Please welcome back Mario Armstrong. Thank you for that. Thank you, thank you I appreciate that. Super awesome of you to be excited like that. Thank you. This is very intimate. So for those of you in the back, come on up. Welcome to NetApp's first customer jam session. As you can see, this is a smaller, more intimate session. But that does not mean it's any less powerful. You should see the people that we have backstage right now. These are heavy hitters, so don't be shy. Come on up. If you're in the back, come on up. We want the panelists to feel your energy. All right. So this is going to be one of those one of a kind panel discussions with great examples of customers putting innovation into action. And here to get things started is the moderator for the jam session, SVP of Technology, Communications and Strategic Alliances at DreamWorks Animation, Kate Swanborg. Come on Kate. HelloOh my gosh, I am so excited to be here. I cannot tell you how thrilled I am. It is so fantastic. Being back at INSIGHT is so fantastic, being back in person, and it is truly an honor and a privilege to be able to host this spectacular, profoundly interesting panel. Today, I'm absolutely - Whoa, there's a lot of me! - I am absolutely thrilled. So one of the things, so for those of you who might have just heard this, so I am from DreamWorks Animation. Yes, true. We make cartoons, we work in the film industry. And for those of you who have gone to movies, you may have been watching one of the previews for a movie andhear this voice: "Imagine a world." And so I always love that, right? Because that's what we do at DreamWorks. Of course we imagine worlds, but today we're imagining worlds of innovation. And the fun part is that the worlds of innovation that are being imagined by this panel is just spectacular now. Innovation. What a fantastic word. Perhaps even used too often. So I had to go look it up. I had to go see what Webster said about it. Webster's dictionary defines innovation as follows: Innovation is a process by which a domain, a product, or a service is renewed - by applying new processes, introducing new techniques, or establishing successful ideas to wait for it, create new value. The creation of value is in fact a defining characteristic of innovation. The creation of value. That is what innovation is about. And so today we are going to hear from our amazing panelists about their incredible innovation stories and how they're creating incredible value, whether it's the world leader in high performance superbike racing or the scientists and engineers who are quite literally reshaping the future of energy. Or the scientists and engineers who are focused on curing children's cancer. Hey, we may even find out what an animation company is doing up here. But before we have our panelists join us on stage, let's take a quick look at the world of innovation as they're imagining it. Awesome. Okay, please join me in welcoming to the stage Mr. MarK Cowley, Computational Biologist at the Children's Cancer Institute and also Mr. Nicolo Mancinelli, Tyre and Experimental Vehicle Dynamics Manager from Ducati Corse. And Mr. Phil Adams, CTO at the National Ignition Facility at Lawrence Livermore National Laboratories. Oh my gosh, best panel ever. Thank you so much gentlemen. Oh I'm so excited about this. So here we are. We're here to talk about innovation. I'm not sure three more incredibly and inherently interesting businesses and realms.have been brought together at one time today. And so as we talk through this and we talk about innovation, I think it's so important to recognize that each one of your areas is, of course, focused on creating value. And that value can be something incredibly specific, like a goal for the entire world, just in different forms. And so we're going to start, we talked about the definition, and now we're going to talk about problem solving using innovation. And soNicolo, I'd like to start with you. When you think about problem solving using innovation, what does that mean to you? For me, innovation is like a bet. It's not like the bet you make here in Vegas, because you invest resources on something that has highly uncertain results, and it can take a lot of time to get the results. Let me tell you a story about our company deal with innovation. In the last years, Ducati started to as competed in a Moto GP since the very beginning, in 2002 Moto GP's the most important two wheel road racing vehicle competition. It's like the Formula One for motorbikes. At the beginning, Ducati invested primarily on a very powerful engine. We have the fastest bike on streets, and was like a rocket, but it was not very easy to ride. And in 2007, a young talented rider, an Aussie one, Casey Stoner, joined our team and he was the only one able to dominate the powerful beast. And he won the title. So the first World championship in 2007. But in the following years, the enchantment no longer helps and we start understanding that having the fastest bike on straight was not enough to beat the competition. So we understood that we need the bike to be faster on corners. And so from 2014 we started to make our bet. We invested on advanced vehicle research. So how aerodynamics affects the cornering speed. How to get the best performance from tires. At that time our knowledge on this area were very limited. So the development was driven mainly by rider feedbacks, by empirical experiences. And so we invested in simulations and advanced measurement technology. So the purpose was collecting data from our vehicle in real conditions and try to simulate this data with the data coming from simulation. Uncertainty on the process was really high. But every time we were on track, we put the wheels on track, we collected new data. And this data helps us to improve our model and also understand it better, the system and what really affects the cornering performance we were looking for. And thanks to this knowledge and innovation, we start introducing new things on the bike that affects cornering performance. For example, we were the first introducing specific winglets for increasing corner speeds. And also we tried we started to manage the tires during the race weekend in a new way was more data driven way. For example, we had to select the right tire spec for each conditions, or we have to manage the power delivery for making the whole race distance in the shortest time as possible. And all this started to be driven from data, from simulation, from models, and not only from rider sensation as it was in the past. So at the end, nowadays, maybe we don't have the fastest bike on the straight line, but for sure we have the most competitive bike overall. At the moment, in the championship, that is not finished yet, the first three positions in the championship are Ducati riders. We won in the last four years, the Constructor Championship and the last year with our top rider, Paco Bagnaia, we bring back the rider championship title to Bologna. So in the end, this new approach, this data driven approach help us to solve our most important challenge that is winning the races with the fastest machines and also with the best riders to match. That's so amazing. And I mean, when you, right? I think so. And congratulations on the championship. It is interesting, isn't it, because life isn't in any business about the straight line. It's about accelerating through the curves, and you've actually figured out how to do that in such success that you're bringing home championships. Well, so I'd love to. So, Phil, you were on stage not terribly long ago, and you were regaling the audience with what is happening at Lawrence Livermore National Laboratories, which it sounds. I mean, it's almost like a sci-fi movie. It's so amazing. And but to get there, you might have had to solve a problem or two. And so if you could fill us in on what innovation meant to you and your problem solving, that'd be awesome. This past weekend, we had a family day at Livermore, which is a unique opportunity for us to be able to bring our family and friends on site. And we went through the NIF before it came here. And I asked my son, "what did you think?" And he said, "it's amazing it all worked." My answer at the time was, "what do you think I worked so hard for?" But today I'm going to tell you what, why some of this all works. We've had to create the most advanced computer control system that's anywhere in the world. And in order to do that, we first looked out to NASA and modeled our control system after theirs. But there's only did 4,000 control points, ours does 60,000 control points. We were doing IoT before IoT was a common buzzword. We're controlling motors, cameras, actuators. et cetera. To make sure that all 192 beams come together within 30 picoseconds of each other. The beam travel has to go through one kilometer to go through the building in order to get energized enough to be able to create, to produce the conditions that can create fusion. And so there was a lot of things we had to solve about that to be able to get that amount of precision and to make it easy and make it repeatable in our environment. And a lot of where our focus was,how do we take something this complex and drive it down into that reliability factor. And we spent a lot of time really focusing on reliability, availability and maintainability. Well, it's interesting you say reliability. Sometimes I think that there's a sense that the word reliable or consistent sounds boring. It's anything but. If you can rely on it and have it be high performance at the same time, that's where you can really drive results. Exactly. And one of the things that we were extremely focused on is the more reliable our system, the more that we gave the opportunity for our science to really focus on the experiments at hand and really drive progress forward. Reliability becomes your platform for innovation. That's right. Oh my gosh. Okay. So Mark, you and your colleagues and your scientists and engineers are focused on changing the world for our children, right. I'm not sure that there could be a more profound or heartfelt journey. Can you talk about what innovation means to your problem solving? So I'd love to tell you a little bit more about what we do. The Children's Cancer Institute in Sydney, Australia. We believe that our life should be long, and unfortunately, cancer is the leading cause of death in children in Australia, in America, and there's about 400,000 newly diagnosed children and young people throughout the world. So this is a pretty important problem. It takes lots and lots of innovation to try and tackle this. Lots of different people with lots of different expertise. But I think the key breakthroughs. I was listening to you earlier, Phil, but 70 years ago I looked it up, the DNA helix structure was solved. And after that blueprint was solved about 22 years ago, we sequenced the first human genome. And so since then, that cost billions of dollars. But now we can sequence a human genome for about $1,000. And when we started this,campaign sort of ten years ago, it was kind of all the naysayers were saying, well, maybe you could get $1,000 genome, but it would be $100,000 analysis. That's where all the bottleneck was in the data and trying to interpret it. So what we've now been able to do through our Zero Childhood Cancer program, where obviously we're trying to eliminate, zero, bring brain cancer down to zero, by understanding the genome of every patient's tumor, we've learned that every patient is unique. One treatment does not fit every patient. I suspect there might be some childhood cancer survivors in the room. I certainly met one last night. And the treatments that people have gone through to even survival, they go through hell and back. So we need desperate. Less toxic treatments that are more accurate, more precise and match to that particular patient's genetics. So I think the real innovation that we've been trying to solve is how do we mainstream, simplify, robustify, what we've been doing, like you've said, Phil and try and make sequencing a genome fast, effective, robust. Build that trust with all the clinicians around the country so that their pathologists trusts thisnew, quite complicated data. The genomes are 6 billion letters, a 3 billion letter code. But you inherit a copy from mom and one from dad. So for every patient, when you crack the genome, you're essentially cracking a 6 billion letter code. There's millions of genetic changes in all of us. Our problem is which 1, 2, or 3 caused the cancer? Which is the susceptibility that we can go after with a range of different drugs? So that's really the innovation is how do we personalize treatment for kids. And our early data suggests it's starting to make a big impact. Well it's good to know - right, okay? Thank you! It's good to know you don't sleep. it's so fascinating, we hear from you, you're solving a 6 billion point code for every patient. And we hear all of the data beinganalyzed in order to create championship bikes. When we look at it somewhere at the base level there, the nature of how you are all going about this has such similarities. And so and,I suspect that that's true not just with the analytics of an astonishing amount of data, but also the process by which you innovate and with whom you're innovating. So, Mark, I'd love to stay with you here. I can imagine that at your institute, the process you go through in order to determine where you're going to innovate and how you're going to focus these amazing people's times, is something where you have to focus on just making that process innovative 100% . Okay, I think I touched on it a little bit. We start with innovation, with people.that really care about the mission and the cause and what we're trying to solve here. So this requires getting clinicians in the room with scientists, with engineers. My team already has software engineers, data engineers, UX designers now. So it's just a multidisciplinary approach. Of course, we need to be driven by the parents and the consumers that have survived their cancers. So I think for innovation, for us, we start with trying to really understand the problem. I feel like I've made a career as a scientist, just learning what the problems are in the hospital and trying to work out how we can use bioinformatics to solve those problems. How do we build algorithms to extract from these large amorphous data sets? What clues might help answer that medical problem? So I think my answer really is around the people. And we'll talk more about the technology I think a bit later on. But let's start there. Well that's fantastic. And Nicolo, when you're thinking about I mean, you shared with us a little bit about the process you went through and you said, hey, we're going to make a bet. We're going to decide to actually fundamentally change what we're focused on. How did you bring people along on that process and how did you innovate through it? Basically, the process of innovation, as I discussed before, is first we try to understand the system. So understand the system means that we build mathematical models of it and can be like physical models or machine learning, artificial intelligence models. And in the other side, we try to measure our system to collect data from the real world, from the real system, in our case with sensors, but maybe markers with other kind of technology. But the fact is collecting data from reality and in the other side, simulate your reality with some models. And I think you really understand your system when the data coming from simulations and models and data from measurements match. When they match, it means, okay, you fully understand your system or not fully, but you have generated an environment where you can unleash the creativity. So the process is first understanding and then unleash creativity in this environment. And of course, as Mark told, what makes the real difference in this process is about the people, is to find passionate people that can work on it and give them the right conditions to express their talent, their creativity. Fortunately, racing and motorsport in general are environments where attracting passionate people is relatively easy and you can give them the opportunity to drive your innovation process from the creativity to the execution of the process itself. Fortunately, my company Ducati does this very early and that's why we decide to invest in racing it's not only a marketing activity it's an activity that generates innovation, and you can bring this innovation in the production motorcycles that our customer uses every day. It's so fascinating. I mentioned a second ago how similar these worlds were. And in fact, there's so much similarity even with DreamWorks Animation. Our process around innovation ofcourse, is entirely people-driven and a creative process. When we're looking at innovation, we are looking at our artists, our filmmakers, and our engineers. And again, I'm going to go back to that Webster's Dictionary definition about the fact that innovation has to create value, that's a defining characteristic. And for us, when we look about the process of innovation at DreamWorks, we are looking at creating value not just for the near term but for the long term. And so we'll we might decide tofocus a group of engineers on a, on a high performance system for a single movie, but we cannot afford, quite literally afford to have it just be for that one film we used. We use 20 visual effects artists for four months to create a brand new system for the film we released this past summer. We had this water system that we'd never done before. We created it from the ground up, using real world physics and simulations, and then redefining them for the world of animation to create a hyper fluidic hydrological system for this movie. But the key is for innovation. We actually had to ensure thatsystem would be fit for purpose for years to come, so that we could leverage the cost investment on not just a single race, if you will, but to win all the races. Now, before we move on, Phil, I want to hear about the process that you go through at the laboratory around innovation. One of the things that really jumps out at me right now is collaboration. We spend so much time to attract the right talent, the bright talent that's going to propel science in the future. And so what we do is we try to get engender in an environment that is strong on diversity, inclusion, equity and strong collaboration. And then we then instrument as much as we can to understand the problem, much like you said. And it really resonated with me in terms of the how we instrument and collect the data and meet around that as a center point to make sure we truly understand what it is we're dealing with. And then from then you develop a hypothesis, you test that hypothesis, you and reiterate. At some point for us that rolls into a common software development lifecycle process, but usually it starts off with the proper scientific method and rolling into then how do you productionize it? It's interesting. I want to stay with that thought for a second and sort of move it into our next topic. We were talking a few seconds ago about things being reliable and stable, and how those incredibly boring words are foundationally critical to the world that we're living in. You were just talking about the scientific process. And again, that's a process that some of us that aren't even scientists, we learn in school as a sort of a fundamental step by step approach to making intelligent decisions and making innovations materially matter. If you think about the foundation that you need there, well, I think another word for that is infrastructure. And so I would love to hear from you, Phil, about how you view infrastructure as a game changer for innovation. Our infrastructure really is thecore foundation by which that innovation will be possible. If our infrastructure is continually having issues, we may miss that eureka moment. That is truly going to be the groundbreaking aspect to unlock the next thing. There was a point where humans didn't think we can break the four minute mile until somebody did. And then, thosetimes started to shrink accordingly. Likewise with fusion ignition. Now we've done it. So the next step now is to... That's worthy of applause. Just casual fusion, man. You solved that, right? Solve that. Next. Right. Next. But it does get better from there. We need to make sure that the infrastructure is something that can support those groundbreaking results. That is amazing. I think, again, we stopped to applaud because it's you talk about breaking the four minute mile and then suddenly other people are capable of it. And you think to yourself, okay, well, we're people capable of it then before it happened? And the answer to that clearly has to be yes. So then but the key is that somebody breaks new ground, something moves it forward. And all of you have done that foryour worlds and for the world, and you're doing it on this foundation that you are personally responsible, this infrastructure. And so I'd love to move on to you, Nicolo, and I'd love to hear about how the infrastructure is helping Ducati move forward. Yeah. For us, infrastructure played a key role in the last 4 or 5 years on how we manage our innovation cycle that I talk to you a few minutes ago. Imagine that we have a few motorcycles racing in tracks all over the world, and so they generate a huge amount of data, some gigabytes of data each bike for each day. The weekends during racing, starts on Friday and go on until Sunday, the race day. And so what we need computational power in place. So this computational power is needed to process all this data, extract more information that is coming from the sensors and joining with models, you can assert more information. And then you need people that can go inside this data and extract information.is valuable information to make decisions during the race weekends for example. So just a few years ago, only the crew, the team members that physically are in the trackside can have access to this data in short time. And so they have to - they had to - make decisions on their own. But thanks to our technology partner solution, nowadays we have a full team of engineers in the base factory in Bologna. They can access the data of the bikes running around the world. And this is thanks to NetApp ONTAP, for example, cluster using FlexCache technology that makes for us a bidirectional synchronization of this data. And in a few minutes in Bologna, we can see the data. And so we can run our simulation, give back the results to the race team, and participate to the all decision processes that take place in the race. For example, in Moto GP, it's not like Formula One where you have pit stops, so you can correct a bit your strategy whilethe race is started. In our case ,we have like a one shot mission on Sunday. So you have to take decision and there is not so much room for mistakes or you cannot correct. So the decision process on which tire specification choose for that condition, how to set up the bike and electronics for the race distance now can be taken in accordance between a team that is at home, that has more time to go inside to the data run simulation and the guys that are directly on track. So for us it make a huge difference, can work in different teams on the same things in a short time. This was a good improvement for our managing. It's interesting you're looking at infrastructure to make the world smaller. Yeah. Honestly, you're using your infrastructure in order to have that data shared by,people all over the world delivered back, creating that value. And just yesterday, Phil, you and I were talking about the fact that, in fact, so much of what of your world is actually happening on lab because we talked about being distributed versus not being distributed. And I love the fact that these two versions of infrastructure are inherently fit for different purposes. One is making the world smaller. One is actually enabling a laboratory in which everyone is coming in and working on bespoke equipment that doesn't exist anywhere else in the world. And yet the solutions that you are employing are very similar solutions. That says something about the extraordinary architecture of those solutions, that they can be deployed against so many different methods of ensuring that you're moving things forward. Now, Mark, clearly, in order to deal with a 6 billion code problem, you are standing on top of an amazing infrastructure to do that, but one that you also have to have enormous confidence in. 100%. Can I tell you where we started? When I sequenced my first genome or the team sequenced the genome? Was it a small, well, fairly large medical research institute? We had a local high performance compute. And when I tried to analyze that genome, I clogged the entire HPC. We had a mailing list, it's called. I copped a paddling over the mailing list. So back then we realized we had to innovate and move to the cloud. This is before our relationship with NetApp had started. So we started using commercial platforms as a service on top of commercial clouds because it just made it really easy for us to scale up, take care of spinning up Kubernetes clusters, and finely tuning our genomic analysis pipelines so that we could use the right instance for the right job. We've been transitioning in the last 3 or 4 years and actually before that, we were then storing these huge data sets. So one patient has 400 gigs of raw data. We make about a terabyte for each patient. And we were just outstripped all of our storage capacity. I was storing data wherever I could. And so fast forward to what NetApp and the StorageGRID has really helped for us. We've been able to consolidate all of our data. We've pulled it out of tape from our national HPC systems, fromGod knows what hard drives there were. So we've been able to centralize everything and connect the StorageGRID to a whole range of different computing platforms. You're no better than me. I'm not the most technical person on the stage here, but we obviously can connect it to commercial cloud. But surprisingly, we could connect it to national HPCs. Rewrite our analysis pipelines so that we could seamlessly pull data straight out of StorageGRID into an academic computing environment. Run, push back. Without the tiny little staging area getting clogged and engineers staging data queue subbing commands. It became much,more easy. So for us, the analysis takes about 5,000 hours of compute, and we can squash that down through parallelization on the cloud to about 24 real hours. A couple of years ago, we first talked to Microsoft and said, can you help? We're running at about $250 to analyze each kid's cancer genome. Now are in the $30 ballpark thanks to Spot, thanks to cloud, and thanks to really innovating. And we don't have these massive storage bills of leaving data because you might need it. You might need to reanalyze it next month. And so being able to pull that back with our StorageGRID has beenfantastic. I think I've learned a lot from being here. I don't think we're using the full suite of NetApp technologies to innovate what we're doing. I just heard about Spot. I'm like, okay, we're spinning up lots of Kubernetes clusters now manually, so I'm going to try and learn what the heck we can innovate with there. Well, there's a lot of amazing businesses to go to school off of here. But I think staying on this infrastructure topic, I saw you nodding your head, Phil, about this concept of having to keep data around and having an infrastructure that is critical to do that. That's something you guys are dealing with all the time. And it's funny, at DreamWorks too, the same thing. So obviously you're focused on children's lives. We're focused on children's laughs. And our world is that if we make a movie, we want to be able to access that data forever. And so we also use StorageGRID. We're also moving off of tape to StorageGRID. I look at the last 30 years of innovation from an infrastructure standpoint at DreamWorks, and I think that one of the most impressive things from our standpoint is that in 30 years and we've we have been using NetApp for that length of time. That's how long we've been using NetApp. We haven't lost any data. Zero.data loss in 30 years. Which in and of itself, yes, I applaud because, gosh, I can't find my eyeglasses most of the time. And so zero data loss. But perhaps even more impressive, is that the infrastructure that we're running on, which sounds again, like we all have tremendous similarities here. We have 100% data availability at DreamWorks. And what does that really mean? What that really means is that any hour of any day, of any week, of any month of the last 30 years, any hour, any day, any week, any month, the data has been available to our artists. So the instant they're inspired, the instant that they think, "Oh, I could make that better, I could do something right there." The instant that infrastructure isn't always on data available infrastructure, that is amazing. And that's the world. And I think about you guys and the people that you have working, and you must be relying on infrastructure for that same reason. Yeah. So okay. We only have a couple of minutes left. But I have to talk about not today, but tomorrow. So tomorrow isa pretty exciting time forthis world. You've each solved big problems, but I want to know about the next big. I want to know about the next mountain you're climbing. I want to know what I can look forward to sitting with you here a year from now and celebrating and chatting about. And so, Phil, we're going to start with you. Our scientists haven't rested on their laurels on getting fusion ignition. So they have been trying to push the boundaries now. And they've been looking at how do we do this more efficiently? How do we do more of these types of experiments that are being able to gather more gain fromeach reaction that we do? How do we create better targets? That's pushing the limits of manufacturing as we know it. What I hope to see with something like this that has been just an extreme catalyst for innovation, we're going to have is just a lot of different spin offs that come as a result of this. And so, what I see right now is that we're going to need a lot more compute, possibly needing to tune up things with regards to AI to get to the next level, to be smarter about how we do things. The future is very bright for us. Well, I think so. I think, and it's going to be very bright for all of us standing on your shoulders. So I'd love to, Mark, I'd love to hear about what you view in your short term and your long term future here. The big challenge for us is how do we take this research grade test and make it standard of care and available for every single patient as part of Medicare. And so there's a lot of hurdles to be able to do that. We have to prove that the test has sufficient quality, accuracy, reproducibility. We've got to try and work out a pathway to make it cost effective for our patients, and so that Medicare can pay for it. And that's that gives us a bit of a time frame, because in two years, we're moving into Australia's first children's comprehensive cancer center, modeled a little bit after the American Comprehensive Cancer Centers. So this will be the first bespoke facility where we've got research and hospital care in the same building. Patients on the same floor as our researchers. So I think that in two years will catalyze that. The big opportunity then, though, is how do we get clinical data? The clinical data that's buried in electronic medical record systems is a totally different silo to our own. Being in the same building, I think catalyzes that. And that gives us a whole new data science challenge. Got all this? I've probably picture the electronic medical record data to be higher quality than it actually is. It's probably lots of three and four word scribbles by oncologists that are busy. So how do we infer how a patient's doing based on the types of tests that the doctor is ordering, or the biochemical results of those tests? I can probably use machine learning and infer how the patient is doing without the non-existent tick box that the oncologist doesn't tick to say, yeah, patient's doing okay today. Thanks. So I think we're going to do a lot more data science when we get access to this messy data. I think that's in our sort of 2 to 5 year time frame. If we think even further, though, we think about the 400,000 young people with cancer. And in our region we have Tonga, Samoa, Fiji, many different countries that need our assistance to try and get basic diagnostics drugs to these countries, and starting to think of innovative ways, which really reinforces why we need to make this complex test today as simple as possible, because then we can start to roll it out, commoditize it, and make it easy to run in a lab with limited air conditioning in Papua New Guinea. That way, the patients around the world can start to get access to these diagnostics. And so hopefully by the end of my career, we can start to see therates of survival just go through the roof around the country, not just in developed countries like our own. So that's where we're headed. I have goosebumps, I mean, it's extraordinary. You are truly imagining a world where children aren't dealing with cancer. That's amazing. Okay, so.Nicolo, I'd love to hear about the next set of challenges andopportunities for Ducati. Yeah. Our next challenge is to, of course, stay ahead of the competition on the races, go on producing innovations for everyday production bikes. We have taken just this year a new challenge following our approach. So starting from races, we started developing an electric motorcycle was the first time for us. So we are from this year the sole supplier for MotoE championship. That is a championship dedicated for electric motorbikes. And of course in this new environment of the automotive that is rapidly changing, we applied the innovation cycle. We discussed before. So wecollected new type of data and we apply the same kind of approach that was successful in Moto GP. And we hope to be successful also for new kind of vehicles for electric vehicles. Absolutely amazing. Okay, beforewe give an enormous round of applause to these three groundbreaking innovators, I just want to say thank you to all of you. I am personally inspired. I am more hopeful today than I was yesterday about the future of the exciting and amazing world of Ducati racing, the future of our energy world, which isso critical to all of our lives, and the future of our children's lives. again, I have goosebumps. I'm so excited. And the other thing that I'm excited about is that while we were focused on these topics today, right here at 2:00 today, I get the opportunity to share about DreamWorks Animation and how we make our movies and what goes into all of the filmmaking processes and why animation companies care so much about technology and data. And I get to do that at 2:00 today at the DreamWorks booth on the show floor. So I'm also excited about that. So I would love to give a huge round of applause to these amazing,gentlemen. Thank you so much.