>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS public sector online. (bright upbeat music) Brought to you by Amazon Web Services. >> Hi, and welcome back to theCUBE's coverage at AWS Public Sector Online, I'm your host Stu Miniman. Really excited always when we come to the AWS public sector show it's not only governments' but you've got nonprofits education, and lots of phenomenal use cases from the practitioners themselves. Really happy to welcome to the program, Dr. Sergio papa. He is a radio diagnostic specialist at the Centro Diagnostico Italiano, of course, in Italy, if you can't tell by the name there, and Dr. Papa, thank you so much for joining us. Why don't you start with a little bit, you know your role at CDI. >> Thank you for your invitation. And I am the director of the diagnostic imaging department and radiotherapy nuclear medicine. We are a very huge institution in the diagnostic area in the laboratory and diagnostic imaging, I think one of the biggest in Europe. >> Excellent. And, of course, one of the very relevant things to talk about, you have a project called Artificial Intelligent or AI for COVID. maybe explain to us a little bit about what led to this and how this what the goals are for this project. >> Yes, we, as you know, is you also are today we were imagining February, March, we're in the middle of the biggest bandemia we have ever experienced it in the past. So we were thinking about some new (mumbles) methodology to give an end for this portrait emergency (mumbles) is working from three or four years around (mumbles) in basic imaging. In particular, we are working very hard in radiomics. After if you need I can talk, I can speak about the radiomics methodology that we are using. So, we had the idea of fine radiomics method on diagnostic imagery and in particular chest X ray and with the with the purpose not to have the diagnosis for this patient, it doesn't matter for us. We were focusing on predicting the clinical outcome of this patient. And, I mean, all these, all the people already diagnosed with COVID-19 viewers where they had an X ray examination, then after we applied over this huge number of X ray examinations radiomic ometers to understand what could be the clear output of this patient. So, I mean, dividing the people going well and then people, otherwise, that we're going to averse of the illness, I mean, to critical therapy even to the death. So we were trying to we are trying to divide two groups of patients. >> Yeah, absolutely. It's so important, of course, one to have that diagnosis, to understand who needs the most treatment, making sure that, you know, hospitals can put the right resources in the right places. So really impressive to do something like machine learning on this on in a relatively short period of time from when this whole pandemic is started. Help us understand a little bit, you know, what are the underlying technologies? How does AWS have been into this whole discussion. >> Well the Support of AWS is in many different areas. The first is that we are really trying to develop a platform without AWS and that's useful for the hospitals, for the institution to store in unique imagery set, all the images can be from different institution, so we don't need any more to send the images in any way. This is the first thing that AWS can give to us. Second is the use of a machine learning all this to analyze this kind of images coming from x ray chest through AWS systems. The third I think could be an aid in the generating the structure of the reports for this patient and moreover, the identification of patterns, different patterns that we can find inside the images. This concerns the radiomics theory, I mean, inside the images, there are many, many more information than what radiologist can can get from. So, I think sector agents can help us, so, AWS can help us to detect all these kind of patterns that we want to collect for our study. And the fourth reason is for the AI that AWS can give us is to share this kind of modality with the other scientific centers of Research Sector and not only for this specific pandemia now but also in the future maybe we will have, we don't hope this but we could have the second wave of the pandemia, there are many signals so about this in China and also in Europe. So these will be useful in the future to find the circle and second wave of pandemia, and also the final reason is that we will share all our results of this study with all the scientific community, I mean, we will improve an open access model together with AWS to share this information with all the scientific community the world. >> It's wonderful that this information can be shared broadly across the community, so important for tackling, you know, this challenging pandemic. I'm curious has your unit or had you used artificial intelligence machine learning before, I'd love to just get a little bit of background on, you know, how much you've used this technology? how accessible it is to be able to leverage it for two use cases like this? >> Absolutely, because, I mean, I'm an radiologist. If I check an image in a CT scan or an X ray, I can see inside that image, the maximum that I can see is 10, 15 to the maximum different patterns, I mean, the volume, the dimension of growth, which is the way of taking contrast media or difference old wishy washout but with my eyes I can see 10, 15 different patterns. And if a machine system examine the same image, it can reach out hundreds of patterns that I cannot see. So, we can detect all these patterns in different images, we can collect this machine learning system can work on these and put together all the similar patterns. So it can divide even in different cluster and then the system has to compare all this difference casts or group or patterns with a very huge database that we built before comparing and try to understand which patterns are linked to different outcome. So we can say, okay, this image has 20, 30, 100 patterns that suggest to us the destiny of the nation will be in one specific while another lesion that for me is exactly the same with my eyes, systems will tell us there are the two lesions are really different, their destiny is really different. This is the radiomics theory and this is what we are applying in our study on X ray chest cavitation. As I said before we select only positive patient. I mean all people that is for sure they have positive to COVID and in the first X ray chest, entering the hospital, we try to evaluate from the first chest X ray what will be the real destiny of the patient better or worse, and then we can also predict, try to predict, obviously, how many intensive care beds are necessity in that institution, we can send the therapy and adjust the therapy for the the different kind, different group of patients, it could be a very big help to an institution, to an hospital especially in periods like in our March or April when every day in every hospital in northern Italy, they were entering 200 person per hospital. It was a dramatic situation. >> Excellent. One of the other things that this pandemic has done is really required some, you know, strong coordination between both public and private entities. If you could speak a little bit to that my understanding is that AWS also help support this with the donation of computational credits. I believe it's the AWS diagnostic development initiative. So help us understand, you know, how the finances and the partnerships between public and private help everyone really, you know, address this current challenge. >> Well, the support from AWS for us is very important because now we, in this way we can use a lot of computing systems much more than what you had in our institution. And moreover, I think that sharing our information without the scientific content at the end of our study, it would be very important thing to do. Now I know we are beginning to appear on our drawn as in our websites, some afford to share information about going. Our study could be really one of the most important of this. >> Great, final question I have for you, Dr. Papa, give us your ideal vision going forward. You talked a little bit about how you know the importance of this to be able to watch and be prepared for a potential wave two where else is this this research relevant and where do you see this project going forward? >> Well, our study is not as focused on pneumonia from COVID-19. But the methodology can be applied in every kind of interstitial pneumonia. I mean, this is one of the first to that, at least, this has made in radiomics to segment at one whole organ, usually in radiomics, we used to studies the single lesions or little areas, I mean, no deals or metastases or primary tumors. This is one of the first, very first important studies where the segmentation is dedicated to the whole organ, I mean, all the lung, both lung. In every patient we segmented to the right or left lung. And in order to study diffuse pathology, in this case, of pneumonia, interstitial pneumonia is very different from bacterial pneumonia. And this methodology at the end of the study will be shared with the scientific community and could be a very interesting advancement our job. >> Dr. Papa, thank you so much for joining us and thank you so much for the very important work that your organization is doing to help attack the global pandemic. >> Thank you too, thank you too. >> I'm Stu Miniman, thank you for watching theCUBE (bright upbeat music)

>> It's a pleasure to be here with you and to tell you about something I've been dreaming about and working for for many years and now is coming to the surface quite powerfully and quite usefully in many areas. I apologize, sometimes this flickers for some reason but I hope it doesn't disturb the story. I'd like to give you a little touch of history since I was there at the beginning of this journey and give you a brief introduction to what we mean for Fog Computing. And then go quickly to three powerful application spaces for this technology, together with industrial internet and one is industrial automation. That's the focus of our activity as Nebbiolo Technologies. The other one is one of my favorite ones and we'll get there is the automotive that caught fire here in Silicone Valley in the last years, the autonomous car, the connected vehicle and so on. And this is related to also to intelligent transportation and Smart Cities. And then a little touch on what Fog Computing means for Smart grid energy but many, many other sectors will find the same usefulness, the same architecture dimensions of Fog Computing applicable. So this is the story that comes back hopefully, here, the day in 2010 when Fog Computing, the word started here, oh God, is this jumping around? I think it's the connector, this is the age of the connector, this is the age of the Dongles. This is not an Apple Dongle and so we are having troubles. And this is not yet one of the last machines that are out. Let's hope for, I never had this problem, okay. Alright, this date 2010 at the Aquarium Research Center in Monterey where I gave a talk about robots going down deep in the bottom of those big valleys under the ocean and when I finished, the lady, Ginny in the middle approached me and told me, look, why don't you call what you're talking about fog computing? Because it's cloud computing brought too close to the ground and I protested for about 15 minutes. And on the drive home, I thought that's really a good name for what we are doing, what we have been doing in the last years and I started trying it out and using it and more and more I found good response and so seven years later, I'm still here talking about the same thing. What's happening is Fog, the edge of the metric zone was very important but it was always very important in IT, is still very important in IT in mobile, in content distribution but when IOT came to the surface, it became even more relevant to understand the need of resources, virtualized real time capable, secure, trusted with storage computing and networking coming together at the edge. At the edge of the IT network, now they are calling this mobile edge, they realize we are realizing that mobile can benefit from local resources at the edge, powerful real time capable resources but also and more importantly for what we are doing in this space of operational technologies, this is the space, the other and the other side of the boundary between information technologies and operational technologies and here is where we are living with Fog Computing these days so, apologize, I apologize for this behavior that is, maybe I have another Dongle, Apple Dongle. Maybe I could look at that, maybe Morris can help me out here, anyway, so what is Fog Computing? Fog Computing is really the platform that brings modern, Cloud inspired Computing storage here is important here for our friends at Western Digital and networking functions closer to the data producing sources. In our case, machines, things, but not just bringing Cloud down, it's also bringing functions up from the machine world, the real time, the safety functions, the trusting and reliability functions required in that area and this is a unified solution at the edge that really brings together communication, device management, data harvesting, analysis and control. So this is kind of new except for our friends in Wall Street. The real time part was not as sensitive. Now we are realizing how important it is and how important the position of resources is in the future of solutions in this space and so it's not boxes. It's a distributed layer of resources, well managed at the edge of the network and really has a lot of potential across multiple industries. Here we see the progress also in the awareness of this topic with the open fog control room that is now a very active and even the Vcs. Peter Levine here is talking about the importance of the edge. What is really happening is the the convergence. I think we should probably stop and use a different Dongle. Is this the one, no, no, this is not the right Dongle. The world of Dongles, sorry. Oh boy. Oh you have the computer with the, okay, is the right Dongle with the right computer, okay. Here we are, okay. Alright, we're getting back there. This is the new Apple. Okay, we are here, this looks better, thank you. Alright, so this is to be understood. This is the convergence of IT functionality, the modern IT functionality with the OT requirements and this is fundamentally the powerful angle that Fog Computing brings to IOT and machine world so all the nice things that happened in the Cloud come down but meet the requirements of resources, the needs and the timing of the Edge. And so when you look at what is brought into particularly the world of operations, you see these kind of functions that are not usually there. In fact, when you meet this operational world, you find microprocessors, you find Windows machines, industrial Pcs and so on, not so much Linux, not so much the modern approaches to computing. These are the type of dimensions that you'll see have a particular impact on the pain points seen in the wold of applications. So now we go to the Use cases in, use cases in the internet of things. I think it's on your side, I'm sorry. Because it's the second machine. Okay, well, maybe here's the solution. So we have seen this picture of IOT multiple times. A lot of verticals, we are concentrating on this tree, one is the industrial, the second one is the autonomous vehicle in intelligent transportation, the third one, just touched upon is the Smart Grid. This is the area of activity for Nebbiolo Technologies. Those kind of body shops and industrial floors with large robots with a lot of activity around those robots with cells protecting the activities within each working space, this is the world PLCs, industrial Pcs controlling robots, very fragmented. Here we are really finding even more critical this boundary between operational and informational technologies. This is a fire wall, also a mental fire wall between the two worlds and best practice is very different in one place than the other particularly also in the way we handle data, security, and many other areas. In this space, which is also a little more characterized here with this kind of machines that you see in this ISA 99 or ISA 95 type of picture, you see the boundary between the two spaces, once more when we come back. And alright, so the key message here, very tough to go across, it's very complex, the interaction between the two worlds. And there is where deeply we find a number of pain points at the security level, at the Hardware architecture level, at the data analytics and storage level, at the networking, software technologies and control architecture. There's a lot happening there that is old, 1980's time frame, very stable but in need of new approaches. And this is where Fog Computing has a very strong impact And we'll see, sorry, this is a disaster here. Alright, what do we do, alright. Maybe I should go around with this computer and show it to you. Okay, now it's there for a moment. Now, this is, maybe you have to remember one picture of all this talk, look at this, what is this? This is a graphical image of a body shop of a an important car company, you see the dots represent computers within boxes, industrial Pcs, PLCs, controllers for welding machines, tools and so on. That is, if you sum up the numbers, it's thousands of computers, each one of them is updated through a UPC, USB stick, sorry and is not managed remotely. It's not secure because there's a trust that the whole area is enclosed and protected through a fire wall on the other side but it's very stable but very rigid. So this is the world that we are finding with dedicated, isolated, not secure computing, this is Edge Computing. But it's not what we hope to be seeing soon as Fog Computing in action there so this is the situation. Very delicate, very powerful and very motivating. And now comes IOT and this is not the solution. It's helping, IOT tries to connect this big region, the operational region to the back end to the Clouds, to the power of computing that is there, very important, predicting maintenance, many other things can be done from there but it's still not solving the problem. Because now you have to put little machines, gateways into that region, one more machine to manage, one more machine to secure and now you're taking the data out. You are not solving a lot of the pain points. There's some important benefits, this is very, very good. But it's not the story, the story is sold once you really go one step deeper, in fact, from connectivity between information technologies and informational technologies to really Convergence and you see it here where you're starting to replace those machines supporting each cell with a fog node, with a powerful convergent point of computing, real time computing that can allow control, analytics and storage and networking in the same nodes so now these nodes are starting to replace all the objects controlling a cell. And offer more functions to the cell itself. And now, you can imagine where this goes, to a convergent architecture, much more compact, much more homogeneous, much more like Cloud. Much more like Cloud brought down to the Edge. When this comes back, okay, almost there. So this is okay, this is now the image that you can image leads to this final picture that is now even not, okay, do you see it, okay. Now you're seeing the operational space with the fabric of computing storage and networking that is modern, that is virtualized, that supports an application store, now you have containers there. You can imagine virtual machines and dockers living the operational space. At the same time, you have it continuing from the Cloud to the network, the modern network, moving to the Edge into the operational space. This is where we are going and this is where the world wants us to go and the picture representing this transition and this application of Fog Computing in this area is the following, the triangle, the pyramid is now showing a layer of modern computing that allows communications analysis control application hosting and orchestration in a new way. This is cataclysmic, really is a powerful shift, still not fully understood but with immense consequences. And now you can do control, tight, close to the machines, a little slower through the Fog and a little slower through the Cloud, this is where we are going. And there's many, many used cases, I don't dwell on those. But we are proceeding with some of our partners exactly in this direction. Now the exciting topics if I can have five more minutes making up the time wasted. What's going on here, the connected vehicle, the autonomous vehicle, the electrification of automobile are all converging and I think it's very clear that the para dime of Fog Computing is fundamental here. And in fact, imagine the equivalent of a manufacturing cell with a converging capabilities into the Fog and compare it with what's going on with the autonomous vehicle. This is a picture we used a Sysco seven years ago. But this is now, a car is a set of little control loops, ECUs, little dispersed, totally connected computers. Very difficult to program, same as the manufacturing cell. And now where are we going, we are going towards a Fog node on wheels, data center on wheels but better a Fog node on wheels with much better networking between, with a convergence of the intelligence, the control, the analytics, the communications in the middle and a modern network deterministic internet called TSN is going to replace all these CAN boxes and all these flakey things of the past. Same movement in industrial and in the automobile and then you look at what's going on in the intelligent transportation, you can imagine Fog Computing at the edge, controlling the junctions, the traffic lights, the interactions with cars, cars to cars and you see it here, this is the image, again where you have the operational space of transportation connected to the Clouds in a seamless way which these nodes of computing storage and networking at the junctions inside the cars talking to each other, so this is the beautiful movement coming to us and it requires the distribution of resources with real time capabilities, here you see it. And now, the Smart Grid, again, it cannot continue to go the same way with a utility data center controlling everything one way, it has to have and this is from Duke and a standardization body, you can see that there's a need of intelligence in the middle, Fog nodes, distributed computing that are allowing local decisions. Energy coming from a microcell into the grid and out, a car that wants to sell it's energy or buy energy doesn't need to go slowly to a utility data center to make decisions so again, same architecture, same technologies needed, very, very, very powerful. And we could go on and on and on, so what are we doing? We won't advertise here but the name has to be remembered. The name comes from a grape that grows in the Fog in Northern Italy, it's in Piedmont, my home town is behind that 13th century castle you see there. Out there is Northern Italy close to Switzerland. That vineyard is from my cousin, it's a good Nebbiolo, starting to be sold in California too. So this is the name Nebbia Fog comes to, Nebbiolo Technologies, we are building a platform for this space with all the features that we feel are required and we are applying it to industrial automation. And our funders are not so much from here, are from Germany, Austria, KUKA Robotics, TTTech, GiTV from Japan and a few bullets to complete my presentation. Fog Computing is really happening. There's a deep need for this converged infrastructure for IOT including Fog or Edge as someone calls it. But we need to continue to learn, demonstrate, validate through pilots and POCs and we need to continue to converge with each other and with the integrators because these solutions are big and they are not from a little start up. They are from integrators, customers, big customers at the other end, an ecosystem of creative companies. No body has all the pieces, no Sisco, no GE and so on. In fact, they are all trying to create the ecosystem. And so let's play, let's enjoy the Cloud, the Fog and the machines and try to solve some of the big problems of this world. >> Okay, Flavio, well done. >> Sorry for that. Sorry for the hiccups. >> Now we do that on purpose to see how you'd react and you're a pro, thank you so much for the great presentation. >> Alright. >> Alright, now we're going to get into panel one, looking at the data models and putting data to work.

>> Welcome back everybody. Jeff Frick here with theCUBE we're in San Francisco at the Minds and Machines conference.  Three thousand people the fifth year of the show. Really everything about GE all the players from GE are here but are really being driven by the digital and the digitization of what was a bunch of stuff and still a bunch of stuff. But now we're digitizing it all. Yeah I'm really excited to get this bill saw you what nine months ago six months ago Timeflies to the Chief Digital Officer of chief power. Welcome. Great to see you again. >> Thank you. Thanks for being here. >> Absolutely. So just first impressions of this event. Pretty amazing. >> Yes it's gotten really bad. Right and I I remember stories of people telling me that hey this is the fifth one we're doing the first one we almost had like pull people to come here. Now we are like figure out how do we get to a bigger location because this is getting mainstream. Everybody is looking at how does digital help their business. Because in the industrial sector productivity had slowed down right over the last four or five years. It had become only 25 percent of what it used to be. So the biggest lever for productivity efficiency and creating new value is through digital transformation. It's not just automation. It's about creating new value new revenue from digital assets and that's why you see the excitement across all of the industries here. What's interesting you came from the I.T. world. >> Yeah there's already kind of been the digital transformation in the I.T. world that a lot of the I.T. stuff has now been Olek been turned into electronic assets right. You have no paper but that that can't happen in the OT world right. We still got generator just for gadget engines. You still got physical things but it's still a digital transformation. So how are those things kind of meshing together. Yeah so you know having worked in software all my career in Silicon Valley you write like you think about Cambridge with a belief that every business every industry will be reimagined with software. We've seen it in retail and music and entertainment and travel but there the software our aid the world. Yes software is going to aid the world but here software is transforming the world too because the physical assets matter. But all of the machines that we make for example in power we make machines that power the world more than one third of the world's electricity comes from a machine. Right. So all of these machines generate electrons but they also generate a lot of data more than you know two terabytes of data a day from a power plant can be generated. That's more data and more consumers will generate across an entire year old social media. So this data matters we can learn a lot from this data and make these machines efficient more productive and kind of like a 360 sexiest word for some of the industrialist is no unplanned downtime right. Element breakdowns which turns into massive productivity and value for our customers. The thing I think that would surprise most people Jeff talked about it in his keynote yesterday is that there has not been the kind of the long traditional productivity gains in the industrial machines themselves and you think wow they've been around for a long time. I would think they would be pretty pretty efficient. But in fact there's still these huge inefficiency opportunities to take advantage of with software which is why there's this huge kind of value creation opportunity. Absolutely. So now also think where the cycle time of innovation. Right. All of these are mechanical machines right. We know with advances in materials science and engineering and you know brilliant manufacturing we can get more out of the physical asset but that requires a big upgrade cycle. What if we agreed to the machine with software and that's really what we did in our businesses across power right where we called them edge applications where it's about improving the flexibility of a machine or they 50 of them. All of these are modeled and algorithms and the way to think about it is all these machines in fact outside we have a giant machine that powers this entire event. And you can see the digital twin version of that machine right here on the screen. All that is is a virtual representation of that machine from the physical world where we have all the thermal models the Trancy models the heat models the performance models all connected. But now we can run the simulation in real time all of the operation data and apply algorithms to get more performance out. A great example as we just launched one of the world's most efficient most flexible gas turbine a giant turbine called H.A.. >> But with the additional software we were able to improve the efficiency it's now the Guinness World Record holder as the most efficient flexible power plant in the world. That was then a brand new unit that was developed with the benefit of software or was that really applying a Software to our approach that was a brand new unit. But overlaid with software was able to eke out more efficiency as well. But we're doing this an older power plants as well. In fact a great story is we had a customer and Italy called A2A their multi utility company in Italy. They have a power plant and Cuba also in northern Italy. They had shut it down because it was no longer competitive to operate that power plant in the modern world where there was so much renewables. Because you got to compete in a market called ancillary services meaning you need to be able to quickly ramp up power when the wind doesn't blow or the sun doesn't shine bright and shouted down right away. You can't do that with giant power plants. What we did was we completely model that's how plant and software and digital trend we show them that this actually can be competitive. So with the addition of software we were able to reopen a power plant that was mothballed and jobs were reinstated and the Paul plan is actually flexible in the open competitive ancillary services market. So all of this is possible because of software we're able to breathe new life into big giant heavy machines. So just a year in the power space I'm just tired. You know we've seen kind of in the US. No the nukes are being turned turned off. >> I grew up in Portland got trojan on the Columbia River we could take field trips with the smoke come out the cooling tower. We've got the rise of renewables are really really really going crazy. He's got this crazy dynamics and the price of oil. How's that played. How are you guys helping kind of deal with this multimodal. It's interesting here that oil and gas is still its own separate group. I'm like they got it like we want to be part of the renewables and didn't just become energy and not renewables oil and gas nuclear etc.. So you know that's a great question the industry is oil and gas has lots of other things and downstream stream and so on. And but at least across all of the electricity businesses we're coming together. And we call this the electricity Value Network. Think about where we used to think about a value chain where the Greens got generated and they traveled to the consumer. It was a linear model. And we know from Silicon Valley when digital anchors industries they all become network model. Right. Right. So we're calling this the electricity Value Network. And the interesting thing is our customers have different mix of fuel. And every part of the geography in the world in North America is still a good mix. Renewables is on the rise in California. We're going to have 50 percent power from renewables by 2030. But you still have to balance and optimize the mix of power from gas and nuclear and other sources of fuel and hydro and steam and so on. Right. And in Europe it's our abundance of renewables. >> They're struggling to integrate them into the great abundance of renewables or abundant capacity right. Renewables are growing and so they have to integrate them better in China and India for example still coal and steam is the big source of power because that's the fuel they have. They don't have as much gas. So the mix of fuel will change the world. The beauty of software as we can help optimize the mix. In the past we always talked about renewables as a silver bullet or gas silver bullet. Now we're saying software is a silver bullet regardless of what the mix of fuel we can optimize the generation of electrons and we're seeing this entire industry of electricity being transformer and digital and we call that the electricity Value Network. It's crazy interesting times so big show any big announcements happening here at the show yeah we know lots of big announcements one of the biggest ones is we're just dying day big enterprise wide digital transformation and relationship with Exelon Exelon is the largest utility in North America and they so are 10 million customers but they also generate a lot of power over 35000 megawatts of cross nuclear wind solar hydro gas and you know a year and a half ago we started a journey with them on understanding what the value of vigilance. There is such a believer and we learned a lot working with them as well and now they're deploying our Predix platform the industrial platform and APM which is our asset command and software and our food speed of operations optimization business optimization and cyber across the entire enterprise. >> So it's a big strategic agreement with them and where we're allowed to tell people is that you know a year and a half ago we were talking about what would happen if a wind farm went digital or a power plant. When you don't right now we're talking about what happens an entire utility goes digital or an entire industry of electricity goes digital and leaders like Exelon have the opportunity to create that tipping point in the industry. It does feel like this is the moment I think digital transformation of the electricity industry went real and this is it I presume not everything that they own is jii equipment no software is agnostic. Right. Right so this is really a software deal with their existing infrastructure that probably has a blend of G gear and who knows what other year that are generating. This is no different than how we in Silicon Valley would think about a enterprise software deal. It is the Enterprise subscription deal for them except it's to our cloud and our edge solutions and it's every machine right every single asset whether it's a giant gas turbine or a small little pump every machine has some sense or we will sense the rise or does the environment but all that data is being put into Predix. We will build digital twins of their entire power plants and give them more new insight and help them you know eliminate unplanned downtime and reduce operational costs citing times. We've got to get on buses to get those batteries done right till we get stored where we can we can connect them and optimize them as well. Right. Absolutely. >> I look forward to catching up six months from now and see where you guys are going out fast Bill and you and the team have grown you know from from a little bit of these kind of software skunkworks out there. Yeah I know many people are in San Ramon now. Now I think we're about a hundred people I think we're diversifying I think and it's a great challenge. So when we get the Adsit camping on the horizon. Oh and Sarah will be there. You can hit me up on Twitter again as well if you're interested in working in meaningful purposeful things like energy and the coolest things and software super. All right good. Thanks for stopping by. All right. Thank you. You have been asking us belum Jeffrey. You're watching the queue. We'll be back with our next segment after this short break.