>>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great to see you, >>Dave. All right, guys. Thank you so much. So I'll tell you, we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done essentially programmed a robot to do something. It did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfasts. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives very important business outcomes. First of all, falling, right? If you look at the cost of quality, even despite decades of, of, uh, companies and manufacturers moving to improve while its quality prompts still account to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. Plant downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just narrowly to implement technology. We're doing it to move these from adverse, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle with what like, right. But so this is actually the business that cloud areas is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics, life something, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking that time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right? And that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you bring these datasets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one histories that say that you can build out those machine learning models I talked to you about earlier. >>So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And then you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. And once you understand that you can actually then build out those models, you deploy the models out to the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, right? Let's schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that a piece of equipment fails and allows us to be very, very proactive. >>So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different, um, manufacturers around the world. I want to just highlight one of them. Cause I thought it's really interesting. This company is bought for Russia. And for SIA for ACA is the, um, is the, is the, um, the, uh, a supplier associated with out of France. They are huge, right? This is a multi-national automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. Um, I mean at once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? >>To be able to just monitor the data from coming in, you know, monitor the process. That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things, just start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections for you. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go, then you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of where the data is, you've got to be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit? >>No, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES system, Freightos your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right? So, and it's uncertain, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own. And for a long time, the silos, the silos, a bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. This is Chris now is getting, you know, instrumented and captured. Uh, and so you've got that, that cultural challenge and, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a great, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about this, a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards the internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I'm got warranty plans in the, in the field, right? So I'm starting to see warranty claims kicked off on them. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots I've got, I've got warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of a car. So, and that, again, also not machine learning is simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day so that you could take corrective actions, but then you get into a whole slew of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with monitoring, get a lot of value, start, then bring together more diverse datasets to do things like connect the.analytics then all and all the way then to, to, to the more advanced machine learning use cases this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. I kind of, um, goes back to one of the things I alluded a little bit about earlier. We've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to get to practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to implement those types of, um, industry 4.0, uh, analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, uh, barrier that we've always had and, and bring together those data sets that really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to Lee lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry. Manufacturing in >>Factor space. Yeah, I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and it had become ubiquitous that number one, we can w we're finally been able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, um, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book, bullet, uh, GP, you know, GPU level, machine learning, all that, those models, and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processes are getting much smarter, uh, very much more quickly. >>Yep. We've got a lot of data and we have way lower costs, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined. Uh, thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

(pulsating electronic music) >> Welcome everybody, this is theCube, I'm Paul Gillin. Physical security and cybersecurity have traditionally been sort of isolated worlds, they didn't talk to each other. But in the age of the Internet of Things we now have unprecedented opportunities to unite these two traditionally separate areas. Armored Things is a startup out of Boston and is doing some very interesting work in using intelligent devices to make decisions and to intuit patterns in crowd behavior which has applications in cybersecurity, crowd management, traffic management, a lot of different potential uses of this technology. With me are Julie Johnson the co-founder and President of Armored Things, and Chris Lord, the Chief Technology Officer, Welcome. >> Thank you. >> Why don't you describe in a nutshell, let's start out, what you do Julie. >> Great, Armored things is building software to do next generation incident response. We're using the IOT devices and their data to power decisions across large environments used for safety. So for example the data that we're collecting can be used to get better situational awareness within seconds and drive incident response in seconds instead tens of minutes, which is the state of the art today. >> And so it's sounds like, is security the primary target area or are there others? >> That's right, we sit at the intersection of physical and cybersecurity. This information can also be used to drive additional value over time but right now we're really focused on achieving that mission, using these devices, this technology to improve both the physical and cyber realms for Internet of Things. >> Chris why don't you give us an example of how your technology might be applied? >> Sure, so a very common one is, you know active shooter. People are very concerned about active shooter, and so how can you leverage all the data that you have across different devices, different systems that you have out there, in order to understand what happened, and get people the right information at the right time. A more commonplace example might be something like a protest formation. So if you look at a university campus where you might have a controversial group meeting on campus and you need to get early warning when there's a protest forming on the other side. Our technology will allow you to see that before it's gotten to a critical proportion or before it's marching down the street. >> So why don't you take a deeper dive and talk about what, how are you federating these devices? How are you using these multiple devices together? >> Well that's exactly what we are. So we're a data analytics layer across all the silos of data that you already have in your environment. So as you look around you might have motion sensors in your environment, you might have access control systems in your environment, you have wireless infrastructure in your environment, all these things are used for specific purposes now but nothings really trying to correlate and connect the data across all of them. So Armored Things builds a layer across all of them, brings that data together to give you better understanding of what's going on in your environment, people and your physical space. >> Julie talk about how the company came about, what are the origins? >> Sure, so I started working with Charles Curran our CEO about two years ago at Qualcomm. We were really focused on understanding the security portion of the IOT layer and how to manage these things in enterprise. So if you're familiar with IOT in the household there's been a lot of proliferation around turning your lights on, understanding who's at your front door, but in enterprise it's been much slower to adopt. Fundamentally we believe that part of that was because management took a lot of time. Every time you provisioned a device it took a number of minutes and because there was an intrinsic lack of security on each of the devices. So we went around and started talking to different potential customer groups about what it would look like to bring more IOT into their environments. And we really got pulled into universities, and large sporting and entertainment venues, who we're still working with as our primary customers today. Because they saw a desperate need for IOT, not only to save time on managing these devices, and to make sure that they're secure in their environments, but also to use them for physical security. So now that we've spent, you know $15 million in selling IP video cameras, or a few million dollars in selling access control systems, how do we actually elevate their use from what they were initially intended for. That spend has a secondary use when it comes to physical security. That ability to, you know quickly get cameras on the scene of an incident. That ability to harness data coming off of motion sensors or environmental sensors. How do we use all of that information to drive an awareness of our environments day-to-day and then use it in critical emergencies for a better response. >> I understand you're working with some sports teams right now. Can you describe a scenario in which you might be able to help them manage crowds more effectively? >> So there was a great example we heard about two weeks ago from a top team, who's recently hosted some World Series events. They had a unfortunate incident where they were watching, they were hosting a watch party for the World Series in their venue during an away game, and they handed about 40,000 paper tickets out. They got a great turnout, 20,000 people came to the venue. But in the seventh inning of the game the other 20,000 people decided that they also wanted to be in the venue in order to celebrate. That was a pretty unanticipated event. Usually in the fifth or sixth inning you start to consolidate your entrances, you start to consolidate your security personnel and send them to other parts of the venue, and the net result of that was they ended up closing the doors, not allowing additional entrance in, and tweeting that there wouldn't be additional people allowed to enter. There were a lot of security issues with letting 20,000 people in, in the seventh inning, not of the least is you don't know where they're coming from, and you don't really know what their intent is in coming so late to that venue. But there's patterns in the data that we could've seen sooner. So hypothetically, understanding that a normal game day has a couple hundred people entering in the fifth, sixth, seventh innings. Seeing a significant uptick in that number of people coming into your environment should immediately say, what's unique, you know what's different about this situation? Now how do I tie in my resources, my security personnel, my responders, and just maybe notify people who are in charge of making these types of decisions, so that we're not closing the gate and tweeting out to our fans that there's no more entries. >> And getting back to the technical nuances of this situation, how might your technology detect this crowd assembling before it was even visually apparent? >> Good question, so there's many, many different things. So part of what we do is rely on diversity of data from different sources. So that might be mobile devices. That might be from wireless. That might be from cameras that you have there and doing occupancy counts on those cameras. It might be from other, you know motion sensors you have in your environment. All this data gets aggregated so that we can come up with a good understanding of population and flow within your environment. So we would have early indications and bring that awareness to people that have to respond, people who might be sitting in a network operations center, and looking at other cameras but not seeing the information. So we can bring the information right there, notify them that there's a problem forming before it's gotten to critical proportions. >> Fantastic. >> One more thought on that is there's kind of a unique advantage in data to go beyond what humans can perceive. When we're looking at these knocks, you know they have thousands of video cameras potentially united in one central screen. It takes not only having the right camera up but also noticing a degree of difference that might be quite minute, to actually see it as an anomaly in real-time. So you can imagine, you know a university campus where people are walking through the campus at a certain pace every single day. One day everyone's walking just 30% faster, not running just walking, why? You know is there a suspicious package? Is there someone gathered there that you know is attracting people that they don't necessarily want to be associated with, or end up in a vulnerable position? How can we see that in the data faster than someone in the control room might notice it and alert people to respond. >> And with machine learning, of course now we have the means to do that. Chris, talk about the, it strikes me that there must be a lot of complexity involved. You've got a great diversity of devices out there you have to connect to. Every institution would have a different fabric. How are you technically pulling this all together? >> Well the nice thing about a lot of these technologies is there is standardization across many of these different types of devices, and there are, you know there are tiers of players right. And so we do have to be selective about who we integrate with. We are integrated with the top-tier players in all these categories, and we'll prioritize other integrations over time based on our customers and our market so. >> And Julie, what are your plans for deployment? What's your timeframe? >> We're looking to rollout our first generation of product in the next nine to twelve months. That really drives home at that situational awareness piece. So before we even get to building through incident response at scale, the ability to give people very specific cues during a critical emergency. How do we start with getting more information to the people who are there? So getting occupancy, flow, the dynamics of movement around a campus or a large venue. How do we start equipping the police personnel, and security personnel to make better decisions and drive value from there. >> I understand there's no shortage of demand for your solution. >> We do have some top-tier universities, and pro-sporting and entertainment venues who we're working with to build the right solution not just the solution that we think is needed, but the solution that they're telling us, "Hey we would really like to use something like this." >> I also understand you've pulled together a team, kind of a dream team, talk about some of the people that you've brought on board for this operation which few people have even heard of. >> Yeah so I think the first of those you're seeing here, so Chris joined us as co-founder and CTO and has been really an asset to this team given his background in cybersecurity from Carbon Black and before that. And you know if you want to add more to that please feel free to. >> No thanks. >> We've also brought in, I would call it two pillars of our strategy. One one the physical security side and one on the machine learning data analytics side, and those two women are Elizabeth Carter. Who came to us from Apple, where she led crisis management for the Americas. She previously worked at Chertoff Group where she sat at the intersection of physical and cybersecurity, and before that actually worked for the city of New York, where she understood weapons of mass destruction, different types of biological and chemical weapons response planning. So she's kind of the pillar of our physical security response understanding and driving product. The other woman, her name is Clare Bernard and she recently joined us from another Boston startup called Tamr where she was running product and engineering for them. Clare's background is actually in particle physics. She was BU and John's Hopkins, and happened to work with the team that discovered the God particle while she was getting her PhD. So we' think she's as smart as you can find, and is going to help us think about these data challenges, the analytics piece at a scale that, you know we think has the potential to really improve physical security and cybersecurity. I would be remiss if I didn't mention the rest of our team. Our CEO Charles comes from a background in the venture capital community and is just incredibly knowledgeable about the process of building a company from the ground up, and has many skills when it comes to recruiting as well. Really helped drive some of these hires forward and the rest of the team is the next generation of rising stars, people from Oracle, HP Vertica, other Carbon Black individuals. People who just have experience from across the board that's going to help us build the right solution. >> And you know at a time when diversity has been a major issue for tech companies, I understand your team is unusually well represented. >> I think our executive team is about 60% women, which we're very proud of. I think our team in general might actually be, >> About that too, yup. >> About 60% women, which we're also very proud of. And I'd like to say that that's organic. That we've worked with some great advisors and potential customers, and I do think that from my perspective, it's been helpful to have younger women coming in who see a path forward for senior women in executive roles in their company. I think that's something that can't be underestimated. >> Where do you stand in funding right now? >> We just closed our first institutional capital about a week and a half ago. We're still finishing the close of that round but we have a Boston based partner who's very focused on machine learning and analytics, and also has been a well recognized investor in the cyber security realm. So we're very fortunate to have this investor as our partner, and excited to keep working with them. >> Chris, as someone whose background is in cybersecurity how do you see the security landscape changing now with the IOT coming on and the possibility of really transforming the way organizations look at their physical and cybersecurity operations? >> Good question, so over time they're converging, and they're converging I think more rapidly than we expected, so now I'm going to step back a little bit and say that there's a lot of parallels. Cybersecurity I think is probably about five years ahead of physical security in terms of maturity of technology and approaches to problems. And then so what we're seeing right now, and we're part of the force behind that, is taking the learnings from cyber security and applying them to physical security right. So when we talk about situational awareness, when we talk about the data analytics that supports that, and when we talk about incident response and orchestration automation. All of those are core to taking cybersecurity and applying it to physical security. In terms of convergence, we're seeing many cases, and this is going back a number of years, where people are using cyber events to create physical problems right. Stuxnet is a classic example, but you can do the same thing by taking over something and instilling panic in a stadium, and causing you know, all sorts of grief, cyber driving physical. You can also see cases where people who are running cybersecurity operation centers want access to physical knowledge of their environment in order to do their job better. Whether it is a malicious insider that they suspect, whether it's an infection that occurs on a particular machine, being able to pull up the cameras, know who was there at the time, bringing all that information together, is again necessary in order to understand their perception of situational awareness. So two converging towards one, we're going to be building towards that goal from our perspective. >> Now the flip side of federating IOT devices is that the bad guys can do the same thing. So you potentially have a much broader attack surface. That has to be factoring into your thinking. What is the embedded security in your platform? >> So, we're not going to address fully that right now, but so we take advantage of best in breed security principles in our design both for security and for privacy. But in terms of the dependency we have on a lot of IOT devices and IOT systems, part of what helps us is diversity of data across those, and diversity of devices right. And so while you might have compromises in specific cases, the fact that you are dealing with so many, and so many different categories at the same time, allows you to maintain and fulfill your mission, and deliver what you're trying to do regardless of some of those individual compromises. We're also in a unique vantage point where we can actually see the operational integrity of what's going on. So when you look across all those different categories and you look at the data that we're collecting, whether it's malicious or not, we're able to identify a failure, and bring that to the attention of the people who are dependent on those systems. So we could be an early morning to cyber events, malicious or not. >> Julie, entrepreneurs love to dream. I'm sure you are thinking big, beyond the immediate cybersecurity applications. Where could Armored Things eventually go? >> That's a great question. The dream is that we become not only the dominant solution for physical and cyber security for schools and large venues. But we bring our solution into K, 12 where some of this is desperately needed. That's kind of the mission orientation of our team. How do we start to drive value in a way that we can get to every school in the country sooner. In the longer term though, I think there's a lot of opportunities with IOT and we're still kind of at the tip of the iceberg here. We're going to see all sorts of new devices come online over the next two, five, 10 years. The growth of these devices is incredible. And the question is how do we continue this challenge of solving the data at scale in a way that continues to drive value, not just for some of the first use cases, which are often around marketing, and understanding an environment in that sense, but also continuing that physical cybersecurity angle. >> Enormous potential and hope you stay based in Boston. We can use more companies like that. Chris Lord and Julie Johnson, thanks very much for joining us today on theCUbe. >> Thanks Paul. >> Thank you. >> Armored Things, keep your eye on them. You're going to be hearing a lot more about this company in the months to come. I'm Paul Gillin, this is theCube.

>> Narrator: Live, from Las Vegas, it's theCUBE, covering AWS re:Invent 2017, presented by AWS, intel, and our ecosystem of partners. Hello, everyone, welcome back to theCUBE. This is Silicon Angle's exclusive coverage with theCUBE, here at Amazon, re:Invent 2017. It's our 5th year covering Amazon's explosive growth. I'm John Furrier, the founder of Silicon Angle media. I'm here with Justin Warren, my cohost here, our next guest on set one is Tom Siebel, who is the founder and CEO of C3 IOT, industry legend, knows the software business, been around the block a few times, and now part of the new wave of innovation. Welcome to theCUBE. >> Thank you. >> I hear you just got in from San Francisco. What a world we're living in. You're at the front-end of your company that you founded and are running, an IOT big data play, doing extremely well. Even last year, the whisper in the hallway was C3 IOT is absolutely doing great, in the industrial side, certainly in the federal government side, and on commercial, congratulations! >> Thank you. >> What's the update, what's the secret formula? >> Well, we live at the convergence of elastic cloud computing, big data, AI, and IOT, and at the point where those converge, I think, is something called digital transformation, where you have these CEOs that, candidly, I think, they're concerned that companies are going through a mass-extinction event. I mean, companies are being, 52% of the Fortune 500 companies, as of 2000 are gone, right, they've disappeared and it's estimated as many 70% might disappear in the next 10 years, and we have this new species of companies with new DNA that look like Tesla and Uber, and Amazon, and, they have no drivers, no cars, and yet they own transportation, and I think that these CEOs are convinced that, unless they take advantage of this new class of technologies that they might be extinct. >> And it's certainly, we're seeing it, too, in a lot of the old guard, as Andy Jassy calls it, really talking about Oracle, IBM, and some of the other folks that are trying to do cloud, but they're winning. I gotta ask you, what's the main difference, from your perspective, that's different now that the culture of a company that's trying to transform, what's the big difference between the old way and new way now, that has to be implemented quickly, or extinction is a possibility? I mean, it's not just suppliers, it's the customers themselves. >> The customers have changed. >> What's the difference? >> So, this is my 4th decade in the information technology business and I've seen the business grow from a couple hundred billion to, say, two trillion worldwide, and I've seen it go from mainframe to mini-computers, to personal computers to the internet, all of that, and I was there when, in all of those generations of technology, when we brought those products to market, would come up in the organization, through the IT organization, to the CIO, and the CIO would say, "well, we're never gonna use a mini computer." or, "we're never gonna use relations database technology." or, "we're never gonna use a PC." And so, you'd wait for that CIO to be fired, then he'd come back two years later, right? Now, so meanwhile we build a two trillion dollar information technology business, globally. Now, what's happening in this space of big data, predictive analytics, IOT, is all of a sudden, it's the CEO at the table. CEO was never there before, and the CEO is mandating this thing called digital transformation, and he or she is appointing somebody in the person of a Chief Digital Officer, who has a mandate and basically a blank check to transform this company and get it done, and whereas it used to be the CIO would report to the CEO once a quarter at the quarterly off-site, the Chief Digital Officer reports to the CEO every week, so, and virtually everyone of our customers, CAT, John Deere, United Healthcare, you name, ENGIE, Enel, it's a CEO-driven initiative. >> You bring up a good point I wanna get your thoughts on, because the old way, and you mentioned, was IT reporting to the CIO. They ran things, they ran the business, they ran the plumbing, software was part of that, now software is the business. No one goes to the teller. The bank relationship's the software, or whatever vertical you're in there's now software, whether it's at the edge, whether it's data analytics, is the product to the consumer. So, the developer renaissance, we see software now changing, where the developer's now an influencer in this transformation. >> True. >> Not just, hey, go do it, and here's some tools, they're in part of that. Can you share your perspective on this because, if we're in a software renaissance, that means a whole new creativity's gonna unleash with software. With that role of the CDO, with the blank check, there's no dogma anymore. It's results. So, what's your perspective on this? >> Well, I think that there's enabling technologies that include the elastic cloud that include, computation and storage is basically free, right? Everything is a computer, so IOT, I used to think about IOT being devices, it's that IOT is a change in the form-factor of computers. In the future, everything's a computer, your eyeglasses, your watch, your heart monitor, your refrigerator, your pool pump, they're all computers, right, and then we have the network effect of Metcalfe's law, say we have 50 billion of theses devices fully connected and well, that's a pretty powerful network. Now, these technologies, in turn, enable AI, they enable machine learning and deep learning. Hey, that's a whole new ball game. Okay, we're able to solve classes of problems with predictive analytics and prescriptive analytics that were simply unsolvable before in history and this changes everything about the way we design products, the way we service customers, the way we manage companies. So, I think this AI thing is not to be underestimated. I think the cloud, IOT, big data, devices, those are just enablers, and I think AI is-- >> So, software and data's key, right? Data trains the AI, data is the fundamental new lifeblood. >> Big data, because now we're doing, what big data is about, people think that big data is the fact that an exabyte is more than a gigabyte, that's not it. Big data is about the fact that there is no sampling error. We have all the data. So, we used to, due to limitations to storage and processing we used to, you know, basically, take samples and infer results from those samples, and deal with it on the level of confidence error that was there. With big data, there's no sampling error. >> It's all there. >> It is a whole different game. >> We were talking before, and John, you mentioned before about the results that you need to show. Now, I know that you picked up a big new customer that I hope you can talk about publicly, which is a public-sector company, but that sounds like something where you're doing predictive maintenance for the Air Force, for the U.S. Air Force, so that's a big customer, good win there, but what is the result that they're actually getting from the use of big data and this machine learning analytics that you're doing? >> By aggregating all the telemetry and aggregating all their maintenance records, and aggregating all their pilot records, and then building machine learning class of ours, we can look at all the signals, and we can predict device failure or systems failure well in advance of failure, so the advantage is some pretty substantial percentages, say of F16s, will not deploy, of F18s will not deploy because, you know, they go to push the button and there's a system failure. Well, if we can predict system failure, I mean, the cost of maintenance goes down dramatically and, basically, it doubles the size of your fleet and, so the economic benefit is staggering. >> Tom, I gotta ask you a personal question. I mean, you've been through four decades, you're a legend in the industry, what was the itch that got you back with this company. Why did you found and run C3 IOT? What was the reason? Was it an itch you were scratching, like, damn, I want the action? I mean, what was the reason why you started the company? >> Well, I'm a computer scientist and out of graduate school, I went to work with a young entrepreneur by the name of Larry Ellison, turned out to be a pretty good idea, and then a decade later, we started Siebel Sytems, and I think, well, we did invent the CRM market and then it turned out to be a pretty good idea and I just see, at this intersection of these vectors we talked about, everything changes about computing. This has been a complete replacement market and I though, you know, there's opportunity to play a significant role in the game, and this what I do, you know. I collect talented people and try to build great companies and make customers satisfied. This is my idea of a good time. You're on the beach, you're on your board hangin' 10 on the big waves. What are the waves? We're seeing this inflection point, a lotta things comin' together, what are the waves that you're ridin' on right now? Obviously, the ones you mentioned, what's the set look like, if I can use a surfing analogy. What's coming in, what are the big waves? The two biggest ones are IOT and AI. I mean, since 2000 we've deployed 19 billion IOT sensors around the world. The next five years, we'll deploy 50 billion more. Everything will be a computer, and you connect all these things that they're all computing and apply AI, I mean we're gonna do things that were, you know, unthinkable, in terms of serving customers, building products, cost efficiencies, we're gonna revolutionize healthcare with precision health. Processes like energy extraction and power delivery will be much safer, much more reliable, much more environmentally-friendly, this is good stuff. So, what's your take on the security aspect of putting a computer in everything, because, I mean, the IT industry hasn't had a great track record of security, and now we're putting computers everywhere. As you say, they're gonna be in watches, they're gonna be in eyeglasses, what do you see as the trend in the way that security is gonna be addressed for this, computers everywhere? Well, I think that it is clearly not yet solved, okay, and it is a solvable problem. I believe that it's easier to secure data in cyber space than it is in your own data room. Maybe you could secure data in your data room when it took a forklift to move a storage device. It doesn't take a forklift anymore, right? It takes one of these little flash drives, you know, to move, to take all the data. So, I think the easiest place we can secure it is gonna be in cyber space. I think we'll use encryption, I think we'll be computing on encrypted data, and we haven't figured out algorithms to do that yet. I think blockchain will play an important role, but there's some invention that needs to happen and this is what we do. >> So, you like blockchain? >> I think blockchain plays a role in security. >> It does. So, I gotta ask you about the way, you're sinking your teeth into a new venture, exciting, it's on the cutting-edge, on the front lines of the innovation. There are a lotta other companies that are trying to retool. IBM, Microsoft, Oracle, if you were back them, probably not as exciting as what you're doing because you've got a new clean sheet of paper, but if you're Oracle, if you're Larry, and he went to be CTO, he's trying to transform, he's getting into the action, they got a lot to do there, IBM same thing, same with Microsoft, what's their strategy in your mind? If you were there, at the helm of those companies, what would you do? >> Well, number one, I would not bet against Larry. I know Larry pretty well and Larry is a formidable player in the information technology industry, and if you have to identify one of four companies that's surviving the long-run, it'll be Oracle that's in that consideration, in that set, so I think betting against Larry is a bad idea. >> He'll go to the mat big time, won't he? I mean, Jassy, there's barbs going back and forth, you gotta be careful there. >> Well, I mean, Andy Jassy is extraordinarily competent, I think, as it relates to this elastic cloud I think he's kinda got a lock on that, but, you know, IBM is hard to explain. I mean, IBM is a sad story. I think IBM is, there's some risk that IBM is the next Hewlett-Packard. I mean, they might be selling this thing off for piece parts this, you mean, if we look at the last 23 quarters, I mean, it's not good. >> And Microsoft's done a great job recently with Satya Nadella, and they're retooling fast. You can see them beavering away. >> But IBM, I mean, how do you bet against the cloud. I mean, are you kidding me? I mean, hello! IBM's a sad story. It's one of the world's great companies, it's an icon. If it fails, and companies like IBM's size do fail, I mean let's look at GE, that would be a sad state for America. >> Okay, on a more positive upbeat, what's next for you? Obviously, you're doing great, the numbers are good. Again, the rumors in the hallways we're hearing that you guys are doing great financially. Not sure if you can share any color on that, big wins, obviously, these are not little deals you're on, but what's next? What's the big innovation that you got comin' around the corner for C3 IOT. Well, so our business grew last year about 600%, this year it'll grow about 300%. We're a profitable, cash-positive business. Our average customer is, say, 20 to $200 billion business. We're engaged in very, very large transactions. In the last 18 months, we've done a lotta work in deep learning, okay. In the next 18 months, we'll do a lotta work in NLP. I think those technologies are hugely important. Technologically, this is where we'll be going. I think machine learning, traditional ML, we have that nailed, now we're exploiting deep learning in a big way using GPUs, and a lotta the work that Jensen Wang's doing at Nvidia, and now NLP, I think, is the next frontier for us. >> Final question for you, advice to other entrepreneurs. You're a serial entrepreneur. you've been very successful, inventive categories. You're looking at Amazon, how do you work with the Amazons of the world. What should entrepreneurs be thinking about in terms of how to enter the market, funding, just strategy in general. The rules have changed a little bit. What advice would you give the young entrepreneurs out there? >> Okay, become a domain expert at whatever domain you're proposing and whatever field you're gonna enter, and then surround yourself with people, whatever job they're doing, engineering, marketing, sales, F&A, who are better than you at what they do and, to the extent that I have succeeded, this is why I've succeeded. Now this might be easier for me than for others, but I try to surround myself with people who are better than me and, to the extent that I've been successful, that's why. >> We really appreciate you taking the time coming on. You're an inspiration, a serial entrepreneur, founder and CEO Tom Siebel of C3 IOT, hot company, big part of the Amazon Web Services ecosystem. Doing great stuff, again, serial entrepreneur. Great four-decade career. Thanks for coming on theCUBE, Tom Siebel. Here inside theCUBE, I'm John Furrier and Justin Warren, here in Las Vegas for AWS re:Invent. We'll be back with more live coverage after this short break. >> Thanks guys, good job.

>> 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.

>> Narrator: From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE. Covering When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back here everybody Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel. At a little event it's When IoT Met AI: The Intelligence of Things. As we hear about the Internet of Things all the time this is really about the data elements behind AI, and machine learning, and IoT. And we're going to get into it with some of the special guests here. We're excited to get the guy that's going to kick off this whole program shortly is Tom Stuermer. He is the I got to get the new title, the Global Managing Director, Ecosystem and Partnership, from Accenture. Tom, welcome-- >> Thank you, Jeff. >> And congrats on the promotion. >> Thank you. >> So IoT, AI, buzz words, a lot of stuff going on but we're really starting to see stuff begin to happen. I mean there's lots of little subtle ways that we're seeing AI work its way in to our lives, and machine learning work our way into its life, but obviously there's a much bigger wave that's about to crest here, shortly. So as you kind of look at the landscape from your point of view, you get to work with a lot of customers, you get to see this stuff implemented in industry, what's kind of your take on where we are? >> Well, I would say that we're actually very early. There are certain spaces with very well-defined parameters where AI's been implemented successfully, industrial controls on a micro level where there's a lot of well-known parameters that the systems need to operate in. And it's been very easy to be able to set those parameters up. There's been a lot of historical heuristic systems to kind of define how those work, and they're really replacing them with AI. So in the industrial spaces a lot of take up and we'll even talk a little bit later about Siemens who's really created a sort of a self-managed factory. Who's been able to take that out from a tool level, to a system level, to a factory level, to enable that to happen at those broader capabilities. I think that's one of the inflection points we're going to see in other areas where there's a lot more predictability and a lot of other IoT systems. To be able to take that kind of system level and larger scale factors of AI and enable prediction around that, like supply chains for example. So we're really not seeing a lot of that yet, but we're seeing some of the micro pieces being injected in where the danger of it going wrong is lower, because the training for those systems is very difficult. >> It's interesting, there's so much talk about the sensors, and the edge, and edge computing, and that's interesting. But as you said it's really much more of a system approach is what you need. And it's really kind of the economic boundaries of the logical system by which you're trying to make a decision in. We talk all the time, we optimizing for one wind turbine? Are you optimizing for one field that contains so many wind turbines? Are you optimizing for the entire plant? Or are you optimizing for a much bigger larger system that may or may not impact what you did on that original single turbine? So a systems approach is a really critical importance. >> It is and what we've seen is that IoT investments have trailed a lot of expectations as to when they were going to really jump in the enterprise. And what we're finding is that when we talk to our customers a lot of them are saying, look I've already got data. I've got some data. Let's say I'm a mining company and I've got equipment down in mines, I've got sensors around oxygen levels, I just don't get that much value from it. And part of the challenge is that they're looking at it from a historical data perspective. And they're saying well I can see the trajectory over time of what's happening inside of my mind. But I haven't really been able to put in prediction. I haven't been able to sort of assess when equipment might fail. And so we're seeing that when we're able to show them the ability to affect an eventual failure that might shut down revenue for a day or two when some significant equipment fails, we're able to get them to start making those investments and they're starting to see the value in those micro pockets. And so I think we're going to see it start to propagate itself through in a smaller scale, and prove itself, because there's a lot of uncertainty. There's a lot of work that's got to be done to stitch them together, and IoT infrastructure itself is already a pretty big investment as it is. >> Short that mine company, because we had Caterpillar on a couple weeks ago and you know their driving fleets of autonomous vehicles, they're talking about some of those giant mining trucks who any unscheduled downtime the economic impact is immense well beyond worrying about a driver being sick, or had a fight with his wife, or whatever reason is bringing down the productivity of those vehicles. So it's actually amazing the little pockets where people are doing it. I'm curious to get your point of view too on kind of you managed to comment the guy's like I'm not sure what the value is because the other kind of big topic that we see is when will the data and the intelligence around the data actually start to impact the balance sheet? Because data used to be kind of a pain, right? You had to store it, and keep it, and it cost money, and you had to provision servers, and storage, but really now and the future the data that you have, the algorithms you apply to it will probably be an increasing percentage of your asset value if not the primary part of you asset value, you seeing some people start to figure that out? >> Well they are. So if you look, if step back away from IoT for a minute and you look at how AI is being applied more broadly, we're finding some transformational value propositions that are delivering a lot of impacts to the bottom line. And it's anywhere from where people inside of a company interact with their customers, being able to anticipate their next move, being able to predict given these parameters of this customer what kind of customer care agent should I put on the phone with them before you even pick up the phone to anticipate some of those expectations. And we're seeing a lot of value in things like that. And so, excuse me, and so when you zoom it back in to IoT some of the challenges are that the infrastructure to implement IoT is very fragmented. There's 360 some IoT platform providers out in the world and the places where we're seeing a lot of traction in using predictive analytics and AI for IoT is really coming in the verticals like industrial equipment manufacturers where they've kind of owned the stack and they can define everything from the bottom up. And what they're actually being able to do is to start to sell product heavy equipment by the hour, by the use, because they're able to get telemeter off of that product, see what's happening, be able to see when a failure is about to come, and actually sell it as a service back to a customer and be able to predictably analyze when something fails and get spares there in time. And so those are some of the pockets where it's really far ahead because they've got a lot of vertical integration of what's happening. And I think the challenge on adoption of broader scale for companies that don't sell very expensive assets into the market is how do I as a company start to stitch my own assets that are for all kinds of different providers, and all kinds of the different companies, into a single platform? And what the focus has really been in IoT lately for the past couple of years is what infrastructure should I place to get the data? How do I provision equipment? How do I track it? How do I manage it? How do I get the data back? And I think that's necessary but completely insufficient to really get a lot of value IoT, because really all your able to do then is get data. What do you do with it? All the value is really in the data itself. And so the alternative approach a lot of companies are taking is starting to attack some of these smaller problems. And each one of them tends to have a lot of value on its own, and so they're really deploying that way. And some of them are looking for ways to let the battles of the platforms, let's at least get from 360 down to 200 so that I can make some bets. And it's actually proving to be a value, but I think that is one of the obstacles that we have to adoption. >> The other thing you mentioned interesting before we turned on the cameras is really thinking about AI as a way to adjust the way that we interact with the machines. There's two views of the machines taking over the world, is it the beautiful view, or we can freeze this up to do other things? Or certainly nobody has a job, right? The answer is probably somewhere in the middle. But clearly AI is going to change the way, and we're starting to see just the barely the beginnings with Alexa, and Siri, and Google Home, with voice interfacing and the way that we interact with these machines which is going to change dramatically with the power of, as you said, prescriptive analytics, presumptive activity, and just change that interaction from what's been a very rote, fixed, hard to change to putting as you said, some of these lighter weight, faster to move, more agile layers on the top stack which can still integrate with some of those core SAP systems, and systems of record in a completely different way. >> Exactly, you know I often use the metaphor of autonomous driving and people seem to think that that's kind of way far out there. But if you look at how driving an autonomous vehicle's so much different from driving a regular car, right? You have to worry about at the minutia of executing the driving process. You don't have to worry about throttle, break. You'd have to worry about taking a right turn on red. You'd have to worry about speeding. What you have to worry about is the more abstract concepts of source, destination, route that I might want to take. You can offload that as well. And so it changes what the person interacting with the AI system is actually able to do, and the level of cognitive capability that they're able to exercise. We're seeing similar things in medical treatment. We're using AI to do predictive analytics around injury coming off of medical equipment. It's not only starting to improve diagnoses in certain scenarios, but it's also enabling the techs and the doctors involved in the scans to think on a more abstract level about what the broader medical issues are. And so it's really changing sort of the dialogue that's happening around what's going on. And I think this is a good metaphor for us to look at when we talk about societal impacts of AI as well. Because there are some people who embrace moving forward to those higher cognitive activities and some who resist it. But I think if you look at it from a customer standpoint as well, no matter what business you're in if you're a services business, if you're a product business, the way you interact with your employees and the way you interact with your customers can fundamentally be changed with AI, because AI can enable the technology to bend it to your intentions. Someone at the call center that we talked about. I mean those are subtle activities. It's not just AI for voice recognition, but it's also using AI to alter what options are given to you, and what scenarios are going to be most beneficial. And more often than not you get it right. >> Well the other great thing about autonomous vehicles, it's just a fun topic because it's something that people can understand, and they can see, and they can touch in terms of a concept to talk about, some of these higher level concepts. But the second order impacts which most people don't even begin to think, they're like I want to drive my car is, you don't need parking lots anymore because the cars can all park off site. Just Like they do at airports today at the rental car agency. You don't need to build a crash cage anymore, because the things are not going to crash that often compared to human drivers. So how does the interior experience of a car change when you don't have to build basically a crash cage? I mean there's just so many second order impacts that people don't even really begin to think about. And we see this time and time again, we saw it with cloud innovation where it's not just is it cheaper to rent a server from Amazon than to buy one from somebody else? It's does the opportunity for innovation enable more of your people to make more contributions than they could before because they were too impatient to wait to order the server from the IT guy? So that's where I think too people so underestimate kind of the big Moore's Law my favorite, we overestimate in the short term and completely underestimate in the long term, the impacts of these things. >> It's the doubling function, exactly. >> Jeff: Yeah, absolutely. >> I mean it's hard for people, human kind is geared towards linear thinking, and so when something like Moore's Law continues to double every 18 months price performance continues to increase. Storage, compute, visualization, display. >> Networking, 5G. >> You know the sensors in MEMS, all of these things have gotten so much cheaper. It's hard for human of any intelligence to really comprehend what happens when that doubling occurs for the next 20 years. Which we're now getting on the tail end of that fact. And so those manifest themselves in ways that are a little bit unpredictable, and I think that's going to be one of our most exciting challenges over the next five years is what does an enterprise look like? What does a product look like? One of the lessons that, I spent a lot of time in race car engineering in my younger days and actually did quants and analytics, what we learned from that point is as you learned about the data you started to fundamentally change the architecture of the product. And I think that's going to be a whole new series of activities that are going to have to happen in the marketplace. Is people rethinking fundamental product. There's a great example of a company that's completely disrupted an industry. On the surface of it it's been disrupted because of the fact that they essentially disassociated the consumption from the provision of the product. And didn't have to own those assets so they could grow rapidly. But what they fundamentally did was to use AI to be able to broker when should I get more cars, where should the cars go? And because they're also we're on the forefront of being able to drive, this whole notion of consumption of cars, and getting people's conceptual mindset shifted to having owned a car to I know an Uber's going to be there. It becomes like a power outlet. I can just rely on it. And now people are actually starting to double think about should I even own a car? >> Whole different impact of the autonomous vehicles. And if I do own a car why should it be sitting in the driveway when I'm not driving it? Or I send it out to go work for me make it a performing asset. Well great conversation. You guys Accenture's in a great spot. You're always at the cutting edge. I used to tease a guy I used to work with at Accenture you've got to squeeze out all the fat in the supply chain (laughs) your RP days and again a lot of these things are people changing the lens and seeing fat and inefficiency and then attacking it in a different way whether it's Uber, Airbnb, with empty rooms in people's houses. We had Paul Doherty on at the GE Industrial Internet launch a few years back, so you guys are in a great position because you get to sit right at the forefront and help these people make those digital transformations. >> I appreciate that. >> I will tell you I mean supply chains is another one of those high level systems opportunities for AI where being able to optimize, think about it a completely automated distribution chain from factory all the way to the drone landing at your front doorstep as a consumer. That's a whole nother level of efficiency that we can't even contemplate right now. >> Don't bet against Bezos that's what I always say. All right, Tom Stuermer thanks for spending a few minutes and good luck with the keynote. >> I appreciate it Jeff. >> All right, I'm Jeff Frick you're watching theCUBE. We are at The Intelligence of Things, When IoT met AI. You're watching theCUBE. Thanks for watching. (upbeat music)

>> Male Voice: From the Fairmont Hotel in the heart of Silicon Valley, it's the Cube covering when IoT Met AI: the Intelligence of Things brought to you by Western Digital. >> Hey welcome back here everybody. Jeff Frick with the Cube. We're in downtown San Jose at the Fairmont Hotel at a small event talking about data and really in IoT and the intersection of all those things and we're excited to have a little startup boutique here and one of the startups is great enough to take the time to sit down with us. This is Shaun Moore, he's the founder and CEO of the recently renamed Trueface.ai. Shaun, welcome. >> Thank you for having me. >> So you've got a really cool company, Trueface,ai. I looked at the site. You have facial recognition software so that's cool but what I think is really more interesting is you're really doing facial recognition as a service. >> Shaun: Yes. >> And you a have a freemium model so I can go in and connect to your API and basically integrate your facial recognition software into whatever application that I built. >> Right so we were thinking about what we wanted to do in terms of pricing structure. We wanted to focus on the developer community so we wanted tinkers, people that just want to play with technology to help us improve it and then go after the kind of bigger clients and so we'll be hosting hack-a-thons. We just actually had one this past week in San Francisco. We had great feedback. We're really trying to get a base of you know, almost outsource engineers to help us improve this technology and so we have to offer it to them for free so we can see what they build from there. >> Right but you don't have an opensource component yet so you haven't gone that route? >> Not quite yet, no. >> Okay. >> We're thinking about that though. >> Okay, and still really young company, angel-funded, haven't taken it the institutional route yet. >> Right, yeah, we've been around since 2013, end of 2013, early 2014, and we were building smart home hardware so we had built the technology around originally to be a smart doorbell that used facial recognition to customize the smart home. From the the trajectory went, we realized our clients were using it more for security purposes and access-control, not necessarily personalization. We made a quick pivot to a quick access control company and continue to learn about how people are using facial recognition in practice. Could it be a commercial technology that people are comfortable with? And throughout that thought process and going through and testing a bunch of other facial recognition technologies, we realized we could actually build our own platform and reach a larger audience with it and essentially be the core technology of a lot cooler and more innovative products. >> Right, and not get into the hardware business of doorbells >> Yeah, the hardware business is tough. >> That's a tough one. >> We were going to through manufacturing one and I'm glad we don't have to do that again. >> So what are some of the cool ways that people are using facial recognition that maybe we would never have thought about? >> Sure, so for face matching - The API is four components. It's face matching, face detection, face identification, and what we call spoof detection. Face matching is what it sounds like: one-to-one matching. Face detection is just detecting that someone is in the frame. The face identification is your one to act so your going into a database of people. And your spoof detection is if someone holds up a picture of me or of you and tries to get it, we'll identify that as an attack attempt and that's kind of where we differentiate our technology from most is not a lot of technology out there can do that piece and so we've packaged that all up into essentially the API for all these developers to use and some of the different ideas that people have come up with for us have been for banking logins, so for ATMs, you walk up to an ATM, you put your card in and set up a PIN so to prevent against fraud it actually scans your face and does a one-to-one match. For ship industries, so for things like cruise ships, when people get off and then come back on, instead of having them show ID, they use quick facial recognition scans. So we're seeing a lot of different ideas. One of the more funny ones is based off a company out in LA that is doing probation monitoring for drunk drivers and so we've built technology that's drunk or not drunk. >> Drunk or not drunk? >> Right so we can actually measure based on historical data if your face appears to be drunk and so you know, the possibilities are truly endless. And that's why I said we went after the development community first because >> Right right >> They're coming to use with these creative ideas. >> So it's interesting with this drunk or not drunk, of course, not to make fun of drunk driving, it's not a funny subject but obviously you've got an algorithm that determines anchor points on the eyes and the nose and certain biometric features but drunk, you're looking for much softer, more subtle clues, I would imagine because the fundamental structure of your face hasn't changed. >> Right so it's a lot of training data, so it's a lot of training data. >> Well a lot of training data, yeah. We don't want to go down that path. >> So a lot of research on our team's part. >> Well then the other thing too is the picture, is the fraud attempt. You must be looking around and shadowing and really more 3D-types of things to look over something as simple as holding up a 2D picture. >> Right so a lot of the technology that's tried to do it, that's tried to prevent against picture attacks has done so with extra hardware or extra sensors. We're actually all cloud-based right now so it isn't our software and that is what is special to us is that picture attack detection but we've a got a very very intelligent way to do it. Everything is powered by deep learning so we're constantly understanding the surroundings, the context, and making an analysis on that. >> So I'm curious from the data side, obviously you're pulling in kind of your anchor data and then for doing comparisons but then are you constantly updating that data? I mean, what's kind of your data flow look like in terms of your algorithms, are you constantly training them and adjusting those algorithms? How does that work kind of based on real time data versus your historical data? >> So we have to continue to innovate and that is how we do it, is we continue to train every single time someone shows up we train their profile once more and so if you decide to grow a beard, you're not going to grow a beard in one day, right? It's going to take you a week, two weeks. We're learning throughout those two weeks and so it's just a way for use to continue to get more data for us but also to ensure that we are identifying you properly. >> Right, do you use any external databases that you pull in as some type of you know, adding more detail or you know, kind of, other public sources or it's all your own? >> It's all our own. >> Okay and I'm curious too on the kind of opening up to the developer community, how has that kind of shaped your product roadmap and your product development? >> It - we've got to be very very conscious of not getting sidetracked because we get to hear cool ideas about what we could do but we've got our core focus of building this API for more people to use. So you know, we continue to reach out them and ask for help and you know if they find flaw or they find something cool that we want to continue to improve, we'll keep working on that so I think it's more of a - we're finding the developer community likes to really tinker and to play and because they're doing it out of passion, it helps us drive our product. >> Right right. Okay, so priorities for the rest of the year? What's at the top of the list? >> We'll be doing a bigger rollout with a couple of partners later on this year and those will be kind of our flagship partners. But again, like I said, we want to continue to support those development communities so we'll be hosting a lot of hack-a-thons and just really pushing the name out there. So we launched our product yesterday and that helped generate some awareness but we're going to have to continue to have to get the brand out there as it's now one day old. >> Right right, well good. Well it was Chui before and it's Trueface.ai so we look forward to keeping an eye on progress and congratulations on where you've gotten to date. >> Thank you very much. I appreciate that. >> Absolutely. Alrighty, Shaun Moore, it's Trueface.ai. Look at the cameras, smile, it will know it's you. You're watching Jeff Frick down at the Cube in downtown San Jose at the When IoT Met AI: The Intelligence of Things. Thanks for watching. We'll be right back after this short break.

>> Announcer: From the Fairmont Hotel in the heart of Silicon Valley, it's The Cube. Covering when IOT met AI, the intelligence of things. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Rick here with The Cube. We're in downtown San Jose at the Fairmont Hotel at a little show called when IOT Met AI, the Intelligence of Things. Talking about big data, IOT, AI and how those things are all coming together with virtual reality, artificial intelligence, augmented reality, all the fun buzz words, but this is where it's actually happening and we're real excited to have a pioneer in this space. He's Jack McCauley. He was a co-founder at Occulus VR, now spending his time at UC Berkeley as an innovator in residence. Jack welcome. >> Thank you. >> So you've been watching this thing evolve, obviously Occulus, way out front in kind of the VR space and I think augmented a reality in some ways is even more exciting than just kind of pure virtual reality. >> Right. >> So what do you think as you see this thing develop from the early days when you first sat down and started putting this all together? >> Well, I come from a gaming background. That's what I did for 30 years. I worked in video game development, particularly in hardware and things, console hardware. >> That's right, you did the Guitar Hero. >> Guitar Hero. Yeah, that's right. >> We got that one at home. >> I built their guitars and designed and built their guitars for Activision. And when were part of Red Octane, which is a studio. I primarily worked in the studio, not the headquarters, but I did some of the IP work with them too, so, to your question, you know when you produce a product and put it on the market, you never really know how it's going to do. >> Jeff: Right. >> So we make, we made two developer kits, put them out there and they exceeded our expectations and that was very good. It means that there is a market for VR, there is. We produce a consumer version and sales are not what we expected for that particular product. That was designated towards PC gamers and hopefully console games. But what has done well is the mobile stuff has exceeded everyone's mildest expectations. I heard numbers, Gear VR, which is Occulus designed product for me, sold 7 million of those. That's a smash hit. Now, worldwide for phone mounted VR goggles, it's about 20 million and that's just in two years, so that's really intriguing. So, what has happened is it's shifted away from an expensive PC based rig with $700 or whatever it costs, plus $1,500 for the computer to something that costs $50 and you just stick your cell phone in it and that's what people, it doesn't give you the best experience, but that's what has sold and so if I were doing a start-up right now, I would not be working on PC stuff, I'd be working on mobile stuff. >> Jeff: Right. >> And the next thing I think, which will play out of this is, and I think you mentioned it prior to the interview, is the 360 cameras and Google has announced a camera that they're going to come out and it's for their VR 180 initiative, which allows you to see 180 video in stereo with a cell phone strapped to your face. And that's very intriguing. There's a couple of companies out there working on similar products. Lucid Cam, which is a start-up company here has a 180 camera that's very, very good and they have one coming out that's in 4K. They just launched their product. So to answer your question, it looks like what is going to happen is for VR, is that it's a cell phone strapped to your face and a camera somewhere else that you can view and experience. A concert. Imagine taking it to a sporting event where 5,000 people can view your video, 10,000 from your seat. That's very intriguing. >> Yeah, it's interesting I had my first kind of experience just not even 360 or live view, but I did a periscope from the YouTube concert here at Levi Stadium a couple of months ago, just to try it out, I'd never really done it and it was fascinating to watch the engagement of people on that application who had either seen them the prior week in Seattle or were anticipating them coming to the Rose Bowl, I think, you know, within a couple of days, and to have an interaction just based on my little, you know, mobile phone, I was able to find a rail so I had a pretty steady vantage point, but it was a fascinating, different way to experience media, as well as engagement, as well as kind of a crowd interaction beyond the people that happened to be kind of standing in a circle. >> You, what's intriguing about VR 180 is that anybody can film the concert and put the video on YouTube or stream it through their phone. And formerly it would require a $10,000 camera, a stereo camera set up professionally, but can you imagine though that a crowd, you know, sourced sort of thing where the media is sourced by the crowd and anyone can watch it with a mobile phone. That's what's happening, I think, and with Google's announcement, it even that reinforces my opinion anyways that that is where the market will be. It's live events, sporting events. >> Right, it's an experience, right? It all comes back to kind of experience. People are so much more experience drive these days than I think thing driven from everything from buying cars versus taking a new Uber and seeing it over and over and over again. People want the experience, but not necessarily, as the CEO of Zura said, the straps and straddles of ownership, let me have the fun, I don't necessarily want to own it. But I think the other thing that gets less talked about, get your opinion, is really the kind of combination of virtual reality plus the real world, augmented reality. We see the industrial internet of things all the time where, you know, you go take a walk on that factory before you put your goggles on and not only do you see what you see that's actually in front of you, but now you can start to see, it's almost like a heads up display, certain characteristics of the machinery and this and that are now driven from the database side back into the goggles, but now the richness of your observation has completely changed. >> Yes, and in some ways when you think of what Google did with Google Glass, not as well as we had liked. >> But for a first attempt. >> Yeah. They're way ahead of their time and there will come a time when, you know, Snap has their specs, right? Have you seen those? It's not augmented reality, but, there will come a time when you can probably have a manacle on your face and see the kinds of things you need to see if your driving a car for instance that, I mean, a heads up display or a projector projecting right into your retina. So, and, so I think that's the main thing for augmented reality. Will people, I mean, your Pokemon Go, that's kind of a AR game in a way. You look through your cell phone and the character stays fixed on the table or wherever you're looking for it. I mean that uses a mobile device to do that and I can imagine other applications that use a mobile device to do that and I'm aware of people working on things like that right now. >> So do you think that the breakthrough on the mobile versus the PC-based system was just good enough? In being able to just experience that so easily, you know, I mean, Google gave out hundreds and hundreds of thousands of the cardboard boxes, so wow. >> Yeah. Well, it didn't mean that Gear VR didn't move into the market, it did. You know, it did anyways, but to answer your question about AR, you know, I think that, you know, without having good locals, I mean the problem with wearing the Google Glass and the Google cardboard and Gear VR is it kind of makes you sick a little bit and nobody's working on the localization part. Like how to get rid of the nausea effect. I watched a video that was filmed with Lucid Cam at the Pride Parade in San Francisco and I put it on and somebody was moving with the crowd and I just felt nauseous, so that problem probably probably is one I would attempt to attack if I were going to build a company or something like that right now. >> But I wonder too, how much of that is kind of getting used to the format because people when they first put them on for sure, there's like, ah, but you know, if you settle in a little bit and our eyes are pretty forgiving, you get used to things pretty quickly. Your mind can get accustomed to it to a certain degree, but even I get nauseous and I don't get nauseous very easily. >> Okay, so you're title should just be tinkerer. I looked at your Twitter handle. You're building all kinds of fun stuff in your not a garage, but your big giant lab and you're working at Berkeley. What are some of the things that you can share that you see coming down the road that people aren't necessary thinking about that's going to take some of these technologies to the next level. >> I got one for you. So you've heard of autonomous vehicles, right? >> Jeff: Yep, yep. >> And you've heard of Hollow Lens, right. Hollow Lens is an augmented reality device you put on your had and it's got built in localization and it creates what's, it's uses what's know as SLAM or S-L-A-M to build a mesh of the world around you. And with that mesh, the next guy that comes into that virtual world that you mapped will be away ahead. In other words, the map will already exists and he'll modify upon that and the mesh always gets updated. Can you imagine getting that into a self-driving vehicle just for safety's sake, mapping out the road ahead of you, the vehicle ahead of you has already mapped the road for you and you're adding to the mesh and adjusting the mesh, so I think that that's, you know, as far as Hollow Lens is concerned and their localization system, that's going to be really relevant to self-driving cars. Now whether or not it'll be Microsoft's SLAM or somebody else's, I think that that's probably the best, that's the good thing that came out of Hollow Lens and that will bleed into the self-driving car market. It's a big data crunching number and in Jobs, he was actually looking at this a long time ago, like what can we do with self-driving vehicles and I think he had banned the idea because he realized he had a huge computing and data problem. That was 10 years ago. Things have changed. But I think that that's the thing that will possibly come out of, you know, this AR stuff is that localization is just going to be transported to other areas of technology and self-driving cars and so forth. >> I just love autonomous vehicles because everything gets distilled and applied into that application, which is a great application for people to see and understand it's so tangible. >> Yeah, it may change the way we think about cars and we may just not ever own a car. >> I think absolutely. The car industry, it's ownership, it's usage, it's frequency of usage, how they're used. It's not a steel cage anymore for safety as the crash rates go down significantly. I think there's a lot of changes. >> Yeah, you buy a car and it sits for 20 hours a day. >> Right. >> Unutilized. >> All right. Well, Jack I hope maybe I get a chance to come out and check out your lab one time because you're making all kinds of cool stuff. When's that car going to be done? >> I took it upon myself to remodel a house the same time I was doing that, but the car is moving ahead. In September I think I can get it started. Get the engine running and get the power train up and running. Right now I'm working on the electronics and we have an interesting feature on that car that we're going to do an announcement on later. >> Okay, we'll look out for that. We'll keep watching the Twitter. All right, thanks for taking a few minutes. All right, let's check with Cauley. I'm Jeff Rick. You're watching The Cube from When IOT Met AI, the Intelligence of Things in San Jose. We'll be right back after this short break. Thanks for watching. (technological jingle)

>> Presenter: From the Fairmont Hotel, in the heart of Silicon Valley, it's theCUBE. Covering When IoT Met AI The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel, at an event called When IoT Met AI The Intelligence of Things. You've heard about the internet of things, and on the intelligence of things, it's IoT, it's AI, it's AR, all this stuff is really coming to play, it's very interesting space, still a lot of start-up activity, still a lot of big companies making plays in this space. So we're excited to be here, and really joined by our host, big thanks to Western Digital for hosting this event with WDLabs' Dave Tang. Got newly promoted since last we spoke. The SVP of corporate marketing and communications, for Western Digital, Dave great to see you as usual. >> Well, great to be here, thanks. >> So I don't think the need for more storage is going down anytime soon, that's kind of my takeaway. >> No, no, yeah. If this wall of data just keeps growing. >> Yeah, I think the term we had yesterday at the Ag event that we were at, also sponsored by you, is really the flood of data using an agricultural term. But it's pretty fascinating, as more, and more, and more data is not only coming off the sensors, but coming off the people, and used in so many more ways. >> That's right, yeah we see it as a virtual cycle, you create more data, you find more uses for that data to harness the power and unleash the promise of that data, and then you create even more data. So, when that virtual cycle of creating more, and finding more uses of it, and yeah one of the things that we find interesting, that's related to this event with IoT and AI, is this notion that data is falling into two general categories. There's big data, and there's fast data. So, big data I think everyone is quite familiar with by this time, these large aggregated likes of data that you can extract information out of. Look for insights and connections between data, predict the future, and create more prescriptive recommendations, right? >> Right. >> And through all of that you can gain algorithms that help to make predictions, or can help machines run based on that data. So we've gone through this phase where we focused a lot on how we harness big data, but now we're taking these algorithms that we've gleaned from that, and we're able to put them in real time applications, and that's sort of been the birth of fast data, it's been really-- >> Right, the streaming data. We cover Spark Summit, we cover Flink, and New, a new kind of open source project that came out of Berlin. That some people would say the next generation of Spark, and the other thing, you know, good for you guys, is that it used to be, not only was it old data, but it was a sampling of old data. Now on this new data, and the data stream that's all of the data. And I would actually challenge, I wonder if that separation as you describe, will stay, because I got to tell you, the last little drive I bought, just last week, was an SSD drive, you know, one terabyte. I needed some storage, and I had a choice between spinning disc and not, and I went with the flat. I mean, 'cause what's fascinating to me, is the second order benefits that we keep hearing time, and time, and time again, once people become a data-driven enterprise, are way more than just that kind of top-level thing that they thought. >> Exactly, and that's sort of that virtual cycle, you got to taste, and you learn how to use it, and then you want more. >> Jeff: Right, right. >> And that's the great thing about the breadth of technologies and products that Western Digital has, is from the solid state products, the higher performance flash products that we have, to the higher capacity helium-filled drive technologies, as well as devices going on up into systems, we cover this whole spectrum of fast data and big data. >> Right, right. >> I'll give an example. So credit card fraud detection is an interesting area. Billions of dollars potentially being lost there. Well to learn how to predict when transactions are fraudulent, you have to study massive amounts of data. Billions of transactions, so that's the big data side of it, and then as soon as you do that, you can take those algorithms and run them in real time. So as transactions come in for authorization, those algorithms can determine, before they're approved, that one's fraudulent, and that one's not. Save a lot of time and processing for fraud claims. So that's a great example of once you learn something from big data, you apply it to the real-time realm, and it's quite dire right? And then that spawned you to collect even more data, because you want to find new applications and new uses. >> Right, and too kind of this wave of computing back and forth from the shared services computer, then the desktop computer, now it's back to the cloud, and then now it's-- >> Dave: Out with the edge. >> IoT, it's all about the edge. >> Yeah, right. >> And at the end of the day, it's going to be application-specific. What needs to be processed locally, what needs to be processed back at the computer, and then all the different platforms. We were again at a navigation for autonomous vehicles show, who knew there was such a thing that small? And even the attributes of the storage required in the ecosystem of a car, right? And the environmental conditions-- >> That's right. >> Is the word I'm looking for. Completely different, new opportunity, kind of new class of hardware required to operate in that environment, and again that still combines cloud and Edge, sensors and maps. So just the, I don't think that the man's going down David. >> Yeah, absolutely >> I think you're in a good spot. (Jeff laughing) >> You're absolutely right, and even though we try to simplify into fast data, and big data, and Core and Edge, what we're finding is that applications are increasingly specialized, and have specialized needs in terms of the type of data. Is it large amounts of data, is it streaming? You know, what are the performance characteristics, and how is it being transformed, what's the compute aspect of it? And what we're finding, is that the days of general-purpose compute and storage, and memory platforms, are fading, and we're getting into environments with increasingly specialized architectures, across all those elements. Compute, memory and storage. So that's what's really exciting to be in our spot in the industry, is that we're looking at creating the future by developing new technologies that continue to fuel that growth even further, and fuel the uses of data even further. >> And fascinating just the ongoing case of Moore's law, which I know is not, you know you're not making microprocessors, but I think it's so powerful. Moore's law really is a philosophy, as opposed to an architectural spec. Just this relentless pace of innovation, and you guys just continue to push the envelope. So what are your kind of priorities? I can't believe we're halfway through 2017 already, but for kind of the balance of the year kind of, what are some of your top-of-mind things? I know it's exciting times, you're going through the merger, you know, the company is in a great space. What are your kind of top priorities for the next several months? >> Well, so, I think as a company that has gone through serial acquisitions and integrations, of course we're continuing to drive the transformation of the overall business. >> But the fun stuff right? It's not to increase your staff (Jeff laughing). >> Right, yeah, that is the hardware. >> Stitching together the European systems. >> But yeah, the fun stuff includes pushing the limits even further with solid state technologies, with our 3D NAND technologies. You know, we're leading the industry in 64 layer 3D NAND, and just yesterday we announced a 96 layer 3D NAND. So pushing those limits even further, so that we can provide higher capacities in smaller footprints, lower power, in mobile devices and out on the Edge, to drive all these exciting opportunities in IoT an AI. >> It's crazy, it's crazy. >> Yeah it is, yeah. >> You know, terabyte SD cards, terabyte Micro SD cards, I mean the amount of power that you guys pack into these smaller and smaller packages, it's magical. I mean it's absolutely magic. >> Yeah, and the same goes on the other end of the spectrum, with high-capacity devices. Our helium-filled drives are getting higher and higher capacity, 10, 12, 14 terabyte high-capacity devices for that big data core, that all the data has to end up with at some point. So we're trying to keep a balance of pushing the limits on both ends. >> Alright, well Dave, thanks for taking a few minutes out of your busy day, and congratulations on all your success. >> Great, good to be here. >> Alright, he's Dave Tang from Western Digital, he's changing your world, my world, and everyone else's. We're here in San Jose, you're watching theCUBE, thanks for watching.

(upbeat electronic music) >> Narrator: From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE. Covering when IoT met AI, The Intelligence of Things. Brought to you by Western Digital. >> Welcome back here everybody, Jeff Frick here with theCUBE. We are at downtown San Jose at the Fairmont Hotel. When IoT met AI it happened right here, you saw it first. The Intelligence of Things, a really interesting event put on by readwrite and Western Digital and we are really excited to welcome back a many time CUBE alumni and always a fan favorite, she's Janet George. She's Fellow & Chief Data Officer of Western Digital. Janet, great to see you. >> Thank you, thank you. >> So, as I asked you when you sat down, you're always working on cool things. You're always kind of at the cutting edge. So, what have you been playing with lately? >> Lately I have been working on neural networks and TensorFlow. So really trying to study and understand the behaviors and patterns of neural networks, how they work and then unleashing our data at it. So trying to figure out how it's training through our data, how many nets there are, and then trying to figure out what results it's coming with. What are the predictions? Looking at how the predictions are, whether the predictions are accurate or less accurate and then validating the predictions to make it more accurate, and so on and so forth. >> So it's interesting. It's a different tool, so you're learning the tool itself. >> Yes. >> And you're learning the underlying technology behind the tool. >> Yes. >> And then testing it actually against some of the other tools that you guys have, I mean obviously you guys have been doing- >> That's right. >> Mean time between failure analysis for a long long time. >> That's right, that's right. >> So, first off, kind of experience with the tool, how is it different? >> So with machine learning, fundamentally we have to go into feature extraction. So you have to figure out all the features and then you use the features for predictions. With neural networks you can throw all the raw data at it. It's in fact data-agnostic. So you don't have to spend enormous amounts of time trying to detect the features. Like for example, If you throw hundreds of cat images at the neural network, the neural network will figure out image features of the cat; the nose, the eyes, the ears and so on and so forth. And once it trains itself through a series of iterations, you can throw a lot of deranged cats at the neural network and it's still going to figure out what the features of a real cat is. >> Right. >> And it will predict the cat correctly. >> Right. So then, how does that apply to, you know, the more specific use case in terms of your failure analysis? >> Yeah. So we have failures and we have multiple failures. Some failures through through the human eye, it's very obvious, right? But humans get tired, and over a period of time we can't endure looking at hundreds and millions of failures, right? And some failures are interconnected. So there is a relationship between these failure patterns or there is a correlation between two failures, right? It could be an edge failure. It could a radial failure, eye pattern type failure. It could be a radial failure. So these failures, for us as humans, we can't escape. >> Right. >> And we used to be able to take these failures and train them at scale and then predict. Now with neural networks, we don't have to take and do all that. We don't have to extract these labels and try to show them what these failures look like. Training is almost like throwing a lot of data at the neural networks. >> So it almost sounds like kind of the promise of the data lake if you will. >> Yes. >> If you have heard about, from the Hadoop Summit- >> Yes, yes, yes. >> For ever and ever and ever. Right? You dump it all in and insights will flow. But we found, often, that that's not true. You need hypothesis. >> Yes, yes. >> You need to structure and get it going. But what you're describing though, sounds much more along kind of that vision. >> Yes, very much so. Now, the only caveat is you need some labels, right? If there is no label on the failure data, it's very difficult for the neural networks to figure out what the failure is. >> Jeff: Right. >> So you have to give it some labels to understand what patterns it should learn. >> Right. >> Right, and that is where the domain experts come in. So we train it with labeled data. So if you are training with a cat, you know the features of a cat, right? In the industrial world, cat is really what's in the heads of people. The domain knowledge is not so authoritative. Like the sky or the animals or the cat. >> Jeff: Right. >> The domain knowledge is much more embedded in the brains of the people who are working. And so we have to extract that domain knowledge into labels. And then you're able to scale the domain. >> Jeff: Right. >> Through the neural network. >> So okay so then how does it then compare with the other tools that you've used in the past? In terms of, obviously the process is very different, but in terms of just pure performance? What are you finding? >> So we are finding very good performance and actually we are finding very good accuracy. Right? So once it's trained, and it's doing very well on the failure patterns, it's getting it right 90% of the time, right? >> Really? >> Yes, but in a machine learning program, what happens is sometimes the model is over-fitted or it's under-fitted or there is bias in the model and you got to remove the bias in the model or you got to figure out, well, is the model false-positive or false-negative? You got to optimize for something, right? >> Right, right. >> Because we are really dealing with mathematical approximation, we are not dealing with preciseness, we are not dealing with exactness. >> Right, right. >> In neural networks, actually, it's pretty good, because it's actually always dealing with accuracy. It's not dealing with precision, right? So it's accurate most of the time. >> Interesting, because that's often what's common about the kind of difference between computer science and statistics, right? >> Yes. >> Computers is binary. Statistics always has a kind of a confidence interval. But what you're describing, it sounds like the confidence is tightening up to such a degree that it's almost reaching binary. >> Yeah, yeah, exactly. And see, brute force is good when your traditional computing programing paradigm is very brute force type paradigm, right? The traditional paradigm is very good when the problems are simpler. But when the problems are of scale, like you're talking 70 petabytes of data or you're talking 70 billion roles, right? Find all these patterns in that, right? >> Jeff: Right. >> I mean you just, the scale at which that operates and at the scale at which traditional machine learning even works is quite different from how neural networks work. >> Jeff: Okay. >> Right? Traditional machine learning you still have to do some feature extraction. You still have to say "Oh I can't." Otherwise you are going to have dimensionality issues, right? It's too broad to get the prediction anywhere close. >> Right. >> Right? And so you want to reduce the dimensionality to get a better prediction. But here you don't have to worry about dimensionality. You just have to make sure the labels are right. >> Right, right. So as you dig deeper into this tool and expose all these new capabilities, what do you look forward to? What can you do that you couldn't do before? >> It's interesting because it's grossly underestimating the human brain, right? The human brain is supremely powerful in all aspects, right? And there is a great deal of difficulty in trying to code the human brain, right? But with neural networks and because of the various propagation layers and the ability to move through these networks we are coming closer and closer, right? So one example: When you think about driving, recently, Google driverless car got into an accident, right? And where it got into an accident was the driverless car was merging into a lane and there was a bus and it collided with the bus. So where did A.I. go wrong? Now if you train an A.I., birds can fly, and then you say penguin is a bird, it is going to assume penguin can fly. >> Jeff: Right, right. >> We as humans know penguin is a bird but it can't fly like other birds, right? >> Jeff: Right. >> It's that anomaly thing, right? Naturally when are driving and a bus shows up, even if it's yield, the bus goes. >> Jeff: Right, right. >> We yield to the bus because it's bigger and we know that. >> A.I. doesn't know that. It was taught that yield is yield. >> Right, right. >> So it collided with the bus. But the beauty is now large fleets of cars can learn very quickly based on what it just got from that one car. >> Right, right. >> So now there are pros and cons. So think about you driving down Highway 85 and there is a collision, it's Sunday morning, you don't know about the collision. You're coming down on the hill, right? Blind corner and boom that's how these crashes happen and so many people died, right? If you were driving a driverless car, you would have knowledge from the fleet and from everywhere else. >> Right. >> So you know ahead of time. We don't talk to each other when we are in cars. We don't have universal knowledge, right? >> Car-to-car communication. >> Car-to-car communications and A.I. has that so directly it can save accidents. It can save people from dying, right? But people still feel, it's a psychology thing, people still feel very unsafe in a driverless car, right? So we have to get over- >> Well they will get over that. They feel plenty safe in a driverless airplane, right? >> That's right. Or in a driveless light rail. >> Jeff: Right. >> Or, you know, when somebody else is driving they're fine with the driver who's driving. You just sit in the driver's car. >> But there's that one pesky autonomous car problem, when the pedestrian won't go. >> Yeah. >> And the car is stopped it's like a friendly battle-lock. >> That's right, that's right. >> Well good stuff Janet and always great to see you. I'm sure we will see you very shortly 'cause you are at all the great big data conferences. >> Thank you. >> Thanks for taking a few minutes out of your day. >> Thank you. >> Alright she is Janet George, she is the smartest lady at Western Digital, perhaps in Silicon Valley. We're not sure but we feel pretty confident. I am Jeff Frick and you're watching theCUBE from When IoT meets AI: The Intelligence of Things. We will be right back after this short break. Thanks for watching. (upbeat electronic music)

>> Announcer: From the Fairmont Hotel in the heart of Silicon Valley, it's The Cube. Covering When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Hey, welcome back, everybody. Jeff Frick here with The Cube. We're in downtown Los Angeles at the Fairmont Hotel at a interesting little show called When IoT Met AI: The Intelligence of Things. A lot of cool startups here along with some big companies. We're really excited go have our next guest, taking a little different angle. He's Scott Noteboom. He is the co-founder and CEO of a company called Litbit. First off, Scott, welcome. >> Yeah, thank you very much. >> Absolutely. For folks that aren't familiar, what is Litbit, what's your core mission? >> Well, probably, the simplest way to put it is, is in business we enable our users who have a lot of experience in a lot of different areas to take their expertise and experience which may not be coding software, or understanding, or even being able to spell what an algorithm is on the data science perspective, and being able to give them an easy interface so they can kind of create their own Siro or Alexa, an AI but an AI that's based on their own subject matter expertise that they can put to work in a lot of different ways. >> So, there's often a lot of talk about kind of tribal knowledge, and how does tribal knowledge get passed down so people know how to do things. Whether it's with new employees, or as you were talking about a little bit off camera, just remote locations for this or that. And there hasn't really been a great system to do that. So, you're really attacking that, not only with the documentation, but then making an AI actionable piece of software that can then drive machines and using IoT to do things. Is that correct? >> That's right. So, if you created, say an AI that I've been passionate about 'cause I ran data centers for a lot of years, is DAC. So, DAC's an AI that has a lot of expertise, and how to run a data center by, and kind of fueled and mentored by a lot of the experts in the industry. So, how can you take DAC and put Dak to work in a lot of places? And the people who need the best trained DAC aren't people who are building apps. They are people who have their area of subject matter expertise, and we view these AI personas that can be put to work as kind of apps of the future, where can people can prescribe to personas that are build directly by the experts, which is a pretty pure way to connect AIs with the right people, and then be able to get them and put them-- >> So, there's kind of two steps to the process. How does the information get from the experts into your system? How's that training happen? >> So, where we spend a lot of attention is, a lot of people question and go, "Well, an AI lives in this virtual logical world "that's disconnected from the physical world." And I always questions for people to close their eyes and imagine their favorite person that loves them in the world. And when they picture that person hear that person's voice in their head, that's actually a very similar virtual world as what AIs working. It's not the physical world. And what connects us as people to the physical world, our senses, our sight, our hearing, our touch, our feeling. And what we've done is we've enabled using IoT sensors, the ability of combining those sensors with AI to turn sensors into senses, which then provide the ability for the AI to connect really meaningful ways to the physical world. And then the experts can teach the AI this is what this looks like, this is what this sounds like, this is what it's supposed to feel like. If it's greater than 80 degrees in an office location, it's hot. Really teaching the AI to be able to form thoughts based on a specific expertise and then be able to take the right actions to do the right things when those thoughts are formed. >> How do you deal with nuance, 'cause I'm sure there's a lot of times where people, as you said, are sensing or smelling or something, but they don't even necessarily consciously know that that's an input into their decision process, even though it really is. They just haven't really thought of it as a discrete input. How do you separate out all these discreet inputs so you get a great model that represents your best of breed technicians? >> Well, to try to answer the question, first of all, the more training the better. So, the good way to think of the AI is, unlike a lot of technologies that typically age and go out of life over time, an AI continuously gets smarter the more it's mentored by people, which would be supervised learning. And the more it can adjust and learn on it's own combined with real day to day data activity combined with that supervised learning and unsupervised learning approach, so enabling it to continuously get better over time. We've figure out some ways that it can produce some pretty meaningful results with a small amount of training. So, yeah. >> Okay. What are some of the applications, kind of your initial go to market? >> We're a small startup, and really, what we've done is we've developed a platform that we really like to, our goal is for it to be very horizontal in nature. And then the applications or the AI personas can be very vertical or subject matter experts across different silos. So, what we're doing is, is we're working with partners right now in different silos developing AIs that have expertise in the oil and gas business, in the pharmaceutical space, in the data center space, in the corporate facilities manage space, and really making sure that people who aren't technologists in all of those spaces, whether you're a very specific scientists who're running a lab, or a facilities guy in a corporate building, can successfully make that experiential connection between themselves and the AI, and put it to practical use. And then as we go, there's a lot of efforts that can be very specific to specific silos, whatever they may be. >> So, those personas are actually roles of individuals, if you will, performing certain tasks within those verticals. >> Absolutely. What we call them is coworkers, and the way things are designed is, one of the things that I think is really important in the AI world is that we approach everything from a human perspective because it's a big disruptive shift, and there's a lot of concern over it. So, if you get people to connect to it in a humanistic way, like coworker Viv works along with coworker Sophia, and Viv has this expertise, Sophia has this expertise, and has better improving ways to interface with people who have names that aren't a lot different from them and have skillsets that aren't a lot different. When you look at the AIS, they don't mind working longer hours. Let them work the weekends so I can spend hours with my family. Let them work the crazy shifts. So, things are different in that regard. But the relationship aspect of how the workplace works, try not to disrupt that too much. >> So, then on a consumption side, with the person coworker that's working with the persona, how do they interact with it, how do they get the data out, and I guess even more importantly, maybe, how do they get the new data back in to continue to train the model? >> So, the biggest thing you have to focus on with a human and machine learning interface that doesn't require a program or a data science, is that the language that the AI is taught in is human language, natural human language. So, we developed a lot of natural human language files that are pretty neat because a human coworker in California here could be interfacing in english to their coworker, and at the same time, someone speaking Mandarin in Shanghai could be interfacing with the same coworker speaking mandarin unless you can get multilingual functionality. Right now, to answer your question, people are doing it in a text based scenario. But the future vision, I think when the industry timing is right, is we view that every one of the coworkers we're developing will have a very distinct unique fingerprint of a voice. So, therefor, when you're engaging with your coworker using voice, you'll begin to recognize, oh, that's Dax, or that's Viv, or that's Sophia, based on their voice. So, like many people, this is how we're communicating with voice, and we believe the same thing's going to occur. And a lot of that's in timing. That's the direction where things are headed. >> Interesting. The whole voice aspect is just a whole 'nother interesting thing in terms of what type of voice personality attributes associated with voice. That's probably going to be a huge piece in terms of the adoption, in terms of having a true coworker experience, if you will. >> One of the things we haven't figure out, and these are important questions, and there's so many unknowns, is we feel really confident that the AI persona should have a unique voice because then I know who I'm engaging with, and I can connect by ear without them saying what their name is. But what does an AI persona look like? That's something where actually we don't know that, and we explore different things and, oh, that looks scary, or oh, that doesn't make sense. Should it look like anything? Which has largely been the approach of what does an Alexa or a Siri look like. As you continue to advance those engagements, and particularly when augmented reality comes into play, through augmented reality, if you're able to look and say, "Oh, a coworker's working over there," there's some value in that. But what is it going to look like? That's interesting, and we don't know that. >> Hopefully, better than those things at the San Jose Airport that are running around. >> Yeah, exactly. >> Classic robot. All right, Scott, very interesting story. I look forward to watching you grow and develop over time. >> Awesome, it's good to talk. >> Absolutely, all right, he's Scott Noteboom, he's from Litbit. I'm Jeff Frick, you're watching The Cube. We're at When IoT met AI: The Intelligence of Things, here at San Jose California. We'll be right back after the short break. Thanks for watching. (upbeat music)

>> Narrator: From the Fairmont Hotel in the heart of Silicon Valley it's theCUBE covering when IoT met AI, The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back here everybody Jeff Frick here with theCUBE. We're in San Jose, California at the Fairmont Hotel, at the when IoT met AI show, it's all about the intelligence of things. A lot of really interesting start ups here, we're still so early days in most of this technology. Facial recognition gets a lot of play, iris recognition, got to get rid of these stupid passwords. We're really excited to have our next guest, he's Modar Alaoui, he's the CEO and founder of Eyeris. And it says here Modar that you guys are into face analytics and emotion recognition. First off welcome. >> Thank you so much for having me. >> So face analytics, I'm a clear customer I love going to clear at the airport, I put my two fingers down, I think they have my iris, they have different things but what's special about the face compared to some of these other biometric options that people have? >> We go beyond just the biometrics, we do pretty much the entire suites of face analytics. Anything from eye openness, face, gender, emotion recognition, head bows, gaze estimation, et cetera et cetera. So it is pretty much anything and everything you can derive from the face including non verbal clues, yawning, head nod, head shake, et cetera. >> That was a huge range of things, so clearly just the face recognition to know that I am me probably relatively straight forward. A couple anchor points, does everything measure up and match the prior? But emotion that's a whole different thing, not only are there lots of different emotions, but the way I express my emotion might be different than the way you express the very same emotion. Right, everybody has a different smile. So how do you start to figure out the algorithms to sort through this? >> Right, so you're right. There are some nuances between cultures, ages, genders, ethnicities and things like that. Generally they've been universalized for the past three and a half decades by the scholars the psychologists et cetera. So what they actually have a consensus on is that there are only seven or six universal emotions plus neutral. >> Six, what are the six? >> Joy, surprise, anger, disgust, fear, sadness, and neutral. >> Okay and everything is some derivation of that, you can kind of put everything into little buckets. >> That is correct so think of them as seven universal colors or seven primary colors and then everything else is a derivative of that. The other thing is that emotions are hard wired into our brain they happen in a 1/15th or a 1/25th of a second, particularly micro expressions. And they can generally give up a lot of information as to whether a person has suppressed the certain emotion or not or whether they are thinking about something negatively before they could respond positively, et cetera. >> Okay so now you've got the data, you know how I'm feeling, what are you doing with it? It must tie back to all types of different applications I would assume. >> That's right there are a number of applications. Initially when we created this, what we call, enabling technology we wanted to focus on two things. One, is what type of application could have the biggest impact but also the quickest adoption in terms of volumes. Today we focus on driver monitoring AI as well as occupants monitoring AI so we focus on Autonomous and semi autonomous vehicles. And a second application is social robotics, but in essence if you think of a car it's also another robot except that social robotics are those potentially AI engines, or even AI engines in form of an actual robot that communicates with humans. Therefore, the word social. >> Right, so I can see a kind of semi autonomous vehicle or even a not autonomous vehicle you want to know if I'm dosing off. And some of those things have been around in a basic form for a little while. But what about in an autonomous vehicle is impacted by my emotion as a passenger, not necessarily a driver if it's a level five? >> That's right, so when we talk about an autonomous vehicle I think what you're referring to is level five autonomy where a vehicle does not actually have a steering wheel or gas pedal or anything like that. And we don't foresee that those will be on a road for at least another 10 years or more. The focus today is on level two, three, and four, and that's semi autonomy. Even for autonomous, fully autonomous vehicles, you would see them come out with vision sensors or vision AI inside the vehicle. So that these sensors could, together with the software that could analyze everything that's happening inside, cater to the services towards what is going to be the ridership economy. Once the car drives itself autonomously, the focus shifts from the driver to the occupants. As a matter of a fact it's the occupants that would be riding in these vehicles or buying them or sharing them, not the driver. And therefore all these services will revolve around who is inside the vehicle like age, gender emotion, activity, et cetera. >> Interesting, so all these things the age, gender emotion, activity, what is the most important do you think in terms of your business and kind of where as you say you can have a big impact. >> We can group them into two categories, the first one is safety obviously, eye openness, head bows, blinking, yawning, and all these things are utmost importance especially focused on the driver at this point. But then there is a number of applications that relates to comfort and personalization. And so those could potentially take advantage of the emotions and the rest of the analytics. >> Okay, so then where are you guys, Eyeris as a company? Where do have some installations I assume out there? Are you still early days kind of? Where are you in terms of the development of the company? >> We have quite a mature product, what I can disclose is we have plans to go into mass production starting 2018. Some plans for Q4 2017 have been pushed out. So we'll probably start seeing some of those in Q1, Q2 2018. >> Okay. >> We made some announcements earlier this year at CS with Toyota and Honda. But then we'll be seeing some mass volume starting 2019 and beyond. >> Okay, and I assume you're a cloud based solution. >> We do have that as well, but we are particularly a local processing solution. >> Jeff: Oh you are? >> Yes so think of it as an edge computing type of solution. >> Okay and then you work with other peoples sensors and existing systems or are you more of a software component that plugs in? Or you provide the whole system in terms of the, I assume, cameras to watch the people? >> So we're a software company only, we however, are hardware processor camera diagnostic. And of course for everything to succeed there will have to be some components of sensor fusion. And therefore we can work and do work with other sensor companies in order to provide higher confidence level of all the analytics that we provide. >> Pretty exciting, so is it commercially available you're GA now or not quite yet? >> We'll be commercially available, you'll start seeing it on the roads or in the market sometime early next year. >> Sometime early next year? Alright well we will look forward to it. >> Thank you so much. >> Very exciting times, alright, he's Modar Alaoui. And he's going to be paying attention to you to make sure you're paying attention to the roads. So you don't fall asleep, or doze off and go to sleep. So I'm Jeff Frick, you're watching theCUBE at IoT met AI, The Intelligence of Things. San Jose, California, we'll be right back after this short break, thanks for watching. (bright techno music)

(upbeat music) >> Announcer: From the Fairmont Hotel, in the heart of Silicon Valley, it's theCUBE. Covering, When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Welcome back everybody. Jeff Frick here with theCUBE. We're at Downtown San Jose at the Fairmont Hotel at a small little conference, very intimate affair, talking about IoT and AI, The Intelligence of Things. When IoT met AI. Now, they've got a cool little start up, kind of expo hall. We're excited to have our next guest here from that. It's Mike Wilson, he's the CEO of BriteThings. Mike, welcome. >> Good to be here, Jeff, how you doin'? >> Absolutely. So, BriteThings. What are BriteThings? >> BriteThings are intelligent plugs, power strips, wall sockets, anything that fits into the plug load space. It learns users behavior and then provides them an intelligent on-off schedule. The goal here is to turn stuff off when it's on and not being needed. >> Right. >> So wasted energy. Nights and weekends in the workspace, for example. >> It sounds like such a simple thing. >> Totally. >> But we were talking before we turned the cameras on, this actually has giant economic impact >> It does. >> in building maintenance, which is a huge category >> Yup. >> as you said, I'll let you kind of break down the numbers as to where >> Sure. >> that energy's being spent and the impact that you guys are having. >> Well our customers are building owners and operators, and they pay an electrical bill to run that building. It's a cost of running the building. About 27% of it goes to lighting, about 38% goes to heating and cooling, and all the rest goes to plug loads. And where we come to the market it, of course there's huge lighting companies, famous names, same with HVAC, but no one's doing anything about plug loads, and the reason is is because plug loads are distributed, they're hard to control. And so what we bring to the market is a product that is small, inexpensive, and can suddenly give owners and operators all the control that they enjoy with lighting and HVAC over their plug loads. >> So it's kind of like Dest, in that it takes a relatively simple function, now because of the cloud, because of the internet, you can add a lot more intelligence into a relatively, I don't want to say dumb device, but the device itself doesn't have to have that much power 'cause you can put the application somewhere else. >> Exactly, so if you just imagine, you're sitting here with me right now. Probably at your workplace and at home there's a bunch of stuff turned on, you're not using it, >> Right >> but you're spending money to keep it powered up, and that's causing CO2 to be generated at power plant down the road. So that's bad for your pocket, it's bad for the environment. So if we can automatically turn that stuff off, then people don't have to worry about it. We can measure it, so here's where the money is. >> Right. >> Not only energy savings, but data. So I can tell you when you turned your stuff on and off, so that means human presence. When you're at work, there's a value to that. If you're going to put a floor of an office building out there and heat it or light it, we can tell you if people are there or not. So you can look at that and make, and save even more money. >> Jeff: Right. >> We've got one customer that uses our product for inventory management. If it plugs in, you can see it on our screen, and you can see if it's on or off, if it's connected and how it's running. So that kind of data ends up being valuable, not only for energy savings, because we turn stuff on and off, but human presence, inventory control, the list goes on and on. Our customers actually every year are coming up with new ways to use our device. >> Right. And just for the baseline savings, you just basically plug it in and turn it on, and you're reporting some huge savings just by just the basic operation of your strips versus a regular strip. >> Exactly. So just imagine, this device is learning your behavior, so that's part of our, you know, that's kind of our core competency here, is these devices measure the amount of energy you're using. When you're not using something, it goes into standby mode, or sleep mode. Then we turn that off to save you the money. But the way we're able to do that is using artificial intelligence to learn patterns, and take those patterns and you can basically guess the best optimized schedule for your devices to be turned and off. >> Right. >> On and off. So if you imagine you've got 100,000 employees, 100,000 different schedules, this thing has to be smart and it can't affect worker productivity. >> Right. >> So we have to be smart enough to know when to turn it on before you come into work, when to turn it off to save you the max amount of money, and be able to measure all of that so you can roll that up and see how much money you're saving. How much CO2 are you reducing? >> Right. >> You know, so sustainability officers love our product too. >> So do you integrate with other types of intelligent systems in that space? The lightings, and the HVAC? >> Yeah. Exactly. So one of the most important things is, I've got a portfolio, my office building is a portfolio of devices and systems, so just one of them is our plug load management, right? So I want to be able to see my plug load in my current control panel. So we've got APIs where our cloud technology is able to take that reporting and stick it into, for example, a Lucid control panel. We're working with Trane right now to integrate their BACnet solution for their building control management. >> Right, right. >> So that their customers are able to see lighting, HVAC, and plug load, >> Just what I was going to say. >> right off the same old screen and operating tools that they've always used. >> Right, right. What's kind of the typical ROI that you pitch people just for the straight-up money savings that they're going to get? >> We got our foot in the door by saying we can reduce your plug load cost a minimum of 30%, and what we're seeing on average is about 40 to 45%. >> Wow. >> It's a huge huge reduction. >> Now where do you go next? >> Well, conquer the world. (Jeff laughs) You know, so imagine this, anywhere in the commercial office space where there's a plug, so let your mind go, how many power strips are out there? >> Right, right. >> How many of those-- >> We're using about 20 of them right here. >> Yeah, so, just, you know, every person at every desk is a potential customer. Every time there's a coffeemaker or a break room, a fax machine, you know, any piece of equipment that's plugged in, we can save you money. Vending machines. We have a customer with these, you know, raise and lower desks. Crazy, they want to just see, they don't want to save energy, they want to know who's using that and how often. >> Jeff: Right, right. >> Our device can do that, too. >> Right. >> And that's that data I was telling you about. Once you start collecting data of how people use plugged-in devices, I'm collecting information about you, how you use your laptop, how you use your charger, how often. >> Because the signature on the draw is different depending on the activity of the device. >> You got it. Exactly. >> I love this. You know, it's so funny because the second-order impact of all these types of things is so much more significant than people give it credit, I think. >> It's about the data. >> Jeff: Yeah. >> And our customer's just love that, because the data gives them control, and when you have control, cost savings. >> And is it just commercial, or you sell them for regular retail customers as well? Or do you-- >> I imagine some day in the future that's a potential, but you know, our focus right now, 'cause the big problem out there is that buildings use 40% of all the energy generated in the United States, and commercial space is the big opportunity, because nights and weekends. >> Right. >> Stuff should be turned off, and we can do that right now. >> Right, right. >> We're the market doing it. >> Buildings with big, big POs. >> Yup. (Jeff laughs) >> Alright, Michael, sounds like exciting stuff, can't wait til I can get one at Best Buy or Office Depot, or something. >> Coming to a store near you, or www.britethings.com. >> Alright, thanks a lot, he's Mike Wilson. Save some energy, get one of these things when they're available, or at least tell the boss to get one at the office. (Michael laughs) >> Definitely. >> Alright, I'm Jeff Frick, you're watching theCUBE. When IoT meets AI in San Jose, California. Thanks for watching. (upbeat music)

>> Narrator: Live, from Las Vegas, it's The Cube, covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. >> Hi everybody, welcome back to Las Vegas, my name is Dave Vellante and this is day three of The Cube's live wall to wall coverage of Hewlett Packard Enterprise, HPE Discover. This is The Cube, the leader and live tech coverage. We have a little reveal here, JR Fuller is here, he's the Global Business Development Manager IoT Edgeline at Hewlett Packard Enterprise and he's joined by Doug Smith, who is the CEO of Texmark. Gentleman, welcome. >> Thank you. >> Thank you for having us. >> Alright lay it on us Doug, what is Texmark all about? We're going to have, like I say, a little virtual reveal here-- >> Sure. And first of all, thanks for having me here-- >> Dave: You're very welcome. >> And, Texmark Chemical is a 50 year-old company, located in Galena Park, Texas, which is right on the Houston Ship Channel outside of the city of Houston. We are a manufacturer of specialty chemicals, one being DCPD, which stands for dicyclopentadiene. We have been making significant capital investments in the physical plant, over the last 20 years. And about two years ago, we realized we needed to move forward in a control system, a new control system, initiative at the plant, as well as a baseline mechanical integrity. Initiative. And so we're a small organization of 53 people and we looked to our contacts and got in touch with HPE and started a conversation. We don't have a normal client-customer relationship. We have a partnership of people, HPE people, Texmark people. >> Absolutely. >> So JR, pick it up from HPE's side. So, you guys have made a big push into this whole IoT business and you need partners like Doug's firm. >> Yeah, absolutely. So it's kind of interesting the way we got started. You probably remember last year, we had the big pump. The pump demo, the Filzer pump demo, so that was a project of mine, and Dough had heard about that from a mutual friend and ... Gracious. Very gracious of him, he invited us to come out at Texmark and actually install that at his facility. And he said "I got this bug pond over there, you can put that in there." And then you have a production version of that, 'cause we had the proof of concept version in our lap, and I said "That is really nice and very sweet, but no. "Let's figure out what we can do that will really benefit you, 'cause that won't really benefit you." And that started a dialogue that's, been about a year that we've been talking about this and I think it was in August, I proposed to him and said, "What do you think about "doing a refinery of the future?" And his words to me were, "JR, I don't know what "it is, but I love it." And I said, "Well, let's figure out what it is "for Texmark and let's go from there." And that's kind of how we started the genesis of this entire journey, of what we're doing. >> So you kind of laid out the vision, which is fantastic-- >> JR: Right. >> Sort of your North Star. And then just for the audiences benefit, you know, everyone here discovered there was this amazing floor exhibit, and it was pumps and tubes and pipes. >> JR: We've seen learning and, yeah. >> And it was all kinds of data, that was flowing through there, and sort of I guess, a digital twin if you will. >> Exactly. >> Of the factory floor ... >> Doug: Well of a plant, yes. And that's a great segway into Texmark and how we have synergy between our two organizations is that Texmark, although a small chemical process facility, we have all the equipment that the huge companies have. We have boilers, we have pipes, we have distillation columns, and we need to move forward, with our people to instrument, to gather data, to data analytics on the edge to have a connected facility with wifi capabilities, so that's where the conversation started. >> So much of the data ... Maybe even most of data today, historically anyway, analog data, is that correct? >> It is a combination. >> Dave: Okay. >> What we are doing, once again, we are a small organization. We have one IT person. And that person is contract, so how we're approaching it is, Texmark stays in the chemical, we use the analogy of, swim lanes. We are swimming towards profitability in the chemical business. HPE is swimming in the lane with-- >> All the technology. >> Technology. And then we're working together on this voyage of discovery, out here, that we're figuring out along the way. >> And for sure, you're not IT, you're operations. >> Doug: Yes, sir. >> Right? And you guys are IT. >> Exactly. >> So talk more about the partnership. What is that all about? >> Doug: People. >> JR: It's totally about people and it's interacting with each other, it's showing up ever day, it's working towards things. It's, when you do run into a problem ... And Doug's got a great story of when we had a problem. When you do run into a problem, you have the mutual of how to solve this problem together. In a typical customer-vendor relationship, there's some kind of built-in tension that's there and you know, you're worried about, "Oh, the vendor's trying to do this to me" , or "Oh, the customer if trying to get something from me." And we don't have any of that. We actually have a very solid partnership and occasionally, if one of my team or one of his team gets off track on that, we bring them back to the fold and say, "No, no, no. We're plowing road here." We need them to cut trees, we need us to cut trees, we all need to be heading in the same direction. You can't stop and go, "How come this isn't paved?" Because it hasn't been done before. >> And it's that shared objective of the refinery of the future that you're working towards. So, can we describe in a little bit more detail, the refinery of the future. >> Doug: Sure. Let me just jump in on that, because in this voyage of discovery, with these conversations, we talked about, what do we need to achieve the goals that we want? And so, first there is the hardware component. What do we here to achieve these goals? We'll just take the example of the pump. The pump is the heart of any process facility. If you have a critical pump go down, it can put you out of operation. There's a cost associated with that, and so what we need to do ... There's a cost associated with putting wiring from our control center to an actual pump. If we can have a wireless network and a censor on a pump, we eliminate the cost of physical wiring. The wireless network was provided by one of our content partners, Aruba, and so that is installed. We are working-- >> Dave: You know those guys? >> JR: I do, I do. >> He's heard of them. So then, what do we do with that data when it comes in? So, we have two Edgeline servers in there, and we have one in our control room, and then we have one, and it's super. They have one here, on the floor here, at the Discovery, the Micro-data center, which is for our place, everybody's like ... (sings) (laughter) >> It's fantastic-- >> Dave: It's data in a box. >> Yes, sir.And what that does, we have the ... I'll just give you an example. So we have our old system, the old server over here, size of a refrigerator, and I have used this numerous times when explaining the project to people here at Discover is that, I have to explain what we're doing to my 81 year-old mother. And when I say we have a refrigerator over there that used to run the plant, and now we have this one little thing the size of a little tablet-- (JR laughs) >> She goes ... And it saves money. It increases efficiency, she gets that. So those are some of the phases of the project, and now I'll pass it over to JR 'cause we then identify how are we going to use this cool hardware to achieve objectives? >> Yeah. So when we look at the refinery future, we usually have a three phrase project, alright? You don't boil the ocean, you bring it down into ... So phase one for us was putting in the Aruba wifi network out in the entire facility. We've done that. And because it's a petro chemical plant, it needs to go into a special enclosure. So we have a partner with Extronics, out in the U.K., that creates this protective enclosure. >> Dave: Like militarize. >> Yeah. Well, it's actually even beyond that, because in type one, dib one environments, there is a potential for hazardous gas to be out in there, and so electronic equipment would be sparking and stuff like that, and gas that can explode. Not a good combination. So, these div one boxes, make it so that, if there is an interaction with a spark, and some flammable gas, and there's an explosion, it's contained in that box, and not contaminates to the whole factory, which would be-- >> Plant. (laughs) >> Plant, the whole plant. Where it would actually create problems for everybody else, so that first phase was putting those div one compliant wifi AP's out there from Aruba. We also put in our beacons, with our location-based services, the meridian system out there, so they can do wave-finders and get to the right pump to fix it. And also, they're clear pass, so putting clear pass out there so it's a secured network, right? We don't want anybody to be able to go in there and mess with anything. >> So basic productivity, the security to allow that, all that basic infrastructure. >> So that was to-- >> To connect the ... >> Exactly. That was phase one. Phase two was, they had rack of other people's compute in there and we replaced all of that, like Doug said, with two of our Edgeline EL 4000 Converge systems. >> Dave: Okay. >> One of those, we actually mounted on the control room floor, so right out on the Edge, not in a data center environment, not in a temperature-controlled place, per say, and what we consider our data center. And then that other one, we actually did get an HPE Micro-data center, and we put the other one in there. It's secured, it's badge-access only. Only a couple people in Texmark have badge access to actually be able to get that. And when we look at the compute needs growing, that's where they're going to probably grow into, is that data. >> So phase two was bring the the compute. >> So I call those two, phase one and phase two, my infrastructure phase, 'cause now I've got what I need to do. Now phase three is really interesting because that's where we're going to start doing IoT stuff, right? So there are five projects that we're doing on IoT. So the first one is predictive analytics. This is both at the discreet and the process level. So, when we talk about that pump that we saw last year, that's a discreet machine. We're doing predicted analytics on that machine. But that machine feeds a process, so how can we predict what's happening on this machine, what's the impact of that to this process? So that's the first one. >> Doug: Can I hop in? >> Yeah, go for it. >> So, JR is using the example of the pump, and I mentioned the pump earlier, being the heart of the organization. So, it's been interesting being at Discover for the first time for me and the way that I have been talking with people, you have people that are extremely interested in the human component, and how is it affecting people? Also there is, the critical bottom line. How is it going to make me money and save me money? >> Dave: Right. >> So this pump is an excellent example that addresses both of those. So, if have a pump fail, there is a significant cost if it shuts us down for the day. We're a seven acre facility, and let's just throw a number out for easy math. Let's just say it costs us $100,000 a day, if that pump goes down. If you have a facility that's 100,000 times larger, just let me pull out my calculator and your math can tell this solves a problem. From a human perspective, it's just like your heart stopping, there's a risk associated with that pump going down within the facility. >> Okay, so we're very tight on time. >> Sorry. >> That's okay. So, you got the five phases for five IoT projects, within phase three, predictive analytics. Let's run through them and ... >> The second one is video is a sensor, so this is-- >> Cool. >> Using video to detect things that are going on and using the Edge analytics to be able to power that. The third one is safety and security. So these are things like, man down. Directive response, those types of things. The fourth one is, connected worker. And I define this as, location-based context-aware content. So, just very quickly, if you have three different people at the pump. One is a operations person, one's a maintenance person, one's a finance person, and they're all using that augmented reality that we saw, they're going to see three different dashboards. Locations base, context-aware content. And then the fifth one is, we're going to tie into the two sister projects that are going on out there with the DCS upgrade and the aneo-spalatio mechanical integrity program, and do a full life cycle as that management. So these are big projects. >> Dave: So now you've got the fully instrumented refinery is where you're at. Now you got all this data flowing. What happens to the data? Where does it get analyzed, where does it end up? Where do you go from there? >> Sure, so of course, having the Edgeline servers there, we're doing data analytics on the Edge so we can have real time, right there information to help our workers work safely and efficiently. And then we have this wealth of historical data that we can start analyzing, either on-premise or off-premise, to help us-- >> JR: Help probe the models. >> Better. And then also, this is one really cool aspect from a Texmark perspective is, we do a significant amount of total processing. That means, somebody comes to us and says, "Here, Dave. Make this for us." And we will run it through our equipment and give them an end product. If we can improve the way we cook, whatever our process, whatever it is that they want, there is a significant value added to that. >> Dave: And that historical data, in the lake if you will, lives on Prim, it lives in the Cloud, or you don't know yet. >> Everything is on Prim. The Cloud applications that we'll probably use are around safety and security when talking about weather, humidity, and those types of things. >> Dave: So bring in some outside data or models that you apply. >> Right. Yes. Texmark is a single facility, so leveraging the Cloud to communicate to other locations and things like that isn't really a necessary driver. Although it would be, completely would be, for some of the target customers that we want to sell this to initially. >> But the vast majority of the data is staying at-- >> JR: On Prim, yeah. >> Correct? So, it confirms the assumptions that we've been making, that 90% of the data is this world is going to be analyzed at the Edge and maybe trickle some stuff back, some nuggets back to the Cloud. >> Absolutely. >> Guys, we got to go. That's a fascinating story. Thank you so much. >> Thanks, you could tell I like the camera a lot in this. Thank you, Dave, I really appreciate it. >> Dave: My pleasure, thank you. Alright, keep it right there, everybody. We'll be back with our next guest as The Cuber live from HPE Discover in Las Vegas, 2017. We'll be right back. (electronic music)

(intro music) >> "theCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> We're not going to- >> Hey everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante, I'm here with Dave Nicholson, day four of MWC23. I mean, it's Dave, it's, it's still really busy. And you walking the floors, you got to stop and start. >> It's surprising. >> People are cheering. They must be winding down, giving out the awards. Really excited. Pier, look at you and Elias here. He's the vice president of Engineering Technology for Edge Computing Offers Strategy and Execution at Dell Technologies, and he's joined by Dan Cummins, who's a fellow and vice president of, in the Edge Business Unit at Dell Technologies. Guys, welcome. >> Thank you. >> Thank you. >> I love when I see the term fellow. You know, you don't, they don't just give those away. What do you got to do to be a fellow at Dell? >> Well, you know, fellows are senior technical leaders within Dell. And they're usually tasked to help Dell solve you know, a very large business challenge to get to a fellow. There's only, I think, 17 of them inside of Dell. So it is a small crowd. You know, previously, really what got me to fellow, is my continued contribution to transform Dell's mid-range business, you know, VNX two, and then Unity, and then Power Store, you know, and then before, and then after that, you know, they asked me to come and, and help, you know, drive the technology vision for how Dell wins at the Edge. >> Nice. Congratulations. Now, Pierluca, I'm looking at this kind of cool chart here which is Edge, Edge platform by Dell Technologies, kind of this cube, like cubes course, you know. >> AK project from here. >> Yeah. So, so tell us about the Edge platform. What, what's your point of view on all that at Dell? >> Yeah, absolutely. So basically in a, when we create the Edge, and before even then was bringing aboard, to create this vision of the platform, and now building the platform when we announced project from here, was to create solution for the Edge. Dell has been at the edge for 30 years. We sold a lot of compute. But the reality was people want outcome. And so, and the Edge is a new market, very exciting, but very siloed. And so people at the Edge have different personas. So quickly realize that we need to bring in Dell, people with expertise, quickly realize as well that doing all these solution was not enough. There was a lot of problem to solve because the Edge is outside of the data center. So you are outside of the wall of the data center. And what is going to happen is obviously you are in the land of no one. And so you have million of device, thousand of million of device. All of us at home, we have all connected thing. And so we understand that the, the capability of Dell was to bring in technology to secure, manage, deploy, with zero touch, zero trust, the Edge. And all the edge the we're speaking about right now, we are focused on everything that is outside of a normal data center. So, how we married the computer that we have for many years, the new gateways that we create, so having the best portfolio, number one, having the best solution, but now, transforming the way that people deploy the Edge, and secure the Edge through a software platform that we create. >> You mentioned Project Frontier. I like that Dell started to do these sort of project, Project Alpine was sort of the multi-cloud storage. I call it "The Super Cloud." The Project Frontier. It's almost like you develop, it's like mission based. Like, "Okay, that's our North Star." People hear Project Frontier, they know, you know, internally what you're talking about. Maybe use it for external communications too, but what have you learned since launching Project Frontier? What's different about the Edge? I mean you're talking about harsh environments, you're talking about new models of connectivity. So, what have you learned from Project Frontier? What, I'd love to hear the fellow perspective as well, and what you guys are are learning so far. >> Yeah, I mean start and then I left to them, but we learn a lot. The first thing we learn that we are on the right path. So that's good, because every conversation we have, there is nobody say to us, you know, "You are crazy. "This is not needed." Any conversation we have this week, start with the telco thing. But after five minutes it goes to, okay, how I can solve the Edge, how I can bring the compute near where the data are created, and how I can do that secure at scale, and with the right price. And then can speak about how we're doing that. >> Yeah, yeah. But before that, we have to really back up and understand what Dell is doing with Project Frontier, which is an Edge operations platform, to simplify your Edge use cases. Now, Pierluca and his team have a number of verticalized applications. You want to be able to securely deploy those, you know, at the Edge. But you need a software platform that's going to simplify both the life cycle management, and the security at the Edge, with the ability to be able to construct and deploy distributed applications. Customers are looking to derive value near the point of generation of data. We see a massive explosion of data. But in particular, what's different about the Edge, is the different computing locations, and the constraints that are on those locations. You know, for example, you know, in a far Edge environment, the people that service that equipment are not trained in the IT, or train, trained in it. And they're also trained in the safety and security protocols of that environment. So you necessarily can't apply the same IT techniques when you're managing infrastructure and deploying applications, or servicing in those locations. So Frontier was designed to solve for those constraints. You know, often we see competitors that are doing similar things, that are starting from an IT mindset, and trying to shift down to cover Edge use cases. What we've done with Frontier, is actually first understood the constraints that they have at the Edge. Both the operational constraints and technology constraints, the service constraints, and then came up with a, an architecture and technology platform that allows them to start from the Edge, and bleed into the- >> So I'm laughing because you guys made the same mistake. And you, I think you learned from that mistake, right? You used to take X86 boxes and throw 'em over the fence. Now, you're building purpose-built systems, right? Project Frontier I think is an example of the learnings. You know, you guys an IT company, right? Come on. But you're learning fast, and that's what I'm impressed about. >> Well Glenn, of course we're here at MWC, so it's all telecom, telecom, telecom, but really, that's a subset of Edge. >> Yes. >> Fair to say? >> Yes. >> Can you give us an example of something that is, that is, orthogonal to, to telecom, you know, maybe off to the side, that maybe overlaps a little bit, but give us an, give us an example of Edge, that isn't specifically telecom focused. >> Well, you got the, the Edge verticals. and Pierluca could probably speak very well to this. You know, you got manufacturing, you got retail, you got automotive, you got oil and gas. Every single one of them are going to make different choices in the software that they're going to use, the hyperscaler investments that they're going to use, and then write some sort of automation, you know, to deploy that, right? And the Edge is highly fragmented across all of these. So we certainly could deploy a private wireless 5G solution, orchestrate that deployment through Frontier. We can also orchestrate other use cases like connected worker, or overall equipment effectiveness in manufacturing. But Pierluca you have a, you have a number. >> Well, but from your, so, but just to be clear, from your perspective, the whole idea of, for example, private 5g, it's a feature- >> Yes. >> That might be included. It happened, it's a network topology, a network function that might be a feature of an Edge environment. >> Yes. But it's not the center of the discussion. >> So, it enables the outcome. >> Yeah. >> Okay. >> So this, this week is a clear example where we confirm and establish this. The use case, as I said, right? They, you say correctly, we learned very fast, right? We brought people in that they came from industry that was not IT industry. We brought people in with the things, and we, we are Dell. So we have the luxury to be able to interview hundreds of customers, that just now they try to connect the OT with the IT together. And so what we learn, is really, at the Edge is different personas. They person that decide what to do at the Edge, is not the normal IT administrator, is not the normal telco. >> Who is it? Is it an engineer, or is it... >> It's, for example, the store manager. >> Yeah. >> It's, for example, the, the person that is responsible for the manufacturing process. Those people are not technology people by any means. But they have a business goal in mind. Their goal is, "I want to raise my productivity by 30%," hence, I need to have a preventive maintenance solution. How we prescribe this preventive maintenance solution? He doesn't prescribe the preventive maintenance solution. He goes out, he has to, a consult or himself, to deploy that solution, and he choose different fee. Now, the example that I was doing from the houses, all of us, we have connected device. The fact that in my house, I have a solar system that produce energy, the only things I care that I can read, how much energy I produce on my phone, and how much energy I send to get paid back. That's the only thing. The fact that inside there is a compute that is called Dell or other things is not important to me. Same persona. Now, if I can solve the security challenge that the SI, or the user need to implement this technology because it goes everywhere. And I can manage this in extensively, and I can put the supply chain of Dell on top of that. And I can go every part in the world, no matter if I have in Papua New Guinea, or I have an oil ring in Texas, that's the winning strategy. That's why people, they are very interested to the, including Telco, the B2B business in telco is looking very, very hard to how they recoup the investment in 5g. One of the way, is to reach out with solution. And if I can control and deploy things, more than just SD one or other things, or private mobility, that's the key. >> So, so you have, so you said manufacturing, retail, automotive, oil and gas, you have solutions for each of those, or you're building those, or... >> Right now we have solution for manufacturing, with for example, PTC. That is the biggest company. It's actually based in Boston. >> Yeah. Yeah, it is. There's a company that the market's just coming right to them. >> We have a, very interesting. Another solution with Litmus, that is a startup that, that also does manufacturing aggregation. We have retail with Deep North. So we can do detecting in the store, how many people they pass, how many people they doing, all of that. And all theses solution that will be, when we will have Frontier in the market, will be also in Frontier. We are also expanding to energy, and we going vertical by vertical. But what is they really learn, right? You said, you know you are an IT company. What, to me, the Edge is a pre virtualization area. It's like when we had, you know, I'm, I've been in the company for 24 years coming from EMC. The reality was before there was virtualization, everybody was starting his silo. Nobody thought about, "Okay, I can run this thing together "with security and everything, "but I need to do it." Because otherwise in a manufacturing, or in a shop, I can end up with thousand of devices, just because someone tell to me, I'm a, I'm a store manager, I don't know better. I take this video surveillance application, I take these things, I take a, you know, smart building solution, suddenly I have five, six, seven different infrastructure to run this thing because someone say so. So we are here to democratize the Edge, to secure the Edge, and to expand. That's the idea. >> So, the Frontier platform is really the horizontal platform. And you'll build specific solutions for verticals. On top of that, you'll, then I, then the beauty is ISV's come in. >> Yes. >> 'Cause it's open, and the developers. >> We have a self certification program already for our solution, as well, for the current solution, but also for Frontier. >> What does that involve? Self-certification. You go through you, you go through some- >> It's basically a, a ISV can come. We have a access to a lab, they can test the thing. If they pass the first screen, then they can become part of our ecosystem very easily. >> Ah. >> So they don't need to spend days or months with us to try to architect the thing. >> So they get the premature of being certified. >> They get the Dell brand associated with it. Maybe there's some go-to-market benefits- >> Yes. >> As well. Cool. What else do we need to know? >> So, one thing I, well one thing I just want to stress, you know, when we say horizontal platform, really, the Edge is really a, a distributed edge computing problem, right? And you need to almost create a mesh of different computing locations. So for example, even though Dell has Edge optimized infrastructure, that we're going to deploy and lifecycle manage, customers may also have compute solutions, existing compute solutions in their data center, or at a co-location facility that are compute destinations. Project Frontier will connect to those private cloud stacks. They'll also collect to, connect to multiple public cloud stacks. And then, what they can do, is the solutions that we talked about, they construct that using an open based, you know, protocol, template, that describes that distributed application that produces that outcome. And then through orchestration, we can then orchestrate across all of these locations to produce that outcome. That's what the platform's doing. >> So it's a compute mesh, is what you just described? >> Yeah, it's, it's a, it's a software orchestration mesh. >> Okay. >> Right. And allows customers to take advantage of their existing investments. Also allows them to, to construct solutions based on the ISV of their choice. We're offering solutions like Pierluca had talked about, you know, in manufacturing with Litmus and PTC, but they could put another use case that's together based on another ISV. >> Is there a data mesh analog here? >> The data mesh analog would run on top of that. We don't offer that as part of Frontier today, but we do have teams working inside of Dell that are working on this technology. But again, if there's other data mesh technology or packages, that they want to deploy as a solution, if you will, on top of Frontier, Frontier's extensible in that way as well. >> The open nature of Frontier is there's a, doesn't, doesn't care. It's just a note on the mesh. >> Yeah. >> Right. Now, of course you'd rather, you'd ideally want it to be Dell technology, and you'll make the business case as to why it should be. >> They get additional benefits if it's Dell. Pierluca talked a lot about, you know, deploying infrastructure outside the walls of an IT data center. You know, this stuff can be tampered with. Somebody can move it to another room, somebody can open up. In the supply chain with, you know, resellers that are adding additional people, can open these devices up. We're actually deploying using an Edge technology called Secure Device Onboarding. And it solves a number of things for us. We, as a manufacturer can initialize the roots of trust in the Dell hardware, such that we can validate, you know, tamper detection throughout the supply chain, and securely transfer ownership. And that's different. That is not an IT technique. That's an edge technique. And that's just one example. >> That's interesting. I've talked to other people in IT about how they're using that technique. So it's, it's trickling over to that side of the business. >> I'm almost curious about the friction that you, that you encounter because the, you know, you paint a picture of a, of a brave new world, a brave new future. Ideally, in a healthy organization, they have, there's a CTO, or at least maybe a CIO, with a CTO mindset. They're seeking to leverage technology in the service of whatever the mission of the organization is. But they've got responsibilities to keep the lights on, as well as innovate. In that mix, what are you seeing as the inhibitors? What's, what's the push back against Frontier that you're seeing in most cases? Is it, what, what is it? >> Inside of Dell? >> No, not, I'm saying out, I'm saying with- >> Market friction. >> Market, market, market friction. What is the push back? >> I think, you know, as I explained, do yourself is one of the things that probably is the most inhibitor, because some people, they think that they are better already. They invest a lot in this, and they have the content. But those are again, silo solutions. So, if you go into some of the huge things that they already established, thousand of store and stuff like that, there is an opportunity there, because also they want to have a refresh cycle. So when we speak about softer, softer, softer, when you are at the Edge, the software needs to run on something that is there. So the combination that we offer about controlling the security of the hardware, plus the operating system, and provide an end-to-end platform, allow them to solve a lot of problems that today they doing by themselves. Now, I met a lot of customers, some of them, one actually here in Spain, I will not make the name, but it's a large automotive. They have the same challenge. They try to build, but the problem is this is just for them. And they want to use something that is a backup and provide with the Dell service, Dell capability of supply chain in all the world, and the diversity of the portfolio we have. These guys right now, they need to go out and find different types of compute, or try to adjust thing, or they need to have 20 people there to just prepare the device. We will take out all of this. So I think the, the majority of the pushback is about people that they already established infrastructure, and they want to use that. But really, there is an opportunity here. Because the, as I said, the IT/OT came together now, it's a reality. Three years ago when we had our initiative, they've pointed out, sarcastically. We, we- >> Just trying to be honest. (laughing) >> I can't let you get away with that. >> And we, we failed because it was too early. And we were too focused on, on the fact to going. Push ourself to the boundary of the IOT. This platform is open. You want to run EdgeX, you run EdgeX, you want OpenVINO, you want Microsoft IOT, you run Microsoft IOT. We not prescribe the top. We are locking down the bottom. >> What you described is the inertia of, of sunk dollars, or sunk euro into an infrastructure, and now they're hanging onto that. >> Yeah. >> But, I mean, you know, I, when we say horizontal, we think scale, we think low cost, at volume. That will, that will win every time. >> There is a simplicity at scale, right? There is a, all the thing. >> And the, and the economics just overwhelm that siloed solution. >> And >> That's inevitable. >> You know, if you want to apply security across the entire thing, if you don't have a best practice, and a click that you can do that, or bring down an application that you need, you need to touch each one of these silos. So, they don't know yet, but we going to be there helping them. So there is no pushback. Actually, this particular example I did, this guy said you know, there are a lot of people that come here. Nobody really described the things we went through. So we are on the right track. >> Guys, great conversation. We really appreciate you coming on "theCUBE." >> Thank you. >> Pleasure to have you both. >> Okay. >> Thank you. >> All right. And thank you for watching Dave Vellante for Dave Nicholson. We're live at the Fira. We're winding up day four. Keep it right there. Go to siliconangle.com. John Furrier's got all the news on "theCUBE.net." We'll be right back right after this break. "theCUBE," at MWC 23. (outro music)

(gentle music) >> Announcer: "theCube's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) (logo whooshing) >> Hey, everyone, welcome back to "theCube's" coverage of day one, MWC23 live from Barcelona, Lisa Martin here with Dave Vellante. Dave, we've got some great conversations so far This is the biggest, most packed show I've been to in years. About 80,000 people here so far. >> Yeah, down from its peak of 108, but still pretty good. You know, a lot of folks from China come to this show, but with the COVID situation in China, that's impacted the attendance, but still quite amazing. >> Amazing for sure. We're going to be talking about trends and mobility, and all sorts of great things. We have a couple of guests joining us for the first time on "theCUBE." Please welcome Dr. Srinivas Mukkamala or Sri, chief product officer at Ivanti. And Dave Shepherd, VP Ivanti. Guys, welcome to "theCUBE." Great to have you here. >> Thank you. >> So, day one of the conference, Sri, we'll go to you first. Talk about some of the trends that you're seeing in mobility. Obviously, the conference renamed from Mobile World Congress to MWC mobility being part of it, but what are some of the big trends? >> It's interesting, right? I mean, I was catching up with Dave. The first thing is from the keynotes, it took 45 minutes to talk about security. I mean, it's quite interesting when you look at the shore floor. We're talking about Edge, we're talking about 5G, the whole evolution. And there's also the concept of are we going into the Cloud? Are we coming back from the Cloud, back to the Edge? They're really two different things. Edge is all decentralized while you recompute. And one thing I observed here is they're talking about near real-time reality. When you look at automobiles, when you look at medical, when you look at robotics, you can't have things processed in the Cloud. It'll be too late. Because you got to make millisecond-based stations. That's a big trend for me. When I look at staff... Okay, the compute it takes to process in the Cloud versus what needs to happen on-prem, on device, is going to revolutionize the way we think about mobility. >> Revolutionize. David, what are some of the things that you're saying? Do you concur? >> Yeah, 100%. I mean, look, just reading some of the press recently, they're predicting 22 billion IoT devices by 2024. Everything Sri just talked about there. It's growing exponentially. You know, problems we have today are a snapshot. We're probably in the slowest place we are today. Everything's just going to get faster and faster and faster. So it's a, yeah, 100% concur with that. >> You know, Sri, on your point, so Jose Maria Alvarez, the CEO of Telefonica, said there are three pillars of the future of telco, low latency, programmable networks, and Cloud and Edge. So, as to your point, Cloud and low latency haven't gone hand in hand. But the Cloud guys are saying, "All right, we're going to bring the Cloud to the Edge." That's sort of an interesting dynamic. We're going to bypass them. We heard somebody, another speaker say, "You know, Cloud can't do it alone." You know? (chuckles) And so, it's like these worlds need each other in a way, don't they? >> Definitely right. So that's a fantastic way to look at it. The Cloud guys can say, "We're going to come closer to where the computer is." And if you really take a look at it with data localization, where are we going to put the Cloud in, right? I mean, so the data sovereignty becomes a very interesting thing. The localization becomes a very interesting thing. And when it comes to security, it gets completely different. I mean, we talked about moving everything to a centralized compute, really have massive processing, and give you the addition back wherever you are. Whereas when you're localized, I have to process everything within the local environment. So there's already a conflict right there. How are we going to address that? >> Yeah. So another statement, I think, it was the CEO of Ericsson, he was kind of talking about how the OTT guys have heard, "We can't let that happen again. And we're going to find new ways to charge for the network." Basically, he's talking about monetizing the API access. But I'm interested in what you're hearing from customers, right? 'Cause our mindset is, what value you're going to give to customers that they're going to pay for, versus, "I got this data I'm going to charge developers for." But what are you hearing from customers? >> It's amazing, Dave, the way you're looking at it, right? So if we take a look at what we were used to perpetual, and we said we're going to move to a subscription, right? I mean, everybody talks about subscription economy. Telcos on the other hand, had subscription economy for a long time, right? They were always based on usage, right? It's a usage economy. But today, we are basically realizing on compute. We haven't even started charging for compute. If you go to AWS, go to Azure, go to GCP, they still don't quite charge you for actual compute, right? It's kind of, they're still leaning on it. So think about API-based, we're going to break the bank. What people don't realize is, we do millions of API calls for any high transaction environment. A consumer can't afford that. What people don't realize is... I don't know how you're going to monetize. Even if you charge a cent a call, that is still going to be hundreds and thousands of dollars a day. And that's where, if you look at what you call low-code no-code motion? You see a plethora of companies being built on that. They're saying, "Hey, you don't have to write code. I'll give you authentication as a service. What that means is, Every single time you call my API to authenticate a user, I'm going to charge you." So just imagine how many times we authenticate on a single day. You're talking a few dozen times. And if I have to pay every single time I authenticate... >> Real friction in the marketplace, David. >> Yeah, and I tell you what. It's a big topic, right? And it's a topic that we haven't had to deal with at the Edge before, and we hear it probably daily really, complexity. The complexity's growing all the time. That means that we need to start to get insight, visibility. You know? I think a part of... Something that came out of the EU actually this week, stated, you know, there's a cyber attack every 11 seconds. That's fast, right? 2016, that was 40 seconds. So actually that speed I talked about earlier, everything Sri says that's coming down to the Edge, we want to embrace the Edge and that is the way we're going to move. But customers are mindful of the complexity that's involved in that. And that, you know, lens thought to how are we going to deal with those complexities. >> I was just going to ask you, how are you planning to deal with those complexities? You mentioned one ransomware attack every 11 seconds. That's down considerably from just a few years ago. Ransomware is a household word. It's no longer, "Are we going to get attacked?" It's when, it's to what extent, it's how much. So how is Ivanti helping customers deal with some of the complexities, and the changes in the security landscape? >> Yeah. Shall I start on that one first? Yeah, look, we want to give all our customers and perspective customers full visibility of their environment. You know, devices that are attached to the environment. Where are they? What are they doing? How often are we going to look for those devices? Not only when we find those devices. What applications are they running? Are those applications secure? How are we going to manage those applications moving forward? And overall, wrapping it round, what kind of service are we going to do? What processes are we going to put in place? To Sri's point, the low-code no-code angle. How do we build processes that protect our organization? But probably a point where I'll pass to Sri in a moment is how do we add a level of automation to that? How do we add a level of intelligence that doesn't always require a human to be fixing or remediating a problem? >> To Sri, you mentioned... You're right, the keynote, it took 45 minutes before it even mentioned security. And I suppose it's because they've historically, had this hardened stack. Everything's controlled and it's a safe environment. And now that's changing. So what would you add? >> You know, great point, right? If you look at telcos, they're used to a perimeter-based network. >> Yep. >> I mean, that's what we are. Boxed, we knew our perimeter. Today, our perimeter is extended to our home, everywhere work, right? >> Yeah- >> We don't have a definition of a perimeter. Your browser is the new perimeter. And a good example, segueing to that, what we have seen is horizontal-based security. What we haven't seen is verticalization, especially in mobile. We haven't seen vertical mobile security solutions, right? Yes, you hear a little bit about automobile, you hear a little bit about healthcare, but what we haven't seen is, what about food sector? What about the frontline in food? What about supply chain? What security are we really doing? And I'll give you a simple example. You brought up ransomware. Last night, Dole was attacked with ransomware. We have seen the beef producer colonial pipeline. Now, if we have seen agritech being hit, what does it mean? We are starting to hit humanity. If you can't really put food on the table, you're starting to really disrupt the supply chain, right? In a massive way. So you got to start thinking about that. Why is Dole related to mobility? Think about that. They don't carry service and computers. What they carry is mobile devices. that's where the supply chain works. And then that's where you have to start thinking about it. And the evolution of ransomware, rather than a single-trick pony, you see them using multiple vulnerabilities. And Pegasus was the best example. Spyware across all politicians, right? And CEOs. It is six or seven vulnerabilities put together that actually was constructed to do an attack. >> Yeah. How does AI kind of change this? Where does it fit in? The attackers are going to have AI, but we could use AI to defend. But attackers are always ahead, right? (chuckles) So what's your... Do you have a point of view on that? 'Cause everybody's crazy about ChatGPT, right? The banks have all banned it. Certain universities in the United States have banned it. Another one's forcing his students to learn how to use ChatGPT to prompt it. It's all over the place. You have a point of view on this? >> So definitely, Dave, it's a great point. First, we all have to have our own generative AI. I mean, I look at it as your digital assistant, right? So when you had calculators, you can't function without a calculator today. It's not harmful. It's not going to take you away from doing multiplication, right? So we'll still teach arithmetic in school. You'll still use your calculator. So to me, AI will become an integral part. That's one beautiful thing I've seen on the short floor. Every little thing there is a AI-based solution I've seen, right? So ChatGPT is well played from multiple perspective. I would rather up level it and say, generated AI is the way to go. So there are three things. There is human intense triaging, where humans keep doing easy work, minimal work. You can use ML and AI to do that. There is human designing that you need to do. That's when you need to use AI. >> But, I would say this, in the Enterprise, that the quality of the AI has to be better than what we've seen so far out of ChatGPT, even though I love ChatGPT, it's amazing. But what we've seen from being... It's got to be... Is it true that... Don't you think it has to be cleaner, more accurate? It can't make up stuff. If I'm going to be automating my network with AI. >> I'll answer that question. It comes down to three fundamentals. The reason ChatGPT is giving addresses, it's not trained on the latest data. So for any AI and ML method, you got to look at three things. It's your data, it's your domain expertise, who is training it, and your data model. In ChatGPT, it's older data, it's biased to the people that trained it, right? >> Mm-hmm. >> And then, the data model is it's going to spit out what it's trained on. That's a precursor of any GPT, right? It's pre-trained transformation. >> So if we narrow that, right? Train it better for the specific use case, that AI has huge potential. >> You flip that to what the Enterprise customers talk about to us is, insight is invaluable. >> Right. >> But then too much insight too quickly all the time means we go remediation crazy. So we haven't got enough humans to be fixing all the problems. Sri's point with the ChatGPT data, some of that data we are looking at there could be old. So we're trying to triage something that may still be an issue, but it might have been superseded by something else as well. So that's my overriding when I'm talking to customers and we talk ChatGPT, it's in the news all the time. It's very topical. >> It's fun. >> It is. I even said to my 13-year-old son yesterday, your homework's out a date. 'Cause I knew he was doing some summary stuff on ChatGPT. So a little wind up that's out of date just to make that emphasis around the model. And that's where we, with our Neurons platform Ivanti, that's what we want to give the customers all the time, which is the real-time snapshot. So they can make a priority or a decision based on what that information is telling them. >> And we've kind of learned, I think, over the last couple of years, that access to real-time data, real-time AI, is no longer nice to have. It's a massive competitive advantage for organizations, but it's going to enable the on-demand, everything that we expect in our consumer lives, in our business lives. This is going to be table stakes for organizations, I think, in every industry going forward. >> Yeah. >> But assumes 5G, right? Is going to actually happen and somebody's going to- >> Going to absolutely. >> Somebody's going to make some money off it at some point. When are they going to make money off of 5G, do you think? (all laughing) >> No. And then you asked a very good question, Dave. I want to answer that question. Will bad guys use AI? >> Yeah. Yeah. >> Offensive AI is a very big thing. We have to pay attention to it. It's got to create an asymmetric war. If you look at the president of the United States, he said, "If somebody's going to attack us on cyber, we are going to retaliate." For the first time, US is willing to launch a cyber war. What that really means is, we're going to use AI for offensive reasons as well. And we as citizens have to pay attention to that. And that's where I'm worried about, right? AI bias, whether it's data, or domain expertise, or algorithmic bias, is going to be a big thing. And offensive AI is something everybody have to pay attention to. >> To your point, Sri, earlier about critical infrastructure getting hacked, I had this conversation with Dr. Robert Gates several years ago, and I said, "Yeah, but don't we have the best offensive, you know, technology in cyber?" And he said, "Yeah, but we got the most to lose too." >> Yeah, 100%. >> We're the wealthiest nation of the United States. The wealthiest is. So you got to be careful. But to your point, the president of the United States saying, "We'll retaliate," right? Not necessarily start the war, but who started it? >> But that's the thing, right? Attribution is the hardest part. And then you talked about a very interesting thing, rich nations, right? There's emerging nations. There are nations left behind. One thing I've seen on the show floor today is, digital inequality. Digital poverty is a big thing. While we have this amazing technology, 90% of the world doesn't have access to this. >> Right. >> What we have done is we have created an inequality across, and especially in mobility and cyber, if this technology doesn't reach to the last mile, which is emerging nations, I think we are creating a crater back again and putting societies a few miles back. >> And at much greater risk. >> 100%, right? >> Yeah. >> Because those are the guys. In cyber, all you need is a laptop and a brain to attack. >> Yeah. Yeah. >> If I don't have it, that's where the civil war is going to start again. >> Yeah. What are some of the things in our last minute or so, guys, David, we'll start with you and then Sri go to you, that you're looking forward to at this MWC? The theme is velocity. We're talking about so much transformation and evolution in the telecom industry. What are you excited to hear and learn in the next couple of days? >> Just getting a complete picture. One is actually being out after the last couple of years, so you learn a lot. But just walking around and seeing, from my perspective, some vendor names that I haven't seen before, but seeing what they're doing and bringing to the market. But I think goes back to the point made earlier around APIs and integration. Everybody's talking about how can we kind of do this together in a way. So integrations, those smart things is what I'm kind of looking for as well, and how we plug into that as well. >> Excellent, and Sri? >> So for us, there is a lot to offer, right? So while I'm enjoying what I'm seeing here, I'm seeing at an opportunity. We have an amazing portfolio of what we can do. We are into mobile device management. We are the last (indistinct) company. When people find problems, somebody has to go remediators. We are the world's largest patch management company. And what I'm finding is, yes, all these people are embedding software, pumping it like nobody's business. As you find one ability, somebody has to go fix them, and we want to be the (indistinct) company. We had the last smile. And I find an amazing opportunity, not only we can do device management, but do mobile threat defense and give them a risk prioritization on what needs to be remediated, and manage all that in our ITSM. So I look at this as an amazing, amazing opportunity. >> Right. >> Which is exponential than what I've seen before. >> So last question then. Speaking of opportunities, Sri, for you, what are some of the things that customers can go to? Obviously, you guys talk to customers all the time. In terms of learning what Ivanti is going to enable them to do, to take advantage of these opportunities. Any webinars, any events coming up that we want people to know about? >> Absolutely, ivanti.com is the best place to go because we keep everything there. Of course, "theCUBE" interview. >> Of course. >> You should definitely watch that. (all laughing) No. So we have quite a few industry events we do. And especially there's a lot of learning. And we just raised the ransomware report that actually talks about ransomware from a global index perspective. So one thing what we have done is, rather than just looking at vulnerabilities, we showed them the weaknesses that led to the vulnerabilities, and how attackers are using them. And we even talked about DHS, how behind they are in disseminating the information and how it's actually being used by nation states. >> Wow. >> And we did cover mobility as a part of that as well. So there's a quite a bit we did in our report and it actually came out very well. >> I have to check that out. Ransomware is such a fascinating topic. Guys, thank you so much for joining Dave and me on the program today, sharing what's going on at Ivanti, the changes that you're seeing in mobile, and the opportunities that are there for your customers. We appreciate your time. >> Thank you >> Thank you. >> Yes. Thanks, guys. >> Thanks, guys. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching "theCUBE" live from MWC23 in Barcelona. As you know, "theCUBE" is the leader in live tech coverage. Dave and I will be right back with our next guest. (gentle upbeat music)

(upbeat music) >> Hello and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier with a special guest here in the studio. As part of our Cloud Native SecurityCon Coverage we had an opportunity to bring in Jon Turow who is the partner at Madrona Venture Partners formerly with AWS and to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm. A million downloads, fastest to the million downloads there. Before some were saying it's just a gimmick. Others saying it's a game changer. Jon's here to break it down, and great to have you on. Thanks for coming in. >> Thanks John. Glad to be here. >> Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, a great firm doing great deals, and they had this future at modern application kind of thesis. Now you are putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in, this AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational big movement into AI? What's your general reaction to all this? >> So, thanks, Jon, again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing. Always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm, Madrona Venture Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. And I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible, that makes them more useful for building applications than what we've seen before. And so that, we can talk about the depths of it, but in a nutshell, that's why I think people are really excited. >> And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been a multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like they're scratching that itch. Every year is going to be, and it's like the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things where required high forms computing. But now there's no real bottleneck to compute. You got cloud. And so you're starting to see the emergence of a massive acceleration of where AI's been and where it needs to be going. Now, it's almost like it's got a reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening. Cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I overreading that? Is that right? >> You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was, 2006, and offered more compute to more people than really was possible before. Initially that was about taking existing applications and running them more easily in a bigger scale. But in that period of time what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came of that is something called the transformer AI model architecture. And Google came out with that, published a paper in 2017. And what that says is, with a transformer model you can actually train an arbitrarily large amount of data into a model, and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside a domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like ChatGPT. That's the underlying term. We don't as an industry quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. >> So one of the debates we had on theCUBE at our Supercloud event last month was, what's the barriers to entry for say OpenAI, for instance? Obviously, I weighed in aggressively and said, "The barriers for getting into cloud are high because all the CapEx." And Howie Xu formerly VMware, now at ZScaler, he's an AI machine learning guy. He was like, "Well, you can spend $100 million and replicate it." I saw a quote that set up for 180,000 I can get this other package. What's the barriers to entry? Is ChatGPT or OpenAI, does it have sustainability? Is it easy to get into? What is the market like for AI? I mean, because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening, Seattle's booming, Boston with MIT's been working on neural networks for generations. That's what we've found the answer. Get off the neural network, Boston jump on the AI bus. So there's total excitement for this. People are enthusiastic around this area. >> You can think of an iPhone versus Android tension that's happening today. In the iPhone world, there are proprietary models from OpenAI who you might consider as the leader. There's Cohere, there's AI21, there's Anthropic, Google's going to have their own, and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in Hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're going to see teams choose sides based on whether they want speed or flexibility. >> That's interesting. And that brings up a point I was talking to a startup and the debate was, do you abstract away from the hardware and be software-defined or software-led on the AI side and let the hardware side just extremely accelerate on its own, 'cause it's flywheel? So again, back to proprietary, that's with hardware kind of bundled in, bolted on. Is it accelerator or is it bolted on or is it part of it? So to me, I think that the big struggle in understanding this is that which one will end up being right. I mean, is it a beta max versus VHS kind of thing going on? Or iPhone, Android, I mean iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? >> In proprietary models, here does seem to be a jump ball. That there's going to be a virtuous flywheel that emerges that, for example, all these excitement about ChatGPT. What's really exciting about it is it's really easy to use. The technology isn't so different from what we've seen before even from OpenAI. You mentioned a million users in a short period of time, all providing training data for OpenAI that makes their underlying models, their next generation even better. So it's not unreasonable to guess that there's going to be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux, there seems to be gravity towards this yin and yang. And my guess, and what other people seem to think is going to be the case is that we're going to continue to see these two poles of AI. >> So let's get into the relationship with data because I've been emerging myself with ChatGPT, fascinated by the ease of use, yes, but also the fidelity of how you query it. And I felt like when I was doing writing SQL back in the eighties and nineties where SQL was emerging. You had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into ChatGPT is a good thing if you know how to talk to it. Labeling whether your input is and it does a great job if you feed it right. If you ask a generic questions like Google. It's like a Google search. It gives you great format, sounds credible, but the facts are kind of wrong. >> That's right. >> That's where general consensus is coming on. So what does that mean? That means people are on one hand saying, "Ah, it's bullshit 'cause it's wrong." But I look at, I'm like, "Wow, that's that's compelling." 'Cause if you feed it the right data, so now we're in the data modeling here, so the role of data's going to be critical. Is there a data operating system emerging? Because if this thing continues to go the way it's going you can almost imagine as you would look at companies to invest in. Who's going to be right on this? What's going to scale? What's sustainable? What could build a durable company? It might not look what like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal. But it was the best organic search on the planet became successful. So I'm trying to figure out like, okay, how do you read this? How do you read the tea leaves? >> Yeah. There are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that that we're proud to be investors in called RunwayML that provides video accelerated, sorry, AI accelerated video editing capabilities. They were used in everything, everywhere all at once and some others. In order to build RunwayML, they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all that. But not every team has those capabilities, maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they shape on behalf of their own users exactly how the prompt to get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google, we consumers are going to start to get more intuitive about this. That's one. The second is what's called information retrieval. How can I get information about the world outside, information from a database or a data store or whatever service into these models so they can reason about them. And the third is, this is going to sound funny, but attribution. Just like you would do in a news report or an academic paper. If you can state where your facts are coming from, the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering, that's retrieval, and that's attribution. >> So that brings me to my next point I want to dig in on is the foundational model stack that you published. And I'll start by saying that with ChatGPT, if you take out the naysayers who are like throwing cold water on it about being a gimmick or whatever, and then you got the other side, I would call the alpha nerds who are like they can see, "Wow, this is amazing." This is truly NextGen. This isn't yesterday's chatbot nonsense. They're like, they're all over it. It's that everybody's using it right now in every vertical. I heard someone using it for security logs. I heard a data center, hardware vendor using it for pushing out appsec review updates. I mean, I've heard corner cases. We're using it for theCUBE to put our metadata in. So there's a horizontal use case of value. So to me that tells me it's a market there. So when you have horizontal scalability in the use case you're going to have a stack. So you publish this stack and it has an application at the top, applications like Jasper out there. You're seeing ChatGPT. But you go after the bottom, you got silicon, cloud, foundational model operations, the foundational models themselves, tooling, sources, actions. Where'd you get this from? How'd you put this together? Did you just work backwards from the startups or was there a thesis behind this? Could you share your thoughts behind this foundational model stack? >> Sure. Well, I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve. And so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona, I think about what technology and resources does she need to be really powerful, to be able to take a brilliant idea, and actually bring that to life. And if you spend time with that community, which I do and I've met with hundreds of founders now who are trying to do exactly this, you can see that the stack is emerging. In fact, we first drew it in, not in January 2023, but October 2022. And if you look at the difference between the October '22 and January '23 stacks you're going to see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand from the developers at the top of the stack. >> If you look at the young generation coming out and even some of the analysts, I was just reading an analyst report on who's following the whole data stacks area, Databricks, Snowflake, there's variety of analytics, realtime AI, data's hot. There's a lot of engineers coming out that were either data scientists or I would call data platform engineering folks are becoming very key resources in this area. What's the skillset emerging and what's the mindset of that entrepreneur that sees the opportunity? How does these startups come together? Is there a pattern in the formation? Is there a pattern in the competency or proficiency around the talent behind these ventures? >> Yes. I would say there's two groups. The first is a very distinct pattern, John. For the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology. And since about 2017, with the rise of the transformer architecture in these foundation models, that pattern has reversed. All of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves. So that's one. And what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet. That's one. The second is there is a broader population of people who by definition has even more collective imagination 'cause there's even more people who sees what should be possible and can use things like the proprietary models, like the OpenAI models that are available off the shelf and try to create something that maybe nobody has seen before. And when they do that, Jasper AI is a great example of that. Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3. They do that and it's really useful and it's almost fun for a marketer to use that. But there are going to be questions of how they can defend that against someone else who has access to the same technology. It's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the the silicon and the science. >> Yeah, and it's going to be also opportunity recognition is one thing. Building a viable venture product market fit. You got competition. And so when things get crowded you got to have some differentiation. I think that's going to be the key. And that's where I was trying to figure out and I think data with scale I think are big ones. Where's the vulnerability in the stack in terms of gaps? Where's the white space? I shouldn't say vulnerability. I should say where's the opportunity, where's the white space in the stack that you see opportunities for entrepreneurs to attack? >> I would say there's two. At the application level, there is almost infinite opportunity, John, because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology. And so if there is a kind of application in almost any vertical, it's hard to rule something out. Almost any vertical that a founder wishes she had created the original app in, well, now it's her time. So that's one. The second is, if you look at the tooling layer that we discussed, tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves. And the tooling layer is still forming. This is the interface between the models themselves and the applications. Tools that help bring in data, as you mentioned, connect to external actions, bring context across multiple calls, chain together multiple models. These kinds of things, there's huge opportunity there. >> Well, Jon, I really appreciate you coming in. I had a couple more questions, but I will take a minute to read some of your bios for the audience and we'll get into, I won't embarrass you, but I want to set the context. You said you were recovering product manager, 10 plus years at AWS. Obviously, recovering from AWS, which is a whole nother dimension of recovering. In all seriousness, I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started. So you guys started seeing the wave coming in early on. So I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth. From a tech perspective, it was pretty clear what was coming, so you were there when this tsunami hit. >> Jon: That's right. >> And you had a front row seat building tech, you were led the product teams for Computer Vision AI, Textract, AI intelligence for document processing, recognition for image and video analysis. You wrote the business product plan for AWS IoT and Greengrass, which we've covered a lot in theCUBE, which extends out to the whole edge thing. So you know a lot about AI/ML, edge computing, IOT, messaging, which I call the law of small numbers that scale become big. This is a big new thing. So as a former AWS leader who's been there and at Madrona, what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs as you got the stack? What's the big picture? What are you looking for? What's the thesis? How do you see this next five years emerging? >> Five years is a really long time given some of this science is only six months out. I'll start with some, no pun intended, some foundational things. And we can talk about some implications of the technology. The basics are the same as they've always been. We want, what I like to call customers with their hair on fire. So they have problems, so urgent they'll buy half a product. The joke is if your hair is on fire you might want a bucket of cold water, but you'll take a tennis racket and you'll beat yourself over the head to put the fire out. You want those customers 'cause they'll meet you more than halfway. And when you find them, you can obsess about them and you can get better every day. So we want customers with their hair on fire. We want founders who have empathy for those customers, understand what is going to be required to serve them really well, and have what I like to call founder-market fit to be able to build the products that those customers are going to need. >> And because that's a good strategy from an emerging, not yet fully baked out requirements definition. >> Jon: That's right. >> Enough where directionally they're leaning in, more than in, they're part of the product development process. >> That's right. And when you're doing early stage development, which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it. But if you have customers with their hair on fire and talented founders who have the capability to serve those customers, that's got me interested. >> So if I'm an entrepreneur, I walk in and say, "I have customers that have their hair on fire." What kind of checks do you write? What's the kind of the average you're seeing for seed and series? Probably seed, seed rounds and series As. >> It can depend. I have seen seed rounds of double digit million dollars. I have seen seed rounds much smaller than that. It really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says, "Look, we have this customer with her hair on fire. We think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out. And then we're going to have something really interesting that we can scale up from there and we can make the future happen. >> So it sounds like your advice to founders is go out and find some customers, show them a product, don't obsess over full completion, get some sort of vibe on fit and go from there. >> Yeah, and I think by the time founders come to me they may not have a product, they may not have a deck, but if they have a customer with her hair on fire, then I'm really interested. >> Well, I always love the professional services angle on these markets. You go in and you get some business and you understand it. Walk away if you don't like it, but you see the hair on fire, then you go in product mode. >> That's right. >> All Right, Jon, thank you for coming on theCUBE. Really appreciate you stopping by the studio and good luck on your investments. Great to see you. >> You too. >> Thanks for coming on. >> Thank you, Jon. >> CUBE coverage here at Palo Alto. I'm John Furrier, your host. More coverage with CUBE Conversations after this break. (upbeat music)