this is such an important part of the enablement of smart cities so really, really cool story. Alright Daniel. So last words, getting excited. Going to get out of individual company relationships and start to have more of a generic service that people can tap into? >> Yes, I, we're tremendously excited about the future of this company. Within two or three weeks from having launched a product on January 30th, we've had, we've received phone calls from every large retailer you name it, in the world wanting to do business with us. So it's a very, very exciting start. >> Alright Daniel, we'll keep an eye. >> Thank you so much, Jeff. >> Thanks for stopping by. Alright, he's Dan, I'm Jeff. You're watching theCUBE. We're at the Autotech Council Autonomous Vehicle Event in Milpitas, California. Thanks for watching. Catch you next time. (upbeat music)

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE covering autonomous vehicles. Brought to you by Western Digital. (upbeat electronic music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Autotech Council Autonomous Vehicles event here at Western Digital. It's part of our ongoing work that we're doing with Western Digital about #datamakespossible and all the really innovative and interesting things that are going on that at the end of the day, there's some data that's driving it all and this is a really crazy and interesting space. So we're excited for our next guest. He's Mark DeSantis. He's the CEO of RoadBotics. Mark, great to see you. >> Welcome. >> Thanks, thanks for having me, Jeff. >> So just to give the quick overview of what is RoadBotics all about? >> Sure, we use a simple cellphone as a data collection device. You put that in the windshield, you drive, it records all the video and all that video gets uploaded to the Cloud and we assess the road's surface meter by meter. Our customers would be Public Works departments at the little town to a big city or even a state, and we apply the same principles that a pavement engineer would apply when they look at a piece of pavement. Looking for all the different subtle little features so that they can get, first of all, get an assessment of the road and then they can do capital planning and fix those roads and do a lot of things that they can't do right now. >> So I think the economics of roads and condition of roads, roads in general, right? We don't think about them much until they're closed, they're being fixed, they're broken up, there's a pothole. >> Mark: Yeah. >> But it's really a complex system and a really high value system that needs ongoing maintenance. >> That's right. I always use the example of the Romans who built a 50,000 mile road network across Europe, the Middle East, and Africa. Some of those roads, like the Appian Way, are still used today. They were very good road builders and they understand the importance of roads. Regrettably, we take our roads for granted. The American Society for Civil Engineers annually rates infrastructure and we're rated about 28% of our nation's 11 million lane miles as poor. Unfortunately, that's- >> Jeff: 28%? >> 28%. And that really means that you need to invest, we'll need to invest at least a million to two million bucks a mile to get those roads back into shape. So we take our roads for granted. I'm enjoying this conference and there's one point that I want to make that I think is very poignant, is the AV revolution will also require a revolution in the maintenance and sustenance of our road network, not just the United States but everywhere in the world. >> So it's interesting, and doing some research before we got together in terms of the active maintenance that's not only required to keep a road in good shape but if you keep the active maintenance in position, those roads will last a very long time. And you made an interesting comment that now the autonomous vehicles, it's actually more important for those vehicles, not only for jolting the electronics around that they're carrying, but also for everything to work the way it's supposed to work according to the algorithms. >> Andrew Ang, who's an eminent computer scientist, machine learning, we were spun out of Carnegie Mellon and he was a graduate of that program, recognized early on that the quality of the roads made all the difference in the world for these vehicles to move around. We, in turn, were spun out of Carnegie Mellon, out of that same group of AV researchers, and in fact, the impetus for the technology was to be able to use the sensing technology that allows a vehicle to move around to assess the quality of roads. And it's road inspection, really, is an important part of road maintenance. The ability to go look at an asset. Interestingly, it's an asset whose challenge is not the fact that it can't be inspected, it's the sheer size of the asset. When you're talking about a small town that might have a 60-mile road network, most and the vast majority of inspection is visual inspection. That means somebody in a car riding very slowly looking down and they'll do that for tens, thousands, hundreds of thousands of miles, very hard to do. Our system makes all that very, much more efficient. The interesting thing about autonomous vehicles is they'll have the capacity to use that data to do that very assessment. So for our company, we ultimately see us embedded in the vehicle itself, but for the time being, cellphones work fine. >> Right. So I'm just curious, what are some of those leading indicator data points? Because obviously we know the pothole. >> Mark: Yeah. >> By then things have gone too far but what are some of the subtle things that maybe I might see but I'm not really looking at? (laughs) >> Well, I think I've changed you right now and you don't know it. You're never going to look at a road the same- >> Oh, I told you, I told you. (laughs) >> After you hear me talk for the next three minutes. I don't look at roads the same and I'm not a civil engineer nor am I a pavement engineer, but as the CEO of this company I had to learn a lot about those two disciplines. And in fact, when you look at a piece of asphalt, you're actually looking for things like alligator cracks, which sort of looks like the back of an alligator's skin. Block cracks, edge cracks, rutting, a whole bunch of things that pavement engineers, frankly, and there is a discipline called pavement engineering, where they look for. And those features determine the state of that road and also dictate what repairs will be done. Concrete pavement has a similar set of characteristics. So what we're looking for when we look at a road is, I always say that, people say, "Well, you're the pothole company." If all you see are potholes, you don't have a business. And the reason is, potholes are at the end of a long process of degradation. So when you see a pothole, there are two problems. One is, you can certain blow out a tire or break an axle on that pothole but also it's indicative of a deeper problem which means the surface of the road has been penetrated which means you to dig up that road and replace it. So if you can see features that are predictive of a road that's just about to go bad, make small fixes, you can extend the useful life of that asset indefinitely. >> Right. So before I let you go, unfortunately, we're just short on time. >> Mark: Yeah. >> I would love to learn about roads. I told you, I skateboard so I pay a lot of attention to smooth roads. >> Mark: (laughs) And you'll pay even more now. >> Now I'll pay even more and call the city. (chuckles) But I want to pivot off what happened at Carnegie Mellon and obviously academic institutions are a huge part of this revolution. >> Yeah, yeah. >> There's a lot of work going on. We're close to Stanford and Berkeley here. Talk a little bit about what happens... It's happening at Carnegie Mellon and I think specifically you came out of the Robotics Institute in something called the Traffic21 project. >> Yeah, Traffic21 is funded by some local private interests who believed that the various technologies that are, really, CMU is known for around computer science, robots, engineering, could be instrumental in bringing about this AV revolution. And as a consequence of that, they developed a program early on to try to bring these technologies together. Uber came along and literally hired 27 of those researchers. Argo, now... Argo, Ford's autonomous vehicle now, is big in Pittsburgh as well. On any given day, by my estimate, it's not an official estimate here, there are about 400 autonomous vehicles, Ford and Uber vehicles, on Pittsburgh's streets every single day. It's an eerie experience being driven around by a completely autonomous Uber vehicle, believe me. >> I've been in a couple. It's interesting and we did a thing with a company called Phantom. They're the ones that step if your Uber gets stuck. >> Oh, yeah. >> Which is interesting. (laughs) So really interesting times and exciting and I will go and pay closer attention for the alligator patterns (laughs) on my route home tonight. (laughs) All right, Mark, thanks for stopping by and sharing the insight. >> Thanks again, Jeff. Appreciate you having me. >> All right, he's Mark, I'm Jeff. You're watching theCUBE from the Autotech Council Autonomous Vehicles event in Milpitas, California. Thanks for watching. (upbeat electronic music)

>> Narrator: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles. Brought to you by Western Digital. >> Hey. Welcome back, everybody. Jeff Frick here with theCUBE. We're in Milpitas, California at the Autotech Council Autonomous Vehicle Event. Autotech Council is an interesting organization really trying to bring a lot of new Silicon Valley technology companies, and get them involved with what's going on in industries. They've done a Teleco Council. This is the auto one. We were here last year. It was all about mapping. This is really kind of looking at the state of autonomous vehicles. We're excited to be here. It's a small intimate event, about 300 people. A couple of cool, dem hook cars out side. And our first guest is here. He's Suneil Mishra. He is the strategic marketing for Tensyr. Nice to be here. >> Thanks, Jeff. Appreciate you having us. >> Yeah. So, give us the overview on Tensyr. >> Sure. So we're a Silicon Valley startup, venture-backed. We're actually just coming out of stealth. So you're one of the first folks to hear about-- >> Jeff: Congratulations. >> what we're up to. And we're basically doing software platforms to actually accelerate autonomous vehicles into production, doing all the things around safety and efficiency, and ROI that will be important when we actually want to make money on all of this stuff. >> Right. So what does that mean because obviously, you're in Palo Alto. I'm in Palo Alto. We see the Waymo cars driving around all the time. And it seems like every day I see a few more cars running around with LIDAR stacks on top. You know, those are all kind of R and D login miles, doing a lot of tests. What are some of the real challenges to get it from where it is today to actual production? And how are you guys helping that process? >> Sure. So yeah, I mean a lot of what people don't think about is these R and D kind of pilot cars. They actually are doing R and D. It's trial and error. That's the whole point of R and D. When you get to production, you can't have that error part anymore. And so safety suddenly becomes a critical element. And part of the things of getting safety is being much more efficient on the vehicle because you have to do a lot more software in order to be safe across multiple different kinds of examples of streets, and locations, of weather conditions, and so on. So, we basically provide essentially all of the glue, all of the grunt work, at the lower levels, to make things as efficient as possible, as safe as possible, as secure as possible. And also making things adaptable and flexible. There's lots of different hardware coming down the pipeline from all different vendors. And if you're a production vehicle, it's which ones you choose. There may be different configurations for different cost points of vehicles. And then of course when you're looking to the future as a production vehicle manufacturer, how do you know which pieces of hardware to use and whether your software will work or not? We kind of give you a lot of insight into all of those things that allow you to certify that your products are safe. And so we don't build the stacks themselves, but we actually take people self-driving models, and we accelerate them onto the vehicles. >> Jeff: With your software in the ecosystem of the self-driving car hardware. >> Exactly. So we have an actual runtime engine that will set on the end device, in this case a vehicle. And it will actually optimize the scheduling, the orchestration of all of your code. That makes it much more efficient. And we can monitor that so you can mitigate for safety. And if something does go wrong, we're essentially like a black box where you can actually see what actually happened to your software. >> So it's interesting. We talked a little bit before we turned the cameras on that a lot of the self-driving vehicles are Fords. We talked to the guys at Phantom and apparently, it's a really nice system to be able to get computer control into the control mechanisms of the car. But you said there's a whole layer of how do you define being able to interact with the control systems of the car, versus is it safe, is it ready for production, and kind of taking it beyond that R and D level. So what are some of the real challenges that people need to be aware of when we're going to make that big leap. >> Yeah, so I mean, a couple of the big things that happen is when you're seeing these pilot vehicles driving around, the amount of software that they actually have on there to control the vehicles is very tuned for the particular cases. That's why you see a lot of these vehicles out in places like Arizona where it's sunny weather. You're not having to deal with snow and all the rest of that stuff. >> Jeff: Right. >> If they actually take a car and move it to Michigan for the snow test, they'll actually deploy different software to do the snow case. But when you're actually in a production vehicle, and nobody can actually come back and change that software, you're going to have to load all of those types of solution, on at the same time. That requires more space, more compute power. And so for solutions like ours, we actually allow the production manufacturers to figure out what the optimal solutions are in those cases because you can't come back and change the software. You don't have an engineer that can go tweak that code. And you don't have a safety driver, of course, to go grab the wheel if something goes wrong. These things essentially have to be able to go out there in the wilderness for years and years, and actually work. So it's a whole different classification of problem that takes a lot more compute power. And people who are seeing those giant sets of sensor rigs don't probably realize there's also a giant trunk for clarisitive, where if there's compute power in the back, running 3,000 watts of power. When you actually get to deployment, you're going to have an embedded system with maybe 500 watts of power. So you have less compute power, and you're trying to do more with it. So it's quite a challenging problem, to actually jump to production. And we're kind of smoothing out a lot of those wrinkles. >> Right. So, I just want to get your kind of perspective on kind of the Apple approach, which everyone kind of sees Tesla as. Right? It's soup to nuts, it's the car's design, it's the software, versus kind of an industry approach where you have all these different players, obviously, 300 people here at this event. There's autonomous vehicle events going on all over the place where you got all these component manufacturers, and component parts, coming together to create the industry autonomous vehicles versus just the Tesla. So what's kind of the vibe in the industry? It feels like early days. Everybody's cooperating. How is this think kind of coalescing? >> Yeah. I think what we're seeing, we basically talk to people up and down the stack, because anyone who's doing this stuff is a potential customer for us, so automotive OEMs to tier one suppliers, to the AI startups are building these software stacks, they're all potential customers for us. What we're seeing from everyone is they're saying there's so many difficult problems to solve along this path that no company can really do it themselves. And of course, you're seeing big companies investing billions of dollars. But it's great because everybody's saying, let's find people that specialize, whether it's in sensors, or compute, all the rest of those things. And kind of get them, and partner with them, have everybody solve the right problem that they're specialized and focused on. And we essentially can kind of come in and we solve parts of those problems, but we're also kind of the glue that fills a lot of those things together. So we actually see ourselves as being quite advantageous in that anyone who's doing their specialized piece, contributes into the collective. And we kind of build that collective and make it easy for the actual end vendor that's trying to sell a car or run a service, to actually access all those mechanisms. >> And are kind of the old school primary manufacturers still the focal point of the coalescing around this organization or are they losing kind of that position? >> I wouldn't say their losing it. It's kind of an interesting play. So you've got a bunch of traditional automotive guys who actually don't really, not to diss them, but they don't really understand large-scale software because they haven't had that in their vehicles until now. And at the same time you've got kind of your startup mode software experts that don't really understand a lot about automotive. But eventually, it's got to go on a car. And so what we're finding is the automotive manufacturers are really saying to get to production, we need certain kinds of safety guarantees and ROI and so on. So they're really driving from that point of view. The software guys are kind of saying, well, we're just going to throw the software over to you and sort of, good luck. So, we're actually finding both sides care, but nobody's quite sure who should be taking the lead. So I think we're getting to the point where ultimately, automotive manufacturers will be the one shipping vehicles and that software's going to be on their car. So they're going to be the ones that care about it most. So we're actually seeing them being quite proactive about how do we solve these problems. How do we get from the R and D stage to the actual production stage? So that's where we're seeing a lot of the interest on our side. >> All right, Suneil. We could go on forever, but we have to leave it there. And congratulations on your launch and coming out of stealth. And we're excited to watch the story unfold. >> Great. Thanks, Jeff. I appreciate the time. >> All right. He's Suneil. I'm Jeff Frick. You're watching The Cube from the Autotech Council Autonomous Vehicle Event in Milpitas, California. Thanks for watching. (upbeat music)

>> Announcer: From Milpitas, California at the edge of Silicon Valley, it's The CUBE. Covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick here with The Cube. We are at the Autotech Council Autonomous Vehicle event at Western Digital. Part of our Data Makes Possible Program with Western Digital where we're looking at all these cool applications and a lot of cutting edge technology that at the end of the day, it's data dependent and data's got to sit somewhere. But really what's interesting here is that the data, and more and more of the data is moving out to the edge and edge computing and nowhere is that more apparent than in autonomous vesicles so we're really excited to have maybe the best title at Western Digital, I don't know. Chris Bergey, VP of Product Marketing. That's not so special, but all the areas that he's involved with: mobile, compute, automotive, connected homes, smart cities, and if that wasn't enough, industrial IOT. Chris, you must be a busy guy. >> Hey, we're having a lot of fun here. This data world is an exciting place to be right now. >> so we're her at the Autonomous Vehicle event. We could talk about smart cities, which is pretty interesting, actually ties to it and internet of things and industrial internets, but what are some of the really unique challenges in autonomous vehicles that most people probably aren't thinking of? >> Well, I think that we all understand that really, autonomous vehicles are being made possible by just the immense amount of sensors that are being put into the car. Not much different than as our smartphones or our phones evolved from really not having a lot of sensors to today's smartphones have many, many sensors. Whether it's sensing your face, gyroscopes, GPS, all these kind of things. The car is having the exact thing happen but many, many more sensors. And, of course, those sensors just drive a tremendous amount of data and then it's really about trying to pull the intelligence out of that data and that's really what the whole artificial intelligence or autonomous is really trying to do is, okay, we've got all this data, how do I understand what's happening in the autonomous vehicle in a very short period of time? >> Right, and there's two really big factors that you've talked about and some of the other things that you've done. I did some homework and one of them is the metadata around the data, so there's the raw data itself that's coming off those sensors, but the metadata is a whole nother level, and a big level, and even more importantly is the context. What is the context of that data and without context, it's just data. It's not really intelligence or smarts or things you can do anything about so that baseline sensor data gets amplified significantly in terms of actually doing anything with that information. >> That's correct. I think one of the examples I give that's easier for people to understand is surveillance, right? We're very familiar with walking into a retail store where there's surveillance cameras and they're recording in the case that maybe there's a theft or something goes wrong, but there's so much data there that's not acutely being processed, right? How may people walked into the store? What was the average time a person came to the store? How many men? How many women? That's the context of the data and that's what's really would be very valuable if you were, say, an owner of the store or a regional manager. So that's really pulling the context out of the raw data. And in the car example, autonomous vehicles, hey, there's going to be something, my sensors are seeing something, and then, of course, you'd use multiple sensors. That's the sensor fusion between them of, "Hey, that's a person, that's a deer, oh, don't worry, "that's a car moving alongside of us and he's "staying in his lane." Those are the types of decisions we're making with this data and that's the context. >> Right, and even they had in the earlier presentation today the reflection of the car off the side of a bus, I mean, these are the nuance things that aren't necessarily obvious when you first start exploring. >> And we're dealing with human life, I mean, so obviously it needs to be right 99.999 plus percent. So that's the challenge, right? It's the corner cases and I think that's what we see with autonomous vehicles. It's really exciting to see the developments going on and, of course, there's been a couple challenges, but we just have so much learning to do to really get to that fifth nine or whatever it is from a probability point of view. And that's where we'll continue to work on those corner cases, but the technology is coming along so fast, it's just mind-boggling how quickly we are starting to attack these more difficult challenges. And we'll get there but it's going to take time like anything. >> The other really important thing, especially now where we're in the rise of Cloud, if you will. Amazon is going bananas. Google Cloud Platform, Microsoft Azure, so we're seeing this huge move of Cloud and enterprise IT. But in a car, right, there's this little thing called latency and this other thing called physics where you've got a real issue when you have to make a quick decision based on data and those sensors when something jumps out in front of the car. So really, the rise of edge computing and moving so much of that stored compute and intelligence into the vehicle and then deciding what goes back to the car to retrain the algorithm. So it's really a shift to back out to the edge, if you will, dependent because of this latency issue. >> Yeah, I mean, they're very complimentary, right? But there's a lot of decisions you can make locally and, obviously, there's a lot of advantages in doing that. Latency being one of them, but just cost of communications and again, what people don't necessarily understand is how big this data is. You see statistics thrown out there, one gigabit per second, two gigabits per second. I mean, that is just massive data. At the end of the day, actually, in some of the development, it's pretty interesting that we have the car developers actually FedExing the terabyte drives that they've captured data because it's the easiest way for them to actually transfer the data. I mean, people think, "Oh, internet connectivity, no problem." You try to ship 80 terabytes in a cost effective manner, FedEx ends up being the best shot right now. So it's pretty interesting. >> The old sneaker, that is pretty funny. But the quantities of this data are so big. I was teasing you on Twitter earlier today. I think we took it up to an xobyte, a zedobyte, a yodabyte, and then the crowd responded. No, it's a brontosaurousbyte is even bigger than a yodabyte. We were at Flink Forward earlier this week and really this whole idea of stream processing, it's really taking new approaches to data processing. You'll be able to take all that stuff in in real time, which probably state of the market now is financial trading and advertising markets. But to do that now in a car where if you make a mistake, there's really significant consequences. It's a really different challenge. >> It is and again, that's really this advent of the sensor data, right? The sensor data is going to swamp probably every other data set that's in the world, but a lot of it's not interesting because you don't know when that interesting event is going to happen. So what you actually find is that you try to put it's intelligence as close as you can to the data, end storage, and again, storage may be 30 seconds to if you had an accident, you want to be able to go back 30 seconds. It may be lifetimes. So just thinking about these data flows and what's the half life of the data relative to the value? But what we're actually finding with many of the machine learning is that data we thought was not valuable, data we thought, "Oh, we have the right amount of granularity," now with machine learning we're going back and saying, "Oh, why didn't we record at an even higher granularity?" We could have pulled out more of these trends or more of these corner cases. So I think that's one of the challenges enterprise are going through right now is that everyone's so scared of getting rid of any data, yet there's just tremendous data growth. And we're sitting right here in the middle of it at Western Digital. >> Well, thankfully for you guys, you're going to store all that data and it is really important, though, because it used to be, it's funny to me. It used to be a sample of things that happened in the past is how you would make your decisions. Now it's not a sample, it's all of what's happening now and hopefully you can make a decision while you still have time to have an impact. So it's a very different world but sampling is going away when, in theory, you don't know what you're going to need that data for and you have the ability to store it. >> Making real-time decisions but then also learning how to use that decision to make better decisions in the future. That's really where Silicon Valley's focused right now. >> All right, Chris, well you're a busy guy so we're going to let you get back to it because you also have to do IOT and industrial internet and mobile an compute. So thanks for taking ... >> And I try to eat in between there too. >> And you try to eat and hopefully see your kids Friday night, so hopefully you'll take >> Absolutely. your wife out to a movie tonight. >> All right, Chris, great to see you. Thanks for taking a few minutes. >> Chris: Thank you very much. >> All right, I'm Jeff Frick. You're watching The CUBE from Autotech Council Autonomous Vehicle event. Thanks for watching.

>> Announcer: From Milpitas, California at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick, here with theCUBE. We're in Milpitas, California, at Western Digital, at the Autotech Council Autonomous Vehicle Event. About 300 people, really deep into this space. It's a developing ecosystem. You know, we think about Tesla, that's kind of got a complete, closed system. But there's a whole ecosystem of other companies getting into the autonomous vehicle space, and as was mentioned in the keynote, there are, literally thousands of problems. A great opportunity for startups. So we're excited to have Oded Sagee, he's a senior director of product marketing from Western Digital. Oded, great to see you. >> Thank you very much, Jeff. >> So you were just on the panel and, really that was a big topic, is there are thousands of problems to solve and this ecosystem's trying to come together, but it's complicated, right? It's not just the big car manufacturers anymore, and the tier one providers, but there's this whole ecosystem that's now growing up to try to solve these problems. So what are you seeing from your point of view? >> Yes, correct. So, definitely in the past automotive was a tough market to play in, but it was simple from the amount of players and people you needed to talk to to design your product inside. With the disruption of connectivity, smart vehicles, even before autonomous, there are so many new systems in the car now that generate data or consume data. And so, for us, to kind of figure out what's the use case, right? How is this going to look in the future? Who's going to define it? Who's going to buy it? Who's going to pay for it? It has become more and more complex. Happily, storage is in the center of all this. >> Jeff: Right. >> So we get a seat at the table and everyone wants to talk to us, but yes, it's a very big ecosystem now. And trying to resolve that problem, it's going to take some time. >> So what are some of the unique characteristics, from a storage point of view, that you have to worry about? Obviously environmental jumps out. We had the guy on before talking about bumpy roads, you know, the huge impacts on vibration. And now you spent a lot of money for a Toughbook back in the day to put a laptop in a cop car, this is a whole other level of expense, investment, and data flow. >> Right. So, for us, I think with all this disruption happening of full autonomous, people are, very much focused on making that autonomous work, right? So, for them it's all about connectivity, it's all about the sensor, whether it's Lidar, or, you know, cameras. Just making that work, right? All the algorithms and the software. And so, for them storage, currently is an afterthought, right? They were saying, once we meet mass production we'll just go and buy some storage and everything's going to be fine. So while they're prototyping, right? They can use any storage that they want. But, if you think about a full autonomous vehicle out there driving, not two hours a day like we are driving today, right? 20 hours a day, from cold to hot, going through areas without connectivity. Suddenly, the storage requirements are very, very different. And this is what we're trying to drive and explain that, if we don't design the future storage solutions today, What's going to end up, is that people are going to pay much more for storage just to make a basic use case work. >> Right. >> But if we start working now, and I'm talking about five, seven years out, we can have affordable solutions to make those business models work. >> And is that resonating in the industry, or are they just too focused on, you know, better cameras? >> It definitely does, but as companies change, right? So let's just take the car makers for a second. They didn't necessarily have a CTO in place, right? To drive engineering and semi-conductor. So you got to find those figures, and you got to start working and educating them. It definitely resonates if you have the right person. Once you find him, yes, it's on the list of priority. So we need to push. But it is happening. Yes, it is resonating. >> And it's so different because you do have this edge case. You have so much data being collected out in the field, if you will, within that vehicle. Some, to go back to the cloud, but you've got latency is always an issue, right? For safety. So, a little different storage challenge. So are there significant design thoughts that you guys are bringing into play on why this is so different and what is it going to take to really have kind of an optimal solution for autonomous vehicles? >> Yes, definitely there are a couple of vectors I would say, or knobs we need to work on. One of them is temperature. So, again vehicles do tend to go between hot and cold. Unlike many other components that just need to make sure that they operate between hot and cold, we actually have a big challenge on keeping data being accurate between hot and cold. So if you program cold and read hot and vice versa, data gets corrupted. >> Oh, even within the structures within the media? >> Yes. >> Okay. >> And people don't know that. So, for us to figure out, what's the temperature range that the car, through its lifetime, is going to go through. And make sure that we meet the use case, that's a big one. What we call the endurance on the cycling of the storage, again, if you cannot rely on connectivity, cannot rely on cloud because of latency, you need to record a lot of data in the car. So, again, a car drives for seven years, 15 years, and you want to record constantly, how much do you need to record? We don't necessarily have the technology today to meet that use case and we need to work with the ecosystem, in figuring it out. So these are just two examples. >> And I would imagine clean power, as you're saying these things, but they can need others. You're not in daddy's data center anymore. This is a pretty harsh environment, I would imagine. >> Very harsh. >> Ugly power, inconsistent power, turning off the car before everything is spun down. There's all kinds of little, kind of environmental impacts in that whole realm that you would never think of in, kind of a typical data center, for instance. >> Correct. And even, you touched power, that's very interesting because even some people think, oh, there's not power limitation in a car. You can just enjoy how much power you want. Actually, it's very, very sensitive. The battery, if you think about an EV car now has so many components to run and so even the power consumption, right? Just the energy that you need to consume is becoming critical for each, and every component >> in the vehicle. >> Right. And it's everybody's AI comparison, right? Is if Kasparov had to fight the computer with the same amount of power, it wouldn't have been much of a match. So the power to run all this AI stuff is not insignificant, so it is going to be a huge drain on these electric vehicles. Pretty exciting times. So when you get up in the morning, what's the biggest thing, when you talk to people about autonomous vehicles, that they just don't get? That people should really be thinking about. >> Yeah, so it goes back to some of the things we've discussed. Definitely, again, we're seeing the use cases change. We are working again with the broad ecosystem to explain the fundamental challenges that we have, right? What is our design cycle? What are the challenges that we have? So we start with educating the ecosystem, so they know what we have. And from that we trigger a discussion because they realize, oh, okay, because I do have a use case that, probably, you don't have a solution for, how do we go together? And we're doing it across the board. It's not only happening in automotive. It's happening in surveillance. It's happening in the home space. A lot of people don't know, but the home space, if you think about it, again, set-top boxes used to be huge, sat outside in the room. People are moving to these sticks, right? And they're behind the TV and they have no ventilation and they're small and they record all the time. And they get to temperatures that we've never seen in the past. So we even need to educate the telcos of the world, the set-top box makers. Everything is changing. Automotive is definitely ahead in a lot of innovation and disruption, but everything is changing for us. >> Right, a lot of those are fond of just the bright shiny object that everybody can see, right? We can't necessarily see a lot of IOT that GE's putting in to connect their factories. Alright, Oded, well thanks for taking a few minutes out of your busy day and I really appreciate the insight. >> Thank you very much. >> All right, he's Oded, I'm Jeff, You're watching theCUBE from Western Digital at The Autonomous Vehicle Event for the Autotech Council. Thanks for watching. Catch you next time. (electronic music)

>> Announcer: Live, from Orlando, Florida, it's TheCUBE, covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to sunny Orlando everybody. This is TheCUBE, the leader in live tech coverage. My name is Dave Vellante and this is PentahoWorld, #PWorld17. Don DeLoach here, he's the co-chair of the midwest IoT council. Thanks so much for coming on TheCUBE. >> Good to be here. >> So you've just written a new book. I got it right in my hot off the presses in my hands. The Future of IoT, leveraging the shift to a data-centric world. Can you see that okay? Alright, great, how's that, you got that? Well congratulations on getting the book done. >> Thanks. >> It's like, the closest a male can come to having a baby, I guess. But, so, it's fantastic. Let's start with sort of the premise of the book. What, why'd you write it? >> Sure, I'll give you the short version, 'cause that in and of itself could go on forever. I'm a data guy by background. And for the last five or six years, I've really been passionate about IoT. And the two converged with a focus on data, but it was kind of ahead of where most people in IoT were, because they were mostly focused on sensor technology and communications, and to a limited extent, the workflow. So I kind of developed this thesis around where I thought the market was going to go. And I would have this conversation over and over and over, but it wasn't really sticking and so I decided maybe I should write a book to talk about it and it took me forever to write the book 'cause fundamentally I didn't know what I was doing. Fortunately, I was able to eventually bring on a couple of co-authors and collectively we were able to get the book written and we published it in May of this year. >> And give us the premise, how would you summarize? >> So the central thesis of the book is that the market is going to shift from a focus on IoT enabled products like a smart refrigerator or a low-fat fryer or a turbine in a factory or a power plant or whatever. It's going to shift from the IoT enabled products to the IoT enabled enterprise. If you look at the Harvard Business Review article that Jim Heppelmann and Michael Porter did in 2014, they talked about the progression from products to smart products to smart, connected products, to product systems, to system of systems. We've largely been focused on smart, connected products, or as I would call IoT enabled products. And most of the technology vendors have focused their efforts on helping the lighting vendor or the refrigerator vendor or whatever IoT enable their product. But when that moves to mass adoption of IoT, if you're the CIO or the CEO of SeaLand or Disney or Walmart or whatever, you're not going to want to be a company that has 100,000 IoT enabled products. You're going to want to be an IoT enabled company. And the difference is really all around data primacy and how that data is treated. So, right now, most of the data goes from the IoT enabled product to the product provider. And they tell you what data you can get. But that, if you look at the progression, it's almost mathematically impossible that that is sustainable because company, organizations are going to want to take my, like let's just say we're talking about a fast food restaurant. They're going to want to take the data from the low-fat fryer and the data from the refrigerator or the shake machine or the lighting system or whatever, and they're going to want to look at it in the context of the other data. And they're going to also want to combine it with their point-of-sale or crew scheduling, or inventory and then if they're smart, they'll start to even pull in external data, like pedestrian traffic or street traffic or microweather or whatever, and they'll create a much richer signature. And then, it comes down to governance, where I want to create this enriched data set, and then propagate it to the right constituent in the right time in the right way. So you still give the product provider back the data that they want, and there's nothing that precludes you from doing that. And you give the low-fat fryer provider the data that they want, but you give your regional and corporate offices a different view of the same data, and you give the FDA or your supply chain partner, it's still the same atomic data, but what you're doing is you're separating the creation of the data from the consumption of the data, and that's where you gain maximum leverage, and that's really the thesis of the book. >> It's data, great summary by the way, so it's data in context, and the context of the low-fat fryer is going to be different than the workflow within that retail operation. >> Yeah, that's right and again, this is where, the product providers have initially kind of pushed back because they feel like they have stickiness and loyalty that's bred out of that link. But, first of all, that's going to change. So if you're Walmart or a major concern and you say, "I'm going to do a lighting RFP," and there's 10 vendors that say, "Hey, we want to compete for this," and six of 'em will allow Walmart to control the data, and four say, "No, we have to control the data," their list just went to six. They're just not going to put up with that. >> Dave: Period, the end, absolutely. >> That's right. So if the product providers are smart, they're going to get ahead of this and say, "Look, I get where the market's going. "We're going to need to give you control of the data, "but I'm going to ask for a contract that says "I'm going to get the data I'm already getting, "'cause I need to get that, and you want me to get that. "But number two, I'm going to recognize that "they can give, Walmart can give me my data back, "but enrich it and contextualize it "so I get better data back." So everybody can win, but it's all about the right architecture. >> Well and the product guys going to have the Trojan horse strategy of getting in when nobody was really looking. >> Don: That's right. >> And okay, so they've got there. Do you envision, Don, a point at which the Walmart might say, "No, that's our data "and you don't get it." >> Um, not really- >> or is there going to be a quid pro quo? >> and here's why. The argument that the product providers have made all along is, almost in a condescending way sometimes, although not intentionally condescending, it's been, look, we're selling you this low-fat fryer for your fast food restaurant. And you say you want the data, but you know, we had a team of people who are experts in this. Leave that to us, we'll analyze the data and we'll give you back what you need. Now, there's some truth to the fact that they should know their products better than anybody, and if I'm the fast food chain, I want them to get that data so that they can continually analyze and help me do my job better. They just don't have to get that data at my expense. There are ways to cooperatively work this, but again, it comes back to just the right architecture. So what we call the first receiver is in essence, setting up an abstraction close to the point of the ingestion of all this data. Upon which it's cleansed, enriched, and then propagated again to the right constituent in the right time in the right way. And by the way, I would add, with the right security considerations, and with the right data privacy considerations, 'cause like, if you look around the market now, things like GEP are in Europe and what we've seen in the US just in the wake of the elections and everything around how data is treated, privacy concerns are going to be huge. So if you don't know how to treat the data in the context of how it needs to be leveraged, you're going to lose that leverage of the data. >> Well, plus the widget guys are going to say "Look, we have to do predictive maintenance "on those devices and you want us to do that." You know, they say follow the money. Let's follow the data. So, what's the data flow look like in your mind? You got these edge devices. >> Yep, physical or virtual. Doesn't have to be a physical edge. Although, in a lot of cases, there are good reasons why you'd want a physical edge, but there's nothing technologically that says you have to have a physical edge. >> Elaborate on that, would you? What do you mean by virtual? >> Sure, so let's say I have a server inside a retail outfit. And it's collecting all of my IoT data and consolidating it and persisting it into a data store and then propagating it to a variety of constituents. That would be creating the first receiver in the physical edge. There's nothing that says that that edge device can't grab that data, but then persist it in a distributed Amazon cloud instance, or a Rackspace instance or whatever. It doesn't actually need to be persisted physically on the edge, but there's no reason it can't either. >> Okay, now I understand that now. So the guys at Wikibon, which is a sort of sister company to TheCUBE, have envisioned this three tiered data model where you've got the devices at the edge where real-time activity's going on, real-time analytics, and then you've got this sort of aggregation point, I guess call it a gateway. And then you've got, and that's as I say, aggregation of all these edge devices. And then you've got the cloud where the heavy modeling is done. It could be your private cloud or your public cloud. So does that three tier model make sense to you? >> Yeah, so what you're describing as the first tier is actually the sensor layer. The gateway layer that you're describing, in the book would be characterized as the first receiver. It's basically an edge tier that is augmented to persist and enrich the data and then apply the proper governance to it. But what I would argue is, in reality, I mean, your reference architecture is spot-on. But if you actually take that one step further, it's actually an n-tier architecture. Because there's no reason why the data doesn't go from the ten franchise stores, to the regional headquarters, to the country headquarters, to the corporate headquarters, and every step along the way, including the edge, you're going to see certain types of analytics and computational work done. I'll put a plug for my friends at Hitachi Lumada in on this, you know, there's like 700 horizontal IoT platforms out there. There aren't going to be 700 winners. There's going to be probably eight to 10, and that's only because the different specific verticals will provide for more winners than it would be if it was just one like a search engine. But, the winners are going to have to have an extensible architecture that is, will ultimately allow enterprises to do the very things I'm talking about doing. And so there are a number out there, but one of the things, and Rob Tiffany, who's the CTO of Lumada, I think has a really good handle on his team on an architecture that is really plausible for accomplishing this as the market migrates into the future. >> And that architecture's got to be very flexible, not just elastic, but sometimes we use the word plastic, plasticity, being able to go in any direction. >> Well, sure, up to and including the use of digital twins and avatars and the logic that goes along with that and the ability to spin something up and spin something down gives you that flexibility that you as an enterprise, especially the larger the enterprise, the more important that becomes, need. >> How much of the data, Don, at that edge do you think will be persisted, two part question? It's not all going to be persisted, is it? Isn't that too expensive? Is it necessary to persist all of that data? >> Well, no. So this is where, you'll hear the notion of data exhaust. What that really means is, let's just say I'm instrumenting every room in this hotel and each room has six different sensors in it and I'm taking a reading once a second. The ratio of inconsequential to consequential data is probably going to be over 99 to one. So it doesn't really make sense to persist that data and it sure as hell doesn't make sense to take that data and push it into a cloud where I spend more to reduce the value of the payload. That's just dumb. But what will happen is that, there are two things, one, I think people will see the value in locally persisting the data that has value, the consequential data, and doing that in a way that's stored at least for some period of time so you can run the type of edge analytics that might benefit from having that persisted store. The other thing that I think will happen, and this is, I don't talk much, I talk a little bit about it in the book, but there's this whole notion where when we get to the volumes of data that we really talk about where IoT will go by like 2025, it's going to push the physical limitations of how we can accommodate that. So people will begin to use techniques like developing statistical metadata models that are a highly accurate metadata representation of the entirety of the data set, but probably in about one percent of the space that's queryable and suitable for machine learning where it's going to enable you to do what you just physically couldn't do before. So that's a little bit into the future, but there are people doing some fabulous work on that right now and that'll creep into the overall lexicon over time. >> Is that a lightweight digital twin that gives you substantially the same insight? >> It could augment the digital twin in ways that allow you to stand up digital twins where you might not be able to before. The thing that, the example that most people would know about are, like in the Apache ecosystem, there are toolsets like SnappyData that are basically doing approximation, but they're doing it via sampling. And that is a step in that direction, but what you're looking for is very high value approximation that doesn't lose the outlier. So like in IoT, one of the things you normally are looking for is where am I going to pick up on anomalous behavior? Well if I'm using a sample set, and I'm only taking 15%, I by definition am going to lose a lot of that anomalous behavior. So it has to be a holistic representation of the data, but what happens is that that data is transformed into statistics that can be queryable as if it was the atomic data set, but what you're getting is a very high value approximation in a fraction of the space and time and resources. >> Ok, but that's not sampling. >> No, it's statistical metadata. There are, there's a, my last company had developed a thing that we called approximate query, and it was based on that exact set of patents around the formation of a statistical metadata model. It just so happens it's absolutely suited for where IoT is going. It's kind of, IoT isn't really there yet. People are still trying to figure out the edge in its most basic forms, but the sheer weight of the data and the progression of the market is going to force people to be innovative in how they look at some of these things. Just like, if you look at things like privacy, right now, people think in terms of anonymization. And that's, basically, I'm going to de-link data contextually where I'm going to effectively lose the linkages to the context in order to conform with data privacy. But there are techniques, like if you look at GDCAR, their techniques, within certain safe harbors, that allow you to pseudonymize the data where you can actually relink it under certain conditions. And there are some smart people out there solving these problems. That's where the market's going to go, it's just going to get there over time. And what I would also add to this equation is, at the end of the day, right now, the concepts that are in the book about the first receiver and the create, the abstraction of the creation of the data from the consumption of the data, look, it's a pretty basic thing, but it's the type of shift that is going to be required for enterprises to truly leverage the data. The things about statistical metadata and pseudonymization, pseudonymization will come before the statistical metadata. But the market forces are going to drive more and more into those areas, but you got to walk before you run. Right now, most people still have silos, which is interesting, because when you think about the whole notion of the internet of things, it infers that it's this exploitation of understanding the state of physical assets in a very broad based environment. And yet, the funny thing is, most IoT devices are silos that emulate M2M, sort of peer to peer networks just using the internet as a communication vehicle. But that'll change. >> Right, and that's really again, back to the premise of the book. We're going from these individual products, where all the data is locked into the product silo, to this digital fabric, that is an enterprise context, not a product context. >> That's right and if you go to the toolsets that Pentaho offers, the analytic toolsets. Let's just say, now that I've got this rich data set, assuming I'm following basic architectural principles so that I can leverage the maximum amount of data, that now gives me the ability to use these type of toolsets to do far better operational analytics to know what's going on, far better forensic analysis and investigative analytics to mine through the date and do root cause analysis, far better predictive analytics and prescriptive analytics to figure out what will go on, and ultimately feed the machine learning algorithms ultimately to get to in essence, the living organism, the adaptive systems that are continuously changing and adapting to circumstances. That's kind of the Holy Grail. >> You mentioned Hitachi Vantara before. I'm curious what your thoughts are on the Hitachi, you know, two years ago, we saw the acquisition, said, okay, now what? And you know, on paper it sounded good, and now it starts to come together, it starts to make more sense. You know, storage is going to the cloud. HDS says, alright, well we got this Hitachi relationship. But what do you make of that? How do you assess it, and where do you see it going? >> First of all, I actually think the moves that they've done are good. And I would not say that if I didn't think it. I'd just find a politically correct way not to say that. But I do think it's good. So they created the Hitachi Insight Group about a year and a half ago, and now that's been folded into Hitachin Vantara, alongside HDS and Pentaho and I think that it's a fairly logical set of elements coming together. I think they're going down the right path. In full disclosure, I worked for Hitachi Data Systems from '91 til '94, so it's not like I'm a recent employee of them, it's 25 years ago, but my experience with Hitachi corporate and the way they approach things has been unlike a lot of really super large companies, who may be super large, but may not be the best engineers, or may not always get everything done so well, Hitachi's a really formidable organization. And I think what they're doing with Pentaho and HDS and the Insight Group and specifically Lumada, is well thought out and I'm optimistic about where they're going. And by the way, they won't be the only winner in the equation. There's going to be eight or nine different key players, but they'll, I would not short them whatsoever. I have high hopes for them. >> The TAM is enormous. Normally, Hitachi eventually gets to where it wants to go. It's a very thoughtful company. I've been watching them for 30 years. But to a lot of people, the Pentaho and the Insight's play make a lot of sense, and then HDS, you used to work for HDS, lot of infrastructure still, lot of hardware, but a relationship with Hitachi Limited, that is quite strong, where do you see that fit, that third piece of the stool? >> So, this is where there's a few companies that have unique advantages, with Hitachi being one of them. Because if you think about IoT, IoT is the intersection of information technology and operational technology. So it's one thing to say, "I know how to build a database." or "I can build machine learning algorithms," or whatever. It's another thing to say, "I know how to build trains "or CAT scans or smart city lighting systems." And the domain expertise married with the technology delivers a set of capabilities that you can't match without that domain expertise. And, I mean, if you even just reduce it down to artificial intelligence and machine learning, you get an expert ML or AI guy, and they're only as good as the limits of their domain expertise. So that's why, and again, that's why I go back to the comparison to search engines, where there's going to be like, there's Google and maybe Yahoo. There's probably going to be more platform winners because the vertical expertise is going to be very, very important, but there's not going to be 700 of 'em. But Hitachi has an advantage that they bring to the table, 'cause they have very deep roots in energy, in medical equipment, in transportation. All of that will manifest itself in what they're doing in a big way, I think. >> Okay, so, but a lot of the things that you described, and help me understand this, are Hitachi Limited. Now of course, Hitachi Data Systems started as, National Advance Systems was a distribution arm for Hitachi IT products. >> Don: Right, good for you, not many people remember. >> I'm old. So, like I said, I had a 30 year history with this company. Do you foresee that that, and by the way, interestingly, was often criticized back when you were working for HDS, it was like, it's still a distribution hub, but in the last decade, HDS has become much more of a contributor to the innovation and the product strategy and so forth. Having said that, it seems to me advantageous if some of those things you discussed, the trains, the medical equipment, can start flowing back through HDS. I'm not sure if that's explicitly the plan. I didn't necessarily hear that, but it sort of has to, right? >> Well, I'm not privy to those discussions, so it would be conjecture on my part. >> Let's opine, but right, doesn't that make sense? >> Don: It makes perfect sense. >> Because, I mean HDS for years was just this storage silo. And then storage became a very uninteresting business, and credit to Hitachi for pivoting. But it seems to me that they could really, and they probably have a, I had Brian Householder on earlier I wish I had explored this more with him. But it just seems, the question for them is, okay, how are you going to tap those really diverse businesses. I mean, it's a business like a GE or a Siemens. I mean, it's very broad based. >> Well, again, conjecture on my part, but one way I would do it would be to start using Lumada in the various operations, the domain-specific operations right now with Hitachi. Whether they plan to do that or not, I'm not sure of. I've heard that they probably will. >> That's a data play, obviously, right? >> Well it's a platform play. And it's enabling technology that should augment what's already going on in the various elements of Hitachi. Again, I'm, this is conjecture on my part. But you asked, let's just go with this. I would say that makes a lot of sense. I'd be surprised if they don't do that. And I think in the process of doing that, you start to crosspollinate that expertise that gives you a unique advantage. It goes back to if you have unique advantages, you can choose to exploit them or not. Very few companies have the set of unique advantages that somebody like Hitachi has in terms of their engineering and massive reach into so many, you know, Hitachi, GE, Siemens, these are companies that have big reach to the extent that they exploit them or not. One of the things about Hitachi that's different than almost anybody though is they have all this domain expertise, but they've been in the technology-specific business for a long time as well, making computers. And so, they actually already have the internal expertise to crosspollinate, but you know, whether they do it or not, time will tell. >> Well, but it's interesting to watch the big whales, the horses in the track, if you will. Certainly GE has made a lot of noise, like, okay, we're a software company. And now you're seeing, wow, that's not so easy, and then again, I'm sanguine about GE. I think eventually they'll get there. And then you see IBM's got their sort of IoT division. They're bringing in people. Another company with a lot of IT expertise. Not a lot of OT expertise. And then you see Hitachi, who's actually got both. Siemens I don't know as well, but presumably, they're more OT than IT and so you would think that if you had to evaluate the companies' positions, that Hitachi's in a unique position. Certainly have a lot of software. We'll see if they can leverage that in the data play, obviously Pentaho is a key piece of that. >> One would assume, yeah for sure. No, I mean, I again, I think, I'm very optimistic about their future. I think very highly of the people I know inside that I think are playing a role here. You know, it's not like there aren't people at GE that I think highly of, but listen, you know, San Ramon was something that was spun up recently. Hitachi's been doing this for years and years and years. You know, so different players have different capabilities, but Hitachi seems to have sort of a holistic set of capabilities that they can bring together and to date, I've been very impressed with how they've been going about it. And especially with the architecture that they're bringing to bear with Lumada. >> Okay, the book is The Future of IoT, leveraging the shift to a data-centric world. Don DeLoach, and you had a co-author here as well. >> I had two co-authors. One is Wael Elrifai from Pentaho, Hitachi Vantara and the other is Emil Berthelsen, a Gartner analyst who was with Machina Research and then Gartner acquired them and Emil has stayed on with them. Both of them great guys and we wouldn't have this book if it weren't for the three of us together. I never would have pulled this off on my own, so it's a collective work. >> Don DeLoach, great having you on TheCUBE. Thanks very much for coming on. Alright, keep it right there buddy. We'll be back. This is PentahoWorld 2017, and this is TheCUBE. Be right back.

hey welcome back everybody Jeff Rick here with the cube we're at the mill pedis at an interesting event is called the auto tech council innovation in motion mapping and navigation event so a lot of talk about autonomous vehicles so it's a lot of elements to autonomous vehicles this is just one small piece of it it's about mapping and navigation and we're excited to have with us our first guest again and give us a background of this whole situation just Derick Curtin and he's the founder and chairman of the auto tech council so first up there welcome thank you very much good to be here absolutely so for the folks that aren't familiar what is the auto tech council autofit council is a sort of a club based in Silicon Valley where we have gathered together some of the industry's largest OMS om is mean car makers you know of like Rio de Gono from France and a variety of other ones they have offices here in Silicon Valley right and their job is to find innovation you find that Silicon Valley spark and take it back and get it into cars eventually and so what we are able to do is gather them up put them in a club and route a whole bunch of Silicon Valley startups and startups from other places to in front of them in a sort of parade and say these are some of the interesting technologies of the month so did they reach out for you did you see an opportunity because obviously they've all got the the Innovation Centers here we were at the Ford launch of their innovation center you see that the tagline is all around is there too now Palo Alto and up and down the peninsula so you know they're all here so was this something that they really needed an assist with that something opportunity saw or was it did it come from more the technology side to say we needed I have a new one to go talk to Raja Ford's well it's certainly true that they came on their own so they spotted Silicon Valley said this is now relevant to us where historically we were able to do our own R&D build our stuff in Detroit or in Japan or whatever the cases all of a sudden these Silicon Valley technologies are increasingly relevant to us and in fact disruptive to us we better get our finger on that pulse and they came here of their own at the time we were already running something called the telecom Council Silicon Valley where we're doing a similar thing for phone companies here so we had a structure in place that we needed to translate that into beyond modem industry and meet all those guys and say listen we can help you we're going to be a great tool in your toolkit to work the valley ok and then specifically what types of activities do you do with them to execute division you know it's interesting when we launched this about five years ago we're thinking well we have telecommunication back when we don't have the automotive skills but we have the organizational skills what turned out to be the cases they're not coming here the car bakers and the tier 1 vendors that sell to them they're not coming here to study break pad material science and things like that they're coming to Silicon Valley to find the same stuff the phone company two years ago it's lookin at least of you know how does Facebook work in a car out of all these sensors that we have in phones relate to automotive industry accelerometers are now much cheaper because of reaching economies of scale and phones so how do we use those more effectively hey GPS is you know reach scale economies how do we put more GPS in cars how do we provide mapping solutions all these things you'll set you'll see and sound very familiar right from that smartphone industry in fact the thing that disrupts them the thing that they're here for that brought them here and out of out of defensive need to be here is the fact that the smartphone itself was that disruptive factor inside the car right right so you have events like today so gives little story what's it today a today's event is called the mapping and navigation event what are people who are not here what's what's happening well so every now and then we pick a theme that's really relevant or interesting so today is mapping and navigation actually specifically today is high definition mapping and sensors and so there's been a battle in the automotive industry for the autonomous driving space hey what will control an autonomous car will it be using a map that's stored in memory onboard the car it knows what the world looked like when they mapped it six months ago say and it follows along a pre-programmed route inside of that world a 3d model world or is it a car more likely with the Tesla's current they're doing where it has a range of sensors on it and the sensors don't know anything about the world around the corner they only know what they're sensing right around them and they drive within that environment so there's two competing ways of modeling a 3d world around autonomous car and I think you know there was a battle looking backwards which one is going to win and I think the industry has come to terms with the fact the answer is both more everyday and so today we're talking about both and how to infuse those two and make better self-driving vehicles so for the outsider looking in right I'm sure they get wait the mapping wars are over you know Google Maps what else is there right but then I see we've got TomTom and meet a bunch of names that we've seen you know kind of pre pre Google Maps and you know shame on me I said the same thing when Google came out with a cert I'm like certain doors are over who's good with so so do well so Eddie's interesting there's a lot of different angles to this beyond just the Google map that you get on your phone well anything MapQuest what do you hear you moved on from MapQuest you print it out you're good together right well that's my little friends okay yeah some people written about some we're burning through paper listen the the upshot is that you've MapQuest is an interesting starting board probably first it's these maps folding maps we have in our car there's a best thing we have then we move to MapQuest era and $5,000 Sat Navs in some cars and then you might jump forward to where Google had kind of dominate they offered it for free kicked you know that was the disruptive factor one of the things where people use their smartphones in the car instead of paying $5,000 like car sat-nav and that was a long-running error that we have in very recent memory but the fact of the matter is when you talk about self-driving cars or autonomous vehicles now you need a much higher level of detail than TURN RIGHT in 400 feet right that's that's great for a human who's driving the car but for a computer driving the car you need to know turn right in 400.000 five feet and adjust one quarter inch to the left please so the level of detail requires much higher and so companies like TomTom like a variety of them that are making more high-level Maps Nokia's form a company called here is doing a good job and now a class of car makers lots of startups and there's crowdsource mapping out there as well and the idea is how do we get incredibly granular high detail maps that we can push into a car so that it has that reference of a 3d world that is extremely accurate and then the next problem is oh how do we keep those things up to date because when we Matt when when a car from this a Nokia here here's the company house drives down the street does a very high-level resolution map with all the equipment you see on some of these cars except for there was a construction zone when they mapped it and the construction zone is now gone right update these things so these are very important questions if you want to have to get the answers correct and in the car stored well for that credit self drive and once again we get back to something to mention just two minutes ago the answer is sensor fusion it's a map as a mix of high-level maps you've got in the car and what the sensors are telling you in real time so the sensors are now being used for what's going on right now and the maps are give me a high level of detail from six months ago and when this road was driven it's interesting back of the day right when we had to have the CD for your own board mapping Houston we had to keep that thing updated and you could actually get to the edge of the sea didn't work we were in the islands are they covering here too which feeds into this is kind of of the optical sensors because there's kind of the light our school of thought and then there's the the biopic cameras tripod and again the answers probably both yeah well good that's a you know that's there's all these beat little battles shaping up in the industry and that's one of them for sure which is lidar versus everything else lidar is the gold standard for building I keep saying a 3d model and that's basically you know a computer sees the world differently than your eye your eye look out a window we build a 3d model of what we're looking at how does computer do it so there's a variety of ways you can do it one is using lidar sensors which spin around biggest company in this space is called Bella died and been doing it for years for defense and aviation it's been around pointing laser lasers and waiting for the signal to come back so you basically use a reflected signal back and the time difference it takes to be billows back it builds a 3d model of the objects around that particular sensor that is the gold standard for precision the problem is it's also bloody expensive so the karmak is said that's really nice but I can't put for $8,000 sensors on each corner of a car and get it to market at some price that a consumers willing to pay so until every car has one and then you get the mobile phone aside yeah but economies of scale at eight thousand dollars we're looking at going that's a little stuff so there's a lot of startups now saying this we've got a new version of lighter that's solid-state it's not a spinning thing point it's actually a silicon chip with our MEMS and stuff on it they're doing this without the moving parts and we can drop the price down to two hundred dollars maybe a hundred dollars in the future and scale that starts being interesting that's four hundred dollars if you put it off all four corners of the car but there's also also other people saying listen cameras are cheap and readily available so you look at a company like Nvidia that has very fast GPUs saying listen our GPUs are able to suck in data from up to 12 cameras at a time and with those different stereoscopic views with different angle views we can build a 3d model from cheap cameras so there's competing ideas on how you build a model of the world and then those come to like Bosh saying well we're strong in car and written radar and we can actually refine our radar more and more and get 3d models from radar it's not the good resolution that lidar has which is a laser sense right so there's all these different sensors and I think there the answer is not all of them because cost comes into play below so a car maker has to choose well we're going to use cameras and radar we're gonna use lidar and high heaven so they're going to pick from all these different things that are used to build a high-definition 3d model of the world around the car cost effective and successful and robust can handle a few of the sensors being covered by snow hopefully and still provide a good idea of the world around them and safety and so they're going to fuse these together and then let their their autonomous driving intelligence right on top of that 3d model and drive the car right so it's interesting you brought Nvidia in what's really fun I think about the autonomous vehicle until driving cars and the advances is it really plays off the kind of Moore's laws impact on the three tillers of its compute right massive compute power to take the data from these sensors massive amounts of data whether it's in the pre-programmed map whether you're pulling it off the sensors you're pulling off a GPS lord knows where by for Wi-Fi waypoints I'm sure they're pulling all kinds of stuff and then of course you know storage you got to put that stuff the networking you gotta worry about latency is it on the edge is it not on the edge so this is really an interesting combination of technologies all bring to bear on how successful your car navigates that exit ramp you're spot-on and that's you're absolutely right and that's one of the reasons I'm really bullish on self-driving cars a lot more than in the general industry analyst is and you mentioned Moore's law and in videos taking advantage of that with a GPUs so let's wrap other than you should be into kind of big answer Big Data and more and more data yes that's a huge factor in cars not only are cars going to take advantage of more and more data high definition maps are way more data than the MapQuest Maps we printed out so that's a massive amount of data the car needs to use but then in the flipside the cars producing massive amounts of data I just talked about a whole range of sensors I talked lidar radar cameras etc that's producing data and then there's all the telemetric data how's the car running how's the engine performing all those things car makers want that data so there's massive amounts of data needing to flow both ways now you can do that at night over Wi-Fi cheaply you can do it over an LTE and we're looking at 5g regular standards being able to enable more transfer of data between the cars and the cloud so that's pretty important cloud data and then cloud analytics on top of that ok now that we've got all this data from the car what do we do with it we know for example that Tesla uses that data sucked out of cars to do their fleet driving their fleet learning so instead of teaching the cars how to drive I'm a programmer saying if you see this that they're they're taking the information out of the cars and saying what are the situation these cars are seen how did our autonomous circuitry suggest the car responds and how did the user override or control the car in that point and then they can compare human driving with their algorithms and tweak their algorithms based on all that fleet to driving so it's a master advantage in sucking data out of cars massive advantage of pushing data to cars and you know we're here at Kingston SanDisk right now today so storage is interesting as well storage in the car increasingly important through these big amount of data right and fast storage as well High Definition maps are beefy beefy maps so what do you do do you have that in the cloud and constantly stream it down to the car what if you drive through a tunnel or you go out of cellular signal so it makes sense to have that map data at least for the region you're in stored locally on the car in easily retrievable flash memory that's dropping in price as well alright so loop in the last thing about that was a loaded question by the way and I love it and this is the thing I love this is why I'm bullish and more crazier than anybody else about the self-driving car space you mentioned Moore's law I find Moore's law exciting used to not be relevant to the automotive industry they used to build except we talked about I talked briefly about brake pad technology material science like what kind of asbestos do we use and how do we I would dissipate the heat more quickly that's science physics important Rd does not take advantage of Moore's law so cars been moving along with laws of thermodynamics getting more miles per gallon great stuff out of Detroit out of Tokyo out of Europe out of Munich but Moore's law not entirely relevant all of a sudden since very recently Moore's law starting to apply to cars so they've always had ECU computers but they're getting more compute put in the car Tesla has the Nvidia processors built into the car many cars having stronger central compute systems put in okay so all of a sudden now Moore's law is making cars more able to do things that they we need them to do we're talking about autonomous vehicles couldn't happen without a huge central processing inside of cars so Moore's law applying now what it did before so cars will move quicker than we thought next important point is that there's other there's other expansion laws in technology if people look up these are the cool things kryder's law so kryder's law is a law about storage in the rapidly expanding performance of storage so for $8.00 and how many megabytes or gigabytes of storage you get well guess what turns out that's also exponential and your question talked about isn't dat important sure it is that's why we could put so much into the cloud and so much locally into the car huge kryder's law next one is Metcalfe's law Metcalfe's law has a lot of networking in it states basically in this roughest form the value of network is valued to the square of the number of nodes in the network so if I connect my car great that's that's awesome but who does it talk to nobody you connect your car now we can have two cars you can talk together and provide some amount of element of car to car communications and some some safety elements tell me the network is now connected I have a smart city all of a sudden the value keeps shooting up and up and up so all of these things are exponential factors and there all of a sudden at play in the automotive industry so anybody who looks back in the past and says well you know the pace of innovation here has been pretty steep it's been like this I expect in the future we'll carry on and in ten years we'll have self-driving cars you can't look back at the slope of the curve right and think that's a slope going forward especially with these exponential laws at play so the slope ahead is distinctly steeper in this deeper and you left out my favorite law which is a Mars law which is you know we underestimate in the short term or overestimate in the short term and underestimate in the long term that's all about it's all about the slope so there we could go on for probably like an hour and I know I could but you got a kill you got to go into your event so thanks for taking min out of your busy day really enjoyed the conversation and look forward to our next one my pleasure thanks all right Jeff Rick here with the Q we're at the Western Digital headquarters in Milpitas at the Auto Tech Council innovation in motion mapping and navigation event thanks for watching

>> Hello everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE been profiling the leaders in the technology world, women in technology from developers to the boardroom, everything in between. We have two great guests promoting in from Malaysia. Nancy Wang is the general manager, also CUBE alumni from AWS Data Protection, and founder and board chair of Advancing Women in Tech, awit.org. And of course Kate Watts who's the executive director of Advancing Women in Tech.org. So it's awit.org. Nancy, Kate, thanks for coming all the way across remotely from Malaysia. >> Of course, we're coming to you as fast as our internet bandwidth will allow us. And you know, I'm just thrilled today that you get to see a whole nother aspect of my life, right? Because typically we talk about AWS, and here we're talking about a topic near and dear to my heart. >> Well, Nancy, I love the fact that you're spending a lot of time taking the empowerment to go out and help the industries and helping with the advancement of women in tech. Kate, the executive director it's a 501C3, it's nonprofit, dedicating to accelerating the careers of women in groups in tech. Can you talk about the organization? >> Yes, I can. So Advancing Women in Tech was founded in 2017 in order to fix some of the pathway problems that we're seeing on the rise to leadership in the industry. And so we specifically focus on supporting mid-level women in technical roles, get into higher positions. We do that in a few different ways through mentorship programs through building technical skills and by connecting people to a supportive community. So you have your peer network and then a vertical sort of relationships to help you navigate the next steps in your career. So to date we've served about 40,000 individuals globally and we're just looking to expand our reach and impact and be able to better support women in the industry. >> Nancy, talk about the creation, the origination story. How'd this all come together? Obviously the momentum, everyone in the industry's been focused on this for a long time. Where did AWIT come from? Advancing Women in Technology, that's the acronym. Advancing Women in Technology.org, where'd it come from? What's the origination story? >> Yeah, so AWIT really originated from this desire that I had, to Kate's point around, well if you look around right and you know, don't take my word for it, right? Look at stats, look at news reports, or just frankly go on your LinkedIn and see how many women in underrepresented groups are in senior technical leadership roles right out in the companies whose names we all know. And so that was my case back in 2016. And so when I first got the idea and back then I was actually at Google, just another large tech company in the valley, right? It was about how do we get more role models, how we get more, for example, women into leadership roles so they can bring up the next generation, right? And so this is actually part of a longer speech that I'm about to give on Wednesday and part of the US State Department speaker program. In fact, that's why Kate and I are here in Malaysia right now is working with over 200 women entrepreneurs from all over in Southeast Asia, including Malaysia Philippines, Vietnam, Borneo, you know, so many countries where having more women entrepreneurs can help raise the GDP right, and that fits within our overall mission of getting more women into top leadership roles in tech. >> You know, I was talking about Teresa Carlson she came on the program as well for this year this next season we're going to do. And she mentioned the decision between the US progress and international. And she's saying as much as it's still bad numbers, it's worse than outside the United States and needs to get better. Can you comment on the global aspect? You brought that up. I think it's super important to highlight that it's just not one area, it's a global evolution. >> Absolutely, so let me start, and I'd love to actually have Kate talk about our current programs and all of the international groups that we're working with. So as Teresa aptly mentioned there is so much work to be done not just outside the US and North Americas where typically tech nonprofits will focus, but rather if you think about the one to end model, right? For example when I was doing the product market fit workshop for the US State Department I had women dialing in from rice fields, right? So let me just pause there for a moment. They were holding their cell phones up near towers near trees just so that they can get a few minutes of time with me to do a workshop and how to accelerate their business. So if you don't call that the desire to propel oneself or accelerate oneself, not sure what is, right. And so it's really that passion that drove me to spend the next week and a half here working with local entrepreneurs working with policy makers so we can take advantage and really leverage that passion that people have, right? To accelerate more business globally. And so that's why, you know Kate will be leading our contingent with the United Nations Women Group, right? That is focused on women's economic empowerment because that's super important, right? One aspect can be sure, getting more directors, you know vice presidents into companies like Google and Amazon. But another is also how do you encourage more women around the world to start businesses, right? To reach economic and freedom independence, right? To overcome some of the maybe social barriers to becoming a leader in their own country. >> Yes, and if I think about our own programs and our model of being very intentional about supporting the learning development and skills of women and members of underrepresented groups we focused very much on providing global access to a number of our programs. For instance, our product management certification on Coursera or engineering management our upcoming women founders accelerator. We provide both access that you can get from anywhere. And then also very intentional programming that connects people into the networks to be able to further their networks and what they've learned through the skills online, so. >> Yeah, and something Kate just told me recently is these courses that Kate's mentioning, right? She was instrumental in working with the American Council on Education and so that our learners can actually get up to six college credits for taking these courses on product management engineering management, on cloud product management. And most recently we had our first organic one of our very first organic testimonials was from a woman's tech bootcamp in Nigeria, right? So if you think about the worldwide impact of these upskilling courses where frankly in the US we might take for granted right around the world as I mentioned, there are women dialing in from rice patties from other, you know, for example, outside the, you know corporate buildings in order to access this content. >> Can you think about the idea of, oh sorry, go ahead. >> Go ahead, no, go ahead Kate. >> I was going to say, if you can't see it, you can't become it. And so we are very intentional about ensuring that we have we're spotlighting the expertise of women and we are broadcasting that everywhere so that anybody coming up can gain the skills and the networks to be able to succeed in this industry. >> We'll make sure we get those links so we can promote them. Obviously we feel the same way getting the word out. I think a couple things I'd like to ask you guys cause I think you hit a great point. One is the economic advantage the numbers prove that diverse teams perform better number one, that's clear. So good point there. But I want to get your thoughts on the entrepreneurial equation. You mentioned founders and startups and there's also different makeups in different countries. It's not like the big corporations sometimes it's smaller business in certain areas the different cultures have different business sizes and business types. How do you guys see that factoring in outside the United States, say the big tech companies? Okay, yeah. The easy lower the access to get in education than stay with them, in other countries is it the same or is it more diverse in terms of business? >> So what really actually got us started with the US State Department was around our work with women founders. And I love for Kate to actually share her experience working with AWS startups in that capacity. But frankly, you know, we looked at the content and the mentor programs that were providing women who wanted to be executives, you know, quickly realize a lot of those same skills such as finding customers, right? Scaling your product and building channels can also apply to women founders, not just executives. And so early supporters of our efforts from firms such as Moderna up in Seattle, Emergence Ventures, Decibel Ventures in, you know, the Bay Area and a few others that we're working with right now. Right, they believed in the mission and really helped us scale out what is now our existing platform and offerings for women founders. >> Those are great firms by the way. And they also are very founder friendly and also understand the global workforce. I mean, that's a whole nother dimension. Okay, what's your reaction to all that? >> Yes, we have been very intentional about taking the product expertise and the learnings of women and in our network, we first worked with AWS startups to support the development of the curriculum for the recent accelerator for women founders that was held last spring. And so we're able to support 25 founders and also brought in the expertise of about 20 or 30 women from Advancing Women in Tech to be able to be the lead instructors and mentors for that. And so we have really realized that with this network and this individual sort of focus on product expertise building strong teams, we can take that information and bring it to folks everywhere. And so there is very much the intentionality of allowing founders allowing individuals to take the lessons and bring it to their individual circumstances and the cultures in which they are operating. But the product sense is a skill that we can support the development of and we're proud to do so. >> That's awesome. Nancy, I want to ask you some never really talk about data storage and AWS cloud greatness and goodness, here's different and you also work full-time at AWS and you're the founder or the chairman of this great organization. How do you balance both and do you get, they're getting behind you on this, Amazon is getting behind you on this. >> Well, as I say it's always easier to negotiate on the way in. But jokes aside, I have to say the leadership has been tremendously supportive. If you think about, for example, my leaders Wayne Duso who's also been on the show multiple times, Bill Vaas who's also been on the show multiple times, you know they're both founders and also operators entrepreneurs at heart. So they understand that it is important, right? For all of us, it's really incumbent on all of us who are in positions to do so, to create a pathway for more people to be in leadership roles for more people to be successful entrepreneurs. So, no, I mean if you just looked at LinkedIn they're always uploading my vote so they reach to more audiences. And frankly they're rooting for us back home in the US while we're in Malaysia this week. >> That's awesome. And I think that's a good culture to have that empowerment and I think that's very healthy. What's next for you guys? What's on the agenda? Take us through the activities. I know that you got a ton of things happening. You got your event out there, which is why you're out there. There's a bunch of other activities. I think you guys call it the Advancing Women in Tech week. >> Yes, this week we are having a week of programming that you can check out at Advancing Women in Tech.org. That is spotlighting the expertise of a number of women in our space. So it is three days of programming Tuesday, Wednesday and Thursday if you are in the US so the seventh through the ninth, but available globally. We are also going to be in New York next week for the event at the UN and are looking to continue to support our mentorship programs and also our work supporting women founders throughout the year. >> All right. I have to ask you guys if you don't mind get a little market data so you can share with us here at theCUBE. What are you hearing this year that's different in the conversation space around the topics, the interests? Obviously I've seen massive amounts of global acceleration around conversations, more video, things like this more stories are scaling, a lot more LinkedIn activity. It just seems like it's a lot different this year. Can you guys share any kind of current trends you're seeing relative to the conversations and topics being discussed across the the community? >> Well, I think from a needle moving perspective, right? I think due to the efforts of wonderful organizations including the Q for spotlighting all of these awesome women, right? Trailblazing women and the nonprofits the government entities that we work with there's definitely more emphasis on creating access and creating pathways. So that's probably one thing that you're seeing is more women, more investors posting about their activities. Number two, from a global trend perspective, right? The rise of women in security. I noticed that on your agenda today, you had Lena Smart who's a good friend of mine chief information security officer at MongoDB, right? She and I are actually quite involved in helping founders especially early stage founders in the security space. And so globally from a pure technical perspective, right? There's right more increasing regulations around data privacy, data sovereignty, right? For example, India's in a few weeks about to get their first data protection regulation there locally. So all of that is giving rise to yet another wave of opportunity and we want women founders uniquely positioned to take advantage of that opportunity. >> I love it. Kate, reaction to that? I mean founders, more pathways it sounds like a neural network, it sounds like AI enabled. >> Yes, and speaking of AI, with the rise of that we are also hearing from many community members the importance of continuing to build their skills upskill learn to be able to keep up with the latest trends. There's a lot of people wondering what does this mean for my own career? And so they're turning to organizations like Advancing Women in Tech to find communities to both learn the latest information, but also build their networks so that they are able to move forward regardless of what the industry does. >> I love the work you guys are doing. It's so impressive. I think the economic angle is new it's more amplified this year. It's always kind of been there and continues to be. What do you guys hope for by next year this time what do you hope to see different from a needle moving perspective, to use your word Nancy, for next year? What's the visual output in your mind? >> I want to see real effort made towards 50-50 representation in all tech leadership roles. And I'd like to see that happen by 2050. >> Kate, anything on your end? >> I love that. I'm going to go a little bit more touchy-feely. I want everybody in our space to understand that the skills that they build and that the networks they have carry with them regardless of wherever they go. And so to be able to really lean in and learn and continue to develop the career that you want to have. So whether that be at a large organization or within your own business, that you've got the potential to move forward on that within you. >> Nancy, Kate, thank you so much for your contribution. I'll give you the final word. Put a plug in for the organization. What are you guys looking for? Any kind of PSA you want to share with the folks watching? >> Absolutely, so if you're in a position to be a mentor, join as a mentor, right? Help elevate and accelerate the next generation of women leaders. If you're an investor help us invest in more women started companies, right? Women founded startups and lastly, if you are women looking to accelerate your career, come join our community. We have resources, we have mentors and who we have investors who are willing to come in on the ground floor and help you accelerate your business. >> Great work. Thank you so much for participating in our International Women's Day 23 program and we'd look to keep this going quarterly. We'll see you next year, next time. Thanks for coming on. Appreciate it. >> Thanks so much John. >> Thank you. >> Okay, women leaders here. >> Nancy: Thanks for having us >> All over the world, coming together for a great celebration but really highlighting the accomplishments, the pathways the investment, the mentoring, everything in between. It's theCUBE. Bring as much as we can. I'm John Furrier, your host. Thanks for watching.

(upbeat music) >> Hi, everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. As part of International Women's Day, we're featuring some of the leading women in business technology from developer to all types of titles and to the executive level. And one topic that's really important is called Getting a Seat at the Table, board makeup, having representation at corporate boards, private and public companies. It's been a big push. And former technology operating executive and corporate board member, she's a board machine Sue Barsamian, formerly with HPE, Hewlett Packard. Sue, great to see you. CUBE alumni, distinguished CUBE alumni. Thank you for coming on. >> Yes, I'm very proud of my CUBE alumni title. >> I'm sure it opens a lot of doors for you. (Sue laughing) We're psyched to have you on. This is a really important topic, and I want to get into the whole, as women advance up, and they're sitting on the boards, they can implement policy and there's governance. Obviously public companies have very strict oversight, and not strict, but like formal. Private boards have to operate, be nimble. They don't have to share all their results. But still, boards play an important role in the success of scaled up companies. So super important, that representation there is key. >> Yes. >> I want to get into that, but first, before we get started, how did you get into tech? How did it all start for you? >> Yeah, long time ago, I was an electrical engineering major. Came out in 1981 when, you know, opportunities for engineering, if you were kind, I went to Kansas State as an undergrad, and basically in those days you went to Texas and did semiconductors. You went to Atlanta and did communication satellites. You went to Boston or you went to Silicon Valley. And for me, that wasn't too hard a choice. I ended up going west and really, I guess what, embarked on a 40 year career in Silicon Valley and absolutely loved it. Largely software, but some time on the hardware side. Started out in networking, but largely software. And then, you know, four years ago transitioned to my next chapter, which is the corporate board director. And again, focused on technology software and cybersecurity boards. >> For the folks watching, we'll cut through another segment we can probably do about your operating career, but you rose through the ranks and became a senior operating executive at the biggest companies in the world. Hewlett Packard Enterprise, Hewlett Packard Enterprise and others. Very great career, okay. And so now you're kind of like, put that on pause, and you're moving on to the next chapter, which is being a board director. What inspired you to be a board director for multiple public companies and multiple private companies? Well, how many companies are you on? But what's the inspiration? What's the inspiration? First tell me how many board ships you're on, board seats you're on, and then what inspired you to become a board director? >> Yeah, so I'm on three public, and you are limited in terms of the number of publics that you can do to four. So I'm on three public, and I'm on four private from a tech perspective. And those range from, you know, a $4 billion in revenue public company down to a 35 person private company. So I've got the whole range. >> So you're like freelancing, I mean, what is it like? It's a full-time job, obviously. It's a lot of work involved. >> Yeah, yeah, it's. >> John: Why are you doing it? >> Well, you know, so I retired from being an operating executive after 37 years. And, but I loved, I mean, it's tough, right? It's tough these days, particularly with all the pressures out there in the market, not to mention the pandemic, et cetera. But I loved it. I loved working. I loved having a career, and I was ready to back off on, I would say the stresses of quarterly results and the stresses of international travel. You have so much of it. But I wasn't ready to back off from being involved and engaged and continuing to learn new things. I think this is why you come to tech, and for me, why I went to the valley to begin with was really that energy and that excitement, and it's like it's constantly reinventing itself. And I felt like that wasn't over for me. And I thought because I hadn't done boards before I retired from operating roles, I thought, you know, that would fill the bill. And it's honestly, it has exceeded expectations. >> In a good way. You feel good about where you're at and. >> Yeah. >> What you went in, what was the expectation going in and what surprised you? And were there people along the way that kind of gave you some pointers or don't do this, stay away from this. Take us through your experiences. >> Yeah, honestly, there is an amazing network of technology board directors, you know, in the US and specifically in the Valley. And we are all incredibly supportive. We have groups where we get together as board directors, and we talk about topics, and we share best practices and stories, and so I underestimated that, right? I thought I was going to, I thought I was going to enter this chapter where I would be largely giving back after 37 years. You've learned a little bit, right? What I underestimated was just the power of continuing to learn and being surrounded by so many amazing people. When, you know, when you do, you know, multiple boards, your learnings are just multiplied, right? Because you see not just one model, but you see many models. You see not just one problem, but many problems. Not just one opportunity, but many opportunities. And I underestimated how great that would be for me from a learning perspective and then your ability to share from one board to the other board because all of my boards are companies who are also quite close to each other, the executives collaborate. So that has turned out to be really exciting for me. >> So you had the stressful job. You rose to the top of the ranks, quarterly shot clock earnings, and it's hard charging. It's like, it's like, you know, being an athlete, as we say tech athlete. You're a tech athlete. Now you're taking that to the next level, which is now you're juggling multiple operational kind of things, but not with super pressure. But there's still a lot of responsibility. I know there's one board, you got compensation committee, I mean there's work involved. It's not like you're clipping coupons and having pizza. >> Yeah, no, it's real work. Believe me, it's real work. But I don't know how long it took me to not, to stop waking up and looking at my phone and thinking somebody was going to be dropping their forecast, right? Just that pressure of the number, and as a board member, obviously you are there to support and help guide the company and you feel, you know, you feel the pressure and the responsibility of what that role entails, but it's not the same as the frontline pressure every quarter. It's different. And so I did the first type. I loved it, you know. I'm loving this second type. >> You know, the retirement, it's always a cliche these days, but it's not really like what people think it is. It's not like getting a boat, going fishing or whatever. It's doing whatever you want to do, that's what retirement is. And you've chose to stay active. Your brain's being tested, and you're working it, having fun without all the stress. But it's enough, it's like going the gym. You're not hardcore workout, but you're working out with the brain. >> Yeah, no, for sure. It's just a different, it's just a different model. But the, you know, the level of conversations, the level of decisions, all of that is quite high. Which again, I like, yeah. >> Again, you really can't talk about some of the fun questions I want to ask, like what's the valuations like? How's the market, your headwinds? Is there tailwinds? >> Yes, yes, yes. It's an amazing, it's an amazing market right now with, as you know, counter indicators everywhere, right? Something's up, something's down, you know. Consumer spending's up, therefore interest rates go up and, you know, employment's down. And so or unemployment's down. And so it's hard. Actually, I really empathize with, you know, the, and have a great deal of respect for the CEOs and leadership teams of my board companies because, you know, I kind of retired from operating role, and then everybody else had to deal with running a company during a pandemic and then running a company through the great resignation, and then running a company through a downturn. You know, those are all tough things, and I have a ton of respect for any operating executive who's navigating through this and leading a company right now. >> I'd love to get your take on the board conversations at the end if we have more time, what the mood is, but I want to ask you about one more thing real quick before we go to the next topic is you're a retired operating executive. You have multiple boards, so you've got your hands full. I noticed there's a lot of amazing leaders, other female tech athletes joining boards, but they also have full-time jobs. >> Yeah. >> And so what's your advice? Cause I know there's a lot of networking, a lot of sharing going on. There's kind of a balance between how much you can contribute on the board versus doing the day job, but there's a real need for more women on boards, so yet there's a lot going on boards. What's the current state of the union if you will, state of the market relative to people in their careers and the stresses? >> Yeah. >> Cause you left one and jumped in all in there. >> Yeah. >> Some can't do that. They can't be on five boards, but they're on a few. What's the? >> Well, and you know, and if you're an operating executive, you wouldn't be on five boards, right? You would be on one or two. And so I spend a lot of time now bringing along the next wave of women and helping them both in their career but also to get a seat at the table on a board. And I'm very vocal about telling people not to do it the way I do it. There's no reason for it to be sequential. You can, you know, I thought I was so busy and was traveling all the time, and yes, all of that was true, but, and maybe I should say, you know, you can still fit in a board. And so, and what I see now is that your learnings are so exponential with outside perspective that I believe I would've been an even better operating executive had I done it earlier. I know I would've been an even better operating executive had I done it earlier. And so my advice is don't do it the way I did it. You know, it's worked out fine for me, but hindsight's 2020, I would. >> If you can go back and do a mulligan or a redo, what would you do? >> Yeah, I would get on a board earlier, full stop, yeah. >> Board, singular, plural? >> Well, I really, I don't think as an operating executive you can do, you could do one, maybe two. I wouldn't go beyond that, and I think that's fine. >> Yeah, totally makes sense. Okay, I got to ask you about your career. I know technical, you came in at that time in the market, I remember when I broke into the business, very male dominated, and then now it's much better. When you went through the ranks as a technical person, I know you had some blockers and definitely some, probably some people like, well, you know. We've seen that. How did you handle that? What were some of the key pivot points in your journey? And we've had a lot of women tell their stories here on theCUBE, candidly, like, hey, I was going to tell that professor, I'm going to sit in the front row. I'm going to, I'm getting two degrees, you know, robotics and aerospace. So, but they were challenged, even with the aspiration to do tech. I'm not saying that was something that you had, but like have you had experience like that, that you overcome? What were those key points and how did you handle them and how does that help people today? >> Yeah, you know, I have to say, you know, and not discounting that obviously this has been a journey for women, and there are a lot of things to overcome both in the workforce and also just balancing life honestly. And they're all real. There's also a story of incredible support, and you know, I'm the type of person where if somebody blocked me or didn't like me, I tended to just, you know, think it was me and like work harder and get around them, and I'm sure that some of that was potentially gender related. I didn't interpret it that way at the time. And I was lucky to have amazing mentors, many, many, many of whom were men, you know, because they were in the positions of power, and they made a huge difference on my career, huge. And I also had amazing female mentors, Meg Whitman, Ann Livermore at HPE, who you know well. So I had both, but you know, when I look back on the people who made a difference, there are as many men on the list as there are women. >> Yeah, and that's a learning there. Create those coalitions, not just one or the other. >> Yeah, yeah, yeah, absolutely. >> Well, I got to ask you about the, well, you brought up the pandemic. This has come up on some interviews this year, a little bit last year on the International Women's Day, but this year it's resonating, and I would never ask in an interview. I saw an interview once where a host asked a woman, how do you balance it all? And I was just like, no one asked men that. And so it's like, but with remote work, it's come up now the word empathy around people knowing each other's personal situation. In other words, when remote work happened, everybody went home. So we all got a glimpse of the backdrop. You got, you can see what their personal life was on Facebook. We were just commenting before we came on camera about that. So remote work really kind of opened up this personal side of everybody, men and women. >> Yeah. >> So I think this brings this new empathy kind of vibe or authentic self people call it. Is remote work an opportunity or a threat for advancement of women in tech? >> It's a much debated topic. I look at it as an opportunity for many of the reasons that you just said. First of all, let me say that when I was an operating executive and would try to create an environment on my team that was family supportive, I would do that equally for young or, you know, early to mid-career women as I did for early to mid-career men. And the reason is I needed those men, you know, chances are they had a working spouse at home, right? I needed them to be able to share the load. It's just as important to the women that companies give, you know, the partner, male or female, the partner support and the ability to share the love, right? So to me it's not just a woman thing. It's women and men, and I always tried to create the environment where it was okay to go to your soccer game. I knew you would be online later in the evening when the kids were in bed, and that was fine. And I think the pandemic has democratized that and made that, you know, made that kind of an everyday occurrence. >> Yeah the baby walks in. They're in the zoom call. The dog comes in. The leaf blower going on the outside the window. I've seen it all on theCUBE. >> Yeah, and people don't try to pretend anymore that like, you know, the house is clean, the dog's behaved, you know, I mean it's just, it's just real, and it's authentic, and I think that's healthy. >> Yeah. >> I do, you know, I also love, I also love the office, and you know, I've got a 31 year old and a soon to be 27 year old daughter, two daughters. And you know, they love going into the office, and I think about when I was their age, how just charged up I would get from being in the office. I also see how great it is for them to have a couple of days a week at home because you can get a few things done in between Zoom calls that you don't have to end up piling onto the weekend, and, you know, so I think it's a really healthy, I think it's a really healthy mix now. Most tech companies are not mandating five days in. Most tech companies are at two to three days in. I think that's a, I think that's a really good combination. >> It's interesting how people are changing their culture to get together more as groups and even events. I mean, while I got you, I might as well ask you, what's the board conversations around, you know, the old conferences? You know, before the pandemic, every company had like a user conference. Right, now it's like, well, do we really need to have that? Maybe we do smaller, and we do digital. Have you seen how companies are handling the in-person? Because there's where the relationships are really formed face-to-face, but not everyone's going to be going. But now certain it's clearly back to face-to-face. We're seeing that with theCUBE as you know. >> Yeah, yeah. >> But the numbers aren't coming back, and the numbers aren't that high, but the stakeholders. >> Yeah. >> And the numbers are actually higher if you count digital. >> Yeah, absolutely. But you know, also on digital there's fatigue from 100% digital, right? It's a hybrid. People don't want to be 100% digital anymore, but they also don't want to go back to the days when everybody got on a plane for every meeting, every call, every sales call. You know, I'm seeing a mix on user conferences. I would say two-thirds of my companies are back, but not at the expense level that they were on user conferences. We spend a lot of time getting updates on, cause nobody has put, interestingly enough, nobody has put T&E, travel and expense back to pre-pandemic levels. Nobody, so everybody's pulled back on number of trips. You know, marketing events are being very scrutinized, but I think very effective. We're doing a lot of, and, you know, these were part of the old model as well, like some things, some things just recycle, but you know, there's a lot of CIO and customer round tables in regional cities. You know, those are quite effective right now because people want some face-to-face, but they don't necessarily want to get on a plane and go to Las Vegas in order to do it. I mean, some of them are, you know, there are a lot of things back in Las Vegas. >> And think about the meetings that when you were an operating executive. You got to go to the sales kickoff, you got to go to this, go to that. There were mandatory face-to-faces that you had to go to, but there was a lot of travel that you probably could have done on Zoom. >> Oh, a lot, I mean. >> And then the productivity to the family impact too. Again, think about again, we're talking about the family and people's personal lives, right? So, you know, got to meet a customer. All right. Salesperson wants you to get in front of a customer, got to fly to New York, take a red eye, come on back. Like, I mean, that's gone. >> Yeah, and oh, by the way, the customer doesn't necessarily want to be in the office that day, so, you know, they may or may not be happy about that. So again, it's and not or, right? It's a mix. And I think it's great to see people back to some face-to-face. It's great to see marketing and events back to some face-to-face. It's also great to see that it hasn't gone back to the level it was. I think that's a really healthy dynamic. >> Well, I'll tell you that from our experience while we're on the topic, we'll move back to the International Women's Day is that the productivity of digital, this program we're doing is going to be streamed. We couldn't do this face-to-face because we had to have everyone fly to an event. We're going to do hundreds of stories that we couldn't have done. We're doing it remote. Because it's better to get the content than not have it. I mean it's offline, so, but it's not about getting people to the event and watch the screen for seven hours. It's pick your interview, and then engage. >> Yeah. >> So it's self-service. So we're seeing a lot, the new user experience kind of direct to consumer, and so I think there will be an, I think there's going to be a digital first class citizen with events, so that that matches up with the kind of experience, but the offline version. Face-to-face optimized for relationships, and that's where the recruiting gets done. That's where, you know, people can build these relationships with each other. >> Yeah, and it can be asynchronous. I think that's a real value proposition. It's a great point. >> Okay, I want to get, I want to get into the technology side of the education and re-skilling and those things. I remember in the 80s, computer science was software engineering. You learned like nine languages. You took some double E courses, one or two, and all the other kind of gut classes in school. Engineering, you had the four class disciplines and some offshoots of specialization. Now it's incredible the diversity of tracks in all engineering programs and computer science and outside of those departments. >> Yeah. >> Can you speak to the importance of STEM and the diversity in the technology industry and how this brings opportunity to lower the bar to get in and how people can stay in and grow and keep leveling up? >> Yeah, well look, we're constantly working on how to, how to help the incoming funnel. But then, you know, at a university level, I'm on the foundation board of Kansas State where I got my engineering degree. I was also Chairman of the National Action Council for Minorities in Engineering, which was all about diversity in STEM and how do you keep that pipeline going because honestly the US needs more tech resources than we have. And if you don't tap into the diversity of our entire workforce, we won't be able to fill that need. And so we focused a lot on both the funnel, right, that starts at the middle school level, particularly for girls, getting them in, you know, the situation of hands-on comfort level with coding, with robot building, you know, whatever gives them that confidence. And then keeping that going all the way into, you know, university program, and making sure that they don't attrit out, right? And so there's a number of initiatives, whether it's mentoring and support groups and financial aid to make sure that underrepresented minorities, women and other minorities, you know, get through the funnel and stay, you know, stay in. >> Got it. Now let me ask you, you said, I have two daughters. You have a family of girls too. Is there a vibe difference between the new generation and what's the trends that you're seeing in this next early wave? I mean, not maybe, I don't know how this is in middle school, but like as people start getting into their adult lives, college and beyond what's the current point of view, posture, makeup of the talent coming in? >> Yeah, yeah. >> Certain orientations, do you see any patterns? What's your observation? >> Yeah, it's interesting. So if I look at electrical engineering, my major, it's, and if I look at Kansas State, which spends a lot of time on this, and I think does a great job, but the diversity of that as a major has not changed dramatically since I was there in the early 80s. Where it has changed very significantly is computer science. There are many, many university and college programs around the country where, you know, it's 50/50 in computer science from a gender mix perspective, which is huge progress. Huge progress. And so, and to me that's, you know, I think CS is a fantastic degree for tech, regardless of what function you actually end up doing in these companies. I mean, I was an electrical engineer. I never did core electrical engineering work. I went right into sales and marketing and general management roles. So I think, I think a bunch of, you know, diverse CS graduates is a really, really good sign. And you know, we need to continue to push on that, but progress has been made. I think the, you know, it kind of goes back to the thing we were just talking about, which is the attrition of those, let's just talk about women, right? The attrition of those women once they got past early career and into mid-career then was a concern, right? And that goes back to, you know, just the inability to, you know, get it all done. And that I am hopeful is going to be better served now. >> Well, Sue, it's great to have you on. I know you're super busy. I appreciate you taking the time and contributing to our program on corporate board membership and some of your story and observations and opinions and analysis. Always great to have you and call you a contributor for theCUBE. You can jump on on one more board, be one of our board contributors for our analysts. (Sue laughing) >> I'm at capacity. (both laughing) >> Final, final word. What's the big seat at the table issue that's going well and areas that need to be improved? >> So I'll speak for my boards because they have made great progress in efficiency. You know, obviously with interest rates going up and the mix between growth and profitability changing in terms of what investors are looking for. Many, many companies have had to do a hard pivot from grow at all costs to healthy balance of growth and profit. And I'm very pleased with how my companies have made that pivot. And I think that is going to make much better companies as a result. I think diversity is something that has not been solved at the corporate level, and we need to keep working it. >> Awesome. Thank you for coming on theCUBE. CUBE alumni now contributor, on multiple boards, full-time job. Love the new challenge and chapter you're on, Sue. We'll be following, and we'll check in for more updates. And thank you for being a contributor on this program this year and this episode. We're going to be doing more of these quarterly, so we're going to move beyond once a year. >> That's great. (cross talking) It's always good to see you, John. >> Thank you. >> Thanks very much. >> Okay. >> Sue: Talk to you later. >> This is theCUBE coverage of IWD, International Women's Day 2023. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> Hello, everyone. Welcome to theCUBE's coverage of Compute Engineered For Your Hybrid World, sponsored by HPE and Intel. I'm John Furrier, host of theCUBE. I'm pleased to be joined by Krista Satterthwaite, SVP and general manager for HPE Mainstream Compute, and Lisa Spelman, corporate vice president, and general manager of Intel Xeon Products, here to discuss the major announcement. Thanks for joining us today. Thanks for coming on theCUBE. >> Thanks for having us. >> Great to be here. >> Great to see you guys. And exciting announcement. Krista, Compute continues to evolve to meet the challenges of businesses. We're seeing more and more high performance, more Compute, I mean, it's getting more Compute every day. You guys officially announced this next generation of ProLiant Gen11s in November. Can you share and talk about what this means? >> Yeah, so first of all, thanks so much for having me. I'm really excited about this announcement. And yeah, in November we announced our HPE ProLiant NextGen, and it really was about one thing. It's about engineering Compute for customers' hybrid world. And we have three different design principles when we designed this generation. First is intuitive cloud operating experience, and that's with our HPE GreenLake for Compute Ops Management. And that's all about management that is simple, unified, and automated. So it's all about seeing everything from one council. So you have a customer that's using this, and they were so surprised at how much they could see, and they were excited because they had servers in multiple locations. This was a hotel, so they had servers everywhere, and they can now see all their different firmware levels. And with that type of visibility, they thought their planning was going to be much, much easier. And then when it comes to updates, they're much quicker and much easier, so it's an exciting thing, whether you have servers just in the data center, or you have them distributed, you could see and do more than you ever could before with HPE GreenLake for Compute Ops Management. So that's number one. Number two is trusted security by design. Now, when we launched our HPE ProLiant Gen10 servers years ago, we launched groundbreaking innovative security features, and we haven't stopped, we've continued to enhance that every since then. And this generation's no exception. So we have new innovations around security. Security is a huge focus area for us, and so we're excited about delivering those. And then lastly, performance for every workload. We have a huge increase in performance with HPE ProLiant Gen11, and we have customers that are clamoring for this additional performance right now. And what's great about this is that, it doesn't matter where the bottleneck is, whether it's CPU, memory or IO, we have advancements across the board that are going to make real differences in what customers are going to be able to get out of their workloads. And then we have customers that are trying to build headroom in. So even if they don't need a today, what they put in their environment today, they know needs to last and need to be built for the future. >> That's awesome. Thanks for the recap. And that's great news for folks looking to power those workloads, more and more optimizations needed. I got to ask though, how is what you guys are announcing today, meeting these customer needs for the future, and what are your customers looking for and what are HPE and Intel announcing today? >> Yeah, so customers are doing more than ever before with their servers. So they're really pushing things to the max. I'll give you an example. There's a retail customer that is waiting to get their hands on our ProLiant Gen11 servers, because they want to do video streaming in every one of their retail stores and what they're building, when they're building what they need, we started talking to 'em about what their needs were today, and they were like, "Forget about what my needs are today. We're buying for headroom. We don't want to touch these servers for a while." So they're maxing things out, because they know the needs are coming. And so what you'll see with this generation is that we've built all of that in so that customers can deploy with confidence and know they have the headroom for all the things they want to do. The applications that we see and what people are trying to do with their servers is light years different than the last big announcement we had, which was our ProLiant Gen10 servers. People are trying to do more than ever before and they're trying to do that at the Edge as well as as the data center. So I'll tell you a little bit about the servers we have. So in partnership with Intel, we're really excited to announce a new batch of servers. And these servers feature the 4th Gen Intel Xeon scalable processors, bringing a lot more performance and efficiency. And I'll talk about the servers, one, the first one is a HPE ProLiant DL320 Gen11. Now, I told you about that retail customer that's trying to do video streaming in their stores. This is the server they were looking at. This server is a new server, we didn't have a Gen10 or a Gen10+ version of the server. This is a new server and it's optimized for Edge use cases. It's a rack-based server and it's very, very flexible. So different types of storage, different types of GPU configurations, really designed to take care of many, many use cases at the Edge and doing more at the Edge than ever before. So I mentioned video streaming, but also VDI and analytics at the Edge. The next two servers are some of our most popular servers, our HPE ProLiant DL360 Gen11, and that's our density-optimized server for enterprise. And that is getting an upgrade across the board as well, big, big improvements in terms of performance, and expansion. And for those customers that need even more expansion when it comes to, let's say, storage or accelerators then the DL 380 Gen11 is a server that's new as well. And that's really for folks that need more expandability than the DL360, which is a one use server. And then lastly, our ML350, which is a tower server. These tower servers are typically used at remote sites, branch offices and this particular server holds a world record for energy efficiency for tower servers. So those are some of the servers we have today that we're announcing. I also want to talk a little bit about our Cray portfolio. So we're announcing two new servers with our HPE Cray portfolio. And what's great about this is that these servers make super computing more accessible to more enterprise customers. These servers are going to be smaller, they're going to come in at lower price points, and deliver tremendous energy efficiency. So these are the Cray XD servers, and there's more servers to come, but these are the ones that we're announcing with this first iteration. >> Great stuff. I can talk about servers all day long, I love server innovation. It's been following for many, many years, and you guys know. Lisa, we'll bring you in. Servers have been powered by Intel Xeon, we've been talking a lot about the scalable processors. This is your 4th Gen, they're in Gen11 and you're at 4th Gen. Krista mentioned this generation's about Security Edge, which is essentially becoming like a data center model now, the Edges are exploding. What are some of the design principles that went into the 4th Gen this time around the scalable processor? Can you share the Intel role here? >> Sure. I love what Krista said about headroom. If there's anything we've learned in these past few years, it's that you can plan for today, and you can even plan for tomorrow, but your tomorrow might look a lot different than what you thought it was going to. So to meet these business challenges, as we think about the underlying processor that powers all that amazing server lineup that Krista just went through, we are really looking at delivering that increased performance, the power efficient compute and then strong security. And of course, attention to the overall operating cost of the customer environment. Intel's focused on a very workload-first approach to solving our customers' real problems. So this is the applications that they're running every day to drive their digital transformation, and we really like to focus our innovation, and leadership for those highest value, and also the highest growth workloads. Some of those that we've uniquely focused on in 4th Gen Xeon, our artificial intelligence, high performance computing, network, storage, and as well as the deployments, like you were mentioning, ranging from the cloud all the way out to the Edge. And those are all satisfied by 4th Gen Xeon scalable. So our strategy for architecting is based off of all of that. And in addition to doing things like adding core count, improving the platform, updating the memory and the IO, all those standard things that you do, we've invested deeply in delivering the industry's CPU with the most built-in accelerators. And I'll just give an example, in artificial intelligence with built-in AMX acceleration, plus the framework optimizations, customers can see a 10X performance improvement gen over gen, that's on both training and inference. So it further cements Xeon as the world's foundation for inference, and it now delivers performance equivalent of a modern GPU, but all within your CPU. The flexibility that, that opens up for customers is tremendous and it's so many new ways to utilize their infrastructure. And like Krista said, I just want to say that, that best-in-class security, and security solutions are an absolute requirement. We believe that starts at the hardware level, and we continue to invest in our security features with that full ecosystem support so that our customers, like HPE, can deliver that full stacked solution to really deliver on that promise. >> I love that scalable processor messaging too around the silicon and all those advanced features, the accelerators. AI's certainly seeing a lot of that in demand now. Krista, similar question to you on your end. How do you guys look at these, your core design principles around the ProLiant Gen11, and how that helps solve the challenges for your customers that are living in this hybrid world today? >> Yeah, so we see how fast things are changing and we kept that in mind when we decided to design this generation. We talked all already about distributed environments. We see the intensity of the requirements that are at the Edge, and that's part of what we're trying to address with the new platform that I mentioned. It's also part of what we're trying to address with our management, making sure that people can manage no matter where a server is and get a great experience. The other thing we're realizing when it comes to what's happening is customers are looking at how they operate. Many want to buy as a service and with HPE GreenLake, we see that becoming more and more popular. With HPE GreenLake, we can offer that to customers, which is really helpful, especially when they're trying to get new technology like this. Sometimes they don't have it in the budget. With something like HP GreenLake, there's no upfront costs so they can enjoy this technology without having to come up with a big capital outlay for it. So that's great. Another one is around, I liked what Lisa said about security starting at the hardware. And that's exactly, the foundation has to be secure, or you're starting at the wrong place. So that's also something that we feel like we've advanced this time around. This secure root of trust that we started in Gen10, we've extended that to additional partners, so we're excited about that as well. >> That's great, Krista. We're seeing and hearing a lot about customers challenges at the Edge. Lisa, I want to bring you back in on this one. What are the needs that you see at the Edge from an Intel perspective? How is Intel addressing the Edge? >> Yeah, thanks, John. You know, one of the best things about Xeon is that it can span workloads and environments all the way from the Edge back to the core data center all within the same software environment. Customers really love that portability. For the Edge, we have seen an explosion of use cases coming from all industries and I think Krista would say the same. Where we're focused on delivering is that performant-enough compute that can fit into a constrained environment, and those constraints can be physical space, they can be the thermal environment. The Network Edge has been a big focus for us. Not only adding features and integrating acceleration, but investing deeply in that software environment so that more and more critical applications can be ported to Xeon and HPE industry standard servers versus requiring expensive, proprietary systems that were quite frankly not designed for this explosion of use cases that we're seeing. Across a variety of Edge to cloud use cases, we have identified ways to provide step function improvements in both performance and that power efficiency. For example, in this generation, we're delivering an up to 2.9X average improvement in performance per watt versus not using accelerators, and up to 70 watt power savings per CPU opportunity with some unique power management features, and improve total cost of ownership, and just overall power- >> What's the closing thoughts? What should people take away from this announcement around scalable processors, 4th Gen Intel, and then Gen11 ProLiant? What's the walkaway? What's the main super thought here? >> So I can go first. I think the main thought is that, obviously, we have partnered with Intel for many, many years. We continue to partner this generation with years in the making. In fact, we've been working on this for years, so we're both very excited that it's finally here. But we're laser focused on making sure that customers get the most out of their workloads, the most out of their infrastructure, and that they can meet those challenges that people are throwing at 'em. I think IT is under more pressure than ever before and the demands are there. They're critical to the business success with digital transformation and our job is to make sure they have everything they need, and they could do and meet the business needs as they come at 'em. >> Lisa, your thoughts on this reflection point we're in right now? >> Well, I agree with everything that Krista said. It's just a really exciting time right now. There's a ton of challenges in front of us, but the opportunity to bring technology solutions to our customers' digital transformation is tremendous right now. I think I would also like our customers to take away that between the work that Intel and HPE have done together for generations, they have a community that they can trust. We are committed to delivering customer-led solutions that do solve these business transformation challenges that we know are in front of everyone, and we're pretty excited for this launch. >> Yeah, I'm super enthusiastic right now. I think you guys are on the right track. This title Compute Engineered for Hybrid World really kind of highlights the word, "Engineered." You're starting to see this distributed computing architecture take shape with the Edge. Cloud on-premise computing is everywhere. This is real relevant to your customers, and it's a great announcement. Thanks for taking the time and joining us today. >> Thank you. >> Yeah, thank you. >> This is the first episode of theCUBE's coverage of Compute Engineered For Your Hybrid World. Please continue to check out thecube.net, our site, for the future episodes where we'll discuss how to build high performance AI applications, transforming compute management experiences, and accelerating VDI at the Edge. Also, to learn more about the new HPE ProLiant servers with the 4th Gen Intel Xeon processors, you can go to hpe.com. And check out the URL below, click on it. I'm John Furrier at theCUBE. You're watching theCUBE, the leader in high tech, enterprise coverage. (bright music)