NVIDIA Corporation (NVDA) Earnings Call Transcript & Summary

Hi, everyone. Thanks for joining us today for our webinar on Transforming Transportation with the Metaverse and AI. Before we begin, we wanted to cover a few housekeeping items. At the bottom of your screen, you can find various widgets to use during the webinar. Each widget is resizable and movable. If you have any questions during the webinar, you can submit them through the Q&A widget near the bottom of your screen. We will try to answer these towards the end of the event. Additionally, you'll notice a clipboard icon, which is a brief survey. Please take a moment to fill this out as it will help us tailor these webinars in the future. Here are some tips to help ensure you have a good viewing and listening experience. To maximize the quality of the audio, please close any open applications aside from your browser window. Also, a good old-fashioned browser refresh can cure many issues. So if your audio stops or the slides seem to be lagging, just give you a browser a refresh. If you do encounter any other technical issues today, please let us know in the Q&A box, and we'll be happy to help. Now without further ado, we'll turn the event over to our speaker, Norm, to begin the presentation.

Hello, everyone. I'm Norm Marks from NVIDIA, a Vice President in our Automotive Business, and I'm excited to talk to you today about Transforming Transportation with the Metaverse and AI. Thanks for joining our webinar. We've got quite a bit of material to get through, so I'll jump right in. From an agenda standpoint, I'll talk in the beginning about the breadth of things that we're seeing for automotive transformation. We'll then jump in and talk briefly about software-defined autonomous vehicles and then get into the meat of the presentation around the metaverse, what we're seeing with digital twins and immersive experiences. And then talk about how AI can apply in automotive to enable those digital twins. And more broadly -- and round things up with some concluding thoughts. And then we'll take questions live from you at the end of the webinar. So please feel free to submit questions throughout the webinar. And again, we'll take them live at the end. So let's talk about the transformation in automotive. There are a number of areas where software is transforming how we have an impact in automotive today, from AI to Omniverse to HPC. Let me talk about 5 areas, all of which will be touched on at some point in this discussion, in how NVIDIA, as an open full stack computing platform, is bringing both hardware and software to bear for the industry. First and foremost is accelerating autonomous vehicle training, development and testing, where DGX SuperPOD and software like NVIDIA, AI Enterprise and Base Command are enabling customers to develop the DNNs for AV and get to market quickly. Simulation is critically important here, starting with synthetic data generation to fill in rare and dangerous corner cases and even capture data in markets where it's difficult. And we provide both hardware platforms to create the synthetic world in OVX server, as well as hardware in the loop with DRIVE Constellation and DRIVE Sim. And modules like IX Sim and DRIVE Replicator are key enablers. I'll be talking quite a bit today about Omniverse as the enabler for the metaverse or digital twins and immersive experiences. And again, OVX Server is what creates those world-class renderings. And I'll say much more about this, but we're increasingly seeing opportunities to AI-enable digital twins with NVIDIA AI Enterprise. And broadly, Enterprise AI and high-performance computing are being adopted in the industry. And our DGX platform is well built to handle these workloads. And finally, while we'll be focusing mostly on Omniverse here, of course, speeding up CAE and CFD workloads and enabling world-class visualization and remote collaboration in this post COVID-impacted world remains critically important. So let's talk about software-defined autonomous. The future car is software defined. What a transformation that we're going through. It used to be that when you would buy a vehicle and drive it off the lot, it immediately depreciates. But today, the opposite is true. We'll purchase a vehicle. And over the years of owning that, the AI software will continue to get better. And through over-the-air updates, the vehicle will get better over time, a complete paradigm shift in terms of how we experience a vehicle over time. Exciting for me is both a consumer of the vehicle, but also being the company driving the enablement of this. If we think about the key elements of the AV development workflow, it starts with data collection and labeling data. But today, of course, synthetic data generation could also be used instead of relying solely on ground truth data from data collection cars. Mapping plays a critical component in mapping the world to help propel a self-driving vehicle. Training networks, deep neural networks, is critically important here. Anywhere from 20 or more DNNs for Level 2 and Level 2+. And ADAS, up to 30, 40. We even have customers publicly pronounce they have 48-deep neural networks to propel their self-driving efforts at Tesla. And then simulation is so important. There's no way you could possibly have enough data collection and AV cars on the world to be able to run every one of the simulations in the real world. So simulation can allow us to test billions of miles in a data center, run through everything in both software in the loop and hardware in the loop testing to ensure every one of the possible global driving scenarios are tested. Now I mentioned some of these deep neural networks that are being developed. And here, you see an example of many of them, from obstacle or object detection, to distance, time to collision. The opposite of object detection, where do I have free space to drive? We're applying LIDAR, everything that we need to be able to see about path and lane planning, reading traffic signs, applying maps, understanding what we're seeing from a high beam parking. Being able to understand how do I approach an intersection? Weight perception is something we as humans do quite well, it's difficult for an AI to do. Again, reading traffic lights. Gestures and poses can be quite difficult to discern. And that's important both externally, where you see someone directing traffic here; but also internally in the vehicle, where we also want to look at things like gaze and eye openness. How do we train models to detect a distracted or drowsy driver? So here you're seeing roughly 20 or more deep neural networks. But as I said, this is quite complex. In other talks, you've heard me go into great detail about the scale that's required, supercomputer scale for the development of these networks Today, I won't be going into that, but I will touch more on the simulation side of things and in particular synthetic data generation. So -- and here, you can get a sense of these deep neural networks running in real time. So let's talk briefly about NVIDIA DRIVE Sim because it's a broad software platform that incorporates a variety of different modes in which you, as consumers, can apply it to your AV development testing efforts. First is synthetic data generation. Super important to be able to create the world and add synthetic data to the ground truth data that you're collecting from your data collection cars. Of course, you would not want to put Norm on a vehicle -- on a bicycle, excuse me, and have me ride directly in front of the vehicle and capture that real-world data when, of course, I'd get run over. So rare and dangerous corner cases, they're certainly one application of synthetic data generation. But we see customers who might be missing data in many markets or examples. Maybe we're missing enough traffic signs in certain markets where synthetic data generation can allow you to create tens of thousands, if not millions of images, where you have gaps in your data set. And synthetic data is super critical for that so that we can accelerate the development efforts, but more importantly, improve the efficacy of the AI models for AV. And then of course as we develop those models, its continuous integration, continuous development. Once we feel that the AI models are effective, we want to start testing them in a simulation environment. And that testing can include both software in the loop, where we can run at various speeds, from real time to superreal time. For really complex scenarios, maybe even run it slower than real time. To hardware in the loop, so that we have perfect bit accuracy at end state driving computer. Instead of being in an actual vehicle on the road, we place it in a fleet in a data center, and now we can have hundreds of vehicles driving in the data center running all types of simulation through every possible driving scenario, from tests like NHTSA and NCAP, to every possible environmental condition, to introducing transient situations like cut-ins, to even applying testing for faults, where you might have a vehicle introduced that's not on the road and be able to see what would happen. So NVIDIA DRIVE Sim is the software platform that can enable both that software in the loop testing as well as that hardware in the loop testing. And then finally, we can use DRIVE Sim for a virtual vehicle simulating factors, including in-cabin experience. Here in the middle, you see depicted one version of an AR heads-up display. We can test all the various different form factors using DRIVE Sim and DRIVE IX Sim. Even early upfront when we're developing the cockpit, before we invest time and more importantly money into designing a cockpit of the future, we can simulate it and test it in all its different form factors and get a sense for what that would look like prior to sinking money into developing that new cockpit. So here, let me give you a sense and play a video so that you can get a sense of the synthetic data that could be created using the drive replicator module. Look at the difficulty here at night, and we're also seeing radar and LiDAR being produced at the same time. We're seeing many emergency vehicles, a difficult lighting condition at night. Could you imagine trying to tell a data collection car to go out and get this scene? We can introduce the idea of creating data for occluded pedestrians, the idea of domain randomization. Here, you're going to see us traversing a parking lot in a variety of different placement of cones and carts, something that would be difficult to do in the real world. So with just one test drive, we can collect a variety of different data sets. And again, we can test every one of the sensors. From -- and create synthetic data for every one of the sensors. So from camera to radar to LiDAR, DRIVE Replicator can create that scenario, drive it and then create the synthetic data, including domain randomization so that you could rapidly accelerate the data assets that you have to be able to improve your AI training. So let's talk about a few success stories here at NVIDIA. We're drinking our own champagne and using DRIVE Replicator in the development and creating synthetic data to be able to develop our own deep neural networks. So going left to right, let's talk about the examples. First one is we call it PathNet. It's our DNN that detects path and lane planning. And one of the things that happens in the real world, but certainly is not within protocol to be able to send a data collection car out there, would be vehicles that are actually driving over the lane lines, whether that's the center line or the end line. And of course, we can't send a data collection car out in the real world to do this, but it's something we need to be prepared for. The second thing that you see visually depicted here are difficult positions of the sun. Here, we're seeing one at sunrise, and that can be so very difficult to be able to pick up. So we used synthetic data generation to both create images, both of these difficult sight conditions, as well as vehicles that would be driving over the lane lines, to be able to improve the efficacy of our model here. And what we actually saw was an improvement in detecting the mean average distance to failure of 10%, quite significant. Secondly, as we move from one version of Hyperion, our sensor configuration, to the next. We wanted to be able to speed up the development of data. So before we even add the actual sensors in, we had models to be able to simulate data of those sensors. And we're able to cut months out by simulating these sensors and creating data assets of those sensors prior to actually having them. A third example are traffic lights. We had this -- it's very difficult to have issues with lights at angles, occluded lights. You might have a tree hanging over. And so here, again, we created tens of thousands of images to be able to improve the ability to spot traffic lights and got almost a 3% improvement to the models. And then lastly, traffic signs. In Europe, we wanted to be able to create a larger set of data assets of traffic signs. And so we created nearly 100,000 images across 6 categories and saw an improvement in our ability to spot traffic signs and appropriately read traffic signs in Europe of nearly 2%. Now 2%, 3%; and of course for path, you see a 10% improvement. Where we're talking about safe self-driving, every little bit of improvement that we can do to the AI models is critically important for safety. So here, you can see some terrific early examples of synthetic data generation, improving our models. And we'll continue to apply this as we continue to advance the development of these models for the industry. So let's pivot and change gears a bit and talk about the metaverse and what we at NVIDIA are building in Omniverse for digital twins and immersive experiences. So what is an Omniverse digital twin? First and foremost, we use USD to create an interoperable and extensible single source of truth, through connectors to all of the various applications. From design to marketing renderings to industrial engineering, we create one source to access all of that data live and in real time. The second thing that is so important is that we create a physically accurate replica of whatever it is. Whether that's the vehicle, whether it's the function of an assembly line, we'll talk about the factory use case, whether it's human ergonomics. And the physics are accurate. It's so important for a digital twin that the physics are actually accurate. Thirdly, and as I talk through here, I'm going to give you a variety of different examples where AI can be an enabler to digital twins and be running both pre and post as we test these digital twins. And lastly, it's not only important that it's physically accurate, that everything is in perfect synchronization when we're running simulations, when we're looking at an entire factory. So we start with the digital twin. Now let me talk about the raft of use cases we see for these digital twins in automotive. So from left to right. And really here, we're depicting them, as you might expect, a workflow. The first task, of course, is how do we design and engineer the next vehicle. I can tell you that, with the shift from ICE to EV, there's a great deal of work that we're seeing from our customers, who, as they're designing that new EV platform and many going full EV, they're applying Omniverse to the design and engineering collaboration. It's not uncommon for a very large brand with many makes and models to have hundreds of design reviews a year. And not uncommon for it to take weeks to be ready for these design reviews. What if, through Omniverse, by having live connectors into all the design data, we can have each of those individual applications being used in real time, do a real-time design review, make changes and see those propagate in real time and cut really the preparation time really for weeks to same day or days? This allows for so much better iteration which of course creates better designs. Simulation is super critical. You heard me already talk about AV simulation with DRIVE Sim, where we can test the entire AV software stack, every possible global driving scenarios. If we had 100 of those base driving scenarios, 100 environmental conditions, 100 transient situations, 100 faults, that's 100 million scenarios to test in simulation for autonomous vehicles. And then of course the exciting new opportunity for cockpit interior design. Again, all of this happening in DRIVE Sim. Factories of the future build is an absolutely terrific use case. We're seeing great adoption immediately following this. We're going to give you a sense of BMW's factory of the future. And here, we're applying the AI to train robots, another terrific use case for synthetic data generation for robot training, whether pick and place or the actual robots doing the assembly. Simulating the movement in the factory, including even human ergonomics, so a fully operational digital twin. We get millions of square feet of plants being traversed through a digital factory, digital first, simulate first. Then we're seeing great opportunities to sell both the vehicle as well as sell the functions of autonomy and be able to have an immersive experience in the configurator. Imagine VR experience. And I'm going to show you one example of an online configurator and talk through what the world could look like in a 3D VR/AR before you even buy the vehicle. And ultimately also monitor the fleets through a service dashboard using Omniverse, and whether that's fleet management or an individual vehicle in an AR -- XR/AR experience. So to build this out a little bit more, we have a video to follow. But just to introduce the video on BMW, we look at them pulling in all of the data from the physical world to the design of the vehicle, to the plan of the factory, to the humans that will need to work in the factory, to the robots that will traverse and move parts throughout the factory. When we talk about a factory digital twin, all of this needs to be done. In all -- we bring in PhysX to ensure that the physics are accurate, material definition language to ensure that we have the right materials. So we apply the AI for how a robot would move, for example. All of this happens in real time. So why don't we let you take a look. For those who like to see it visually, let's walk you through a video of BMW's factory of the future. [Presentation]

Pretty amazing to see the level of detail. Look at the parts here, just in looking at the powertrain itself, but simulating everything from the human ergonomics to the robots' movement in the factory, quite an impressive work that BMW has done. So now let's talk about not the factory side of things, but the consumer side of things. And of course, you want to be able to get the best information about your products into the hands of the consumers in the best way that they can consume it. So whether we're talking about 3D models replacing physical products, in terms of being able to present on the web and online and through a variety of other experience, introducing the idea of an AI assistant into an online experience and ultimately tying that all together into configurators and virtual showrooms of the future, which we're really excited. And this is already being done, but we're going to see a terrific expansion of this. So let me show you another video of what Rimac has done in terms of early-stage development of an online configurator for their, of course, high-performance product. [Presentation]

Pretty impressive, isn't it? And imagine there what you saw at the end, having a commercial potentially be filmed in Omniverse instead of the traditional mannerisms that we have today. So let's talk a little bit more about how this can manifest itself with the connected customer and showroom experiences. So imagine today, you can go into a showroom and have a VR experience. And in fact, you can do this today with Lucid Motors showroom, where you can go in and see the various configurations of the vehicle. Today, though, what's missing is you're talking to an assistant and asking them to make choices on an iPad to change the exterior color, to see what that would look like, change, different configurations of the interior. So it's terrific what you can do to experience it today, but in the future, imagine also adding an immersive test drive. Here, you see we're responding the lanes. You can see the animal that we spotted on the side of the road. And imagine you're actually, with the VR/AR experience, driving. And you can say, show it to me driving at night, show it to me driving on a highway, show it to me driving in a city, and experience the autonomous functions before you buy the vehicle. What a terrific way to: A, sell more vehicles; and b, sell more subscriptions. We can test all of the various aspects of the configuration. Today, you're seeing the iPad experience. In the future, imagine just being able to have conversational AI applied. And you could just speak out loud into your VR/AR headset with a microphone and just speak out loud what you want to see. Show me the exteriors black. Show me the interiors, this package. In the future, we see great opportunities for AR for things like service. Here, we're seeing we're in the actual showroom. But imagine, in the future, maybe having a FaceTime-like experience where the dealer has assessed some repair, but now has this experience where they show you an AR, the brake pads that need repair. Something where, today, that would just be a phone call and they're explaining it to you. In the future, you can actually see it for yourself. So terrific opportunities for Omniverse here in the connected customer and showroom experiences. So now let's talk about AI and automotive, and I'll start with where AI can be applied to these digital twins, and then I'll go through sort of the art of the possible of a variety of different ways that we're seeing AI applied in automotive. So starting with these opportunities for AI in HPC across these digital twins that I described, and we'll go from left to right. From design, imagine having the AI models to have the optimal supply chain forecast and demand modeling be a part of you designing the vehicle in the right configuration so that you can maximize for customer choice and profit at the same time. From an engineering standpoint, optimizing around price as well as performance, building the optimal configurations. One of the greatest opportunities that we see here is generative AI for design, and I'll say more about that. And then traditional HPC for computer-aided engineering, CFD and CSM. In simulation, SDG, of course, to improve the AI. You heard me talk a little bit about imagine, for configurators down the road, even having recommenders being able to test for how would that intelligent cockpit experience happen here. And then down the road -- and a configurator have a recommender providing dynamic pricing and offers. If I go back in the middle of the build, the SDG for the AI for robotics I've already talked about; AI for logistics, for parts delivery; optimal prep time, whether it's walk time, assembly time; applying AI for predictive maintenance to be able to get the most out of the machines on the floor. We're seeing great opportunities already being done in the industry applying AI for visual inspection and motion tracking to both improve the quality of vehicles coming off the line as well as improve the actual factory operations themselves and the time to produce vehicles. And then finally, in service and fleet management, moving from traditional data science and machine learning to the AI for predictive maintenance, ultimately, if we're out of warranty, having recommenders around loyalty. So a number of things that can be built into these digital twins so that the digital twin is also AI-enabled. Now why is AI important? Really, look no further than what leaders are doing today that have transformed their industries, what Netflix has done, what Uber is doing in terms of the ride hailing experience and music. And our expectations of customers, it's changed forever. We expect to have intelligent recommendations provided to us. We expect the AI to understand us, interact -- and to interact with us in a way that is personalized, that is intelligent. That's the expectation going forward. But the consumer things is not the only one. McKinsey did a study recently, and they looked at what is the value of AI in automotive across all of the use cases. If we look at, while the consumer side and my opening comments around some of these leaders, but we look at manufacturing use cases. Look at nearly $300 billion of opportunity in supply chain management and predictive maintenance. Over $150 billion in opportunity in maximizing yield and throughput and energy optimization, nearly $100 billion in opportunity for logistics and warehouse optimization, nearly $50 billion in opportunity in optimizing around product features and product development. And then there is that $300 billion in these intelligent customer interactions, from sales and marketing to servicing vehicles to even finance and IT. So a significant amount of value that's available to you. And now let's kind of go through these in an art of the possible way through a variety of use cases that I'm seeing our customers adopt and apply AI. And move from, as I said, these traditional data science and machine learning to full AI. The first is in the connected vehicle, where we're seeing vehicle management, whether it's a fleet or predictive maintenance for an individual vehicle, being able to alert you that it's time for maintenance early, before you're going to have a vehicle stranded on the side of the road. To mobility, whether it's ride hailing or eventually in the future as a service offerings, where dynamic pricing and route optimizations. Think of it as inventory. Do I have a vehicle nearby? And so AI absolutely applying here. And then the AI in the cockpit. I would arguably say that we're going to see 2 applications of loyalty for autonomous vehicles. The first loyalty will be, is the AV software networks running well? Do I enjoy my autonomous drive experience? The second is, as we move to these AI cockpits of the future is, does that brand have AI infused throughout the cockpit? Will I have an intelligent experience? Whether I've got my child in the back seat who's gaming, or my spouse is watching a movie. And are they getting an intelligent interaction of what next movie they might want to watch if we're on a long drive? And every other interaction in the cockpit. And loyalty, we know that for large brands, just 1% of loyalty can equate to $700 million of opportunity. So the implications here are huge. Another very interesting application of AI is around connected service. Here, we're seeing a very interesting service application, where you can drive in and it can do that early assessment of the vehicle and what need for repair is being done. But there's the other interactions in the dealership as well, where, conversational AI, if you're in a call center, can be providing recommendations, potentially even doing auto scheduling, applying any recommendations and/or loyalty throughout the experience that you have with the dealership. And we know that this opportunity is also in the billions. And if you just captured 1% of the $75 billion of opportunity that's out there, this could mean almost $1 billion to your brand. Now let's talk about the AI in digital twins for design and HPC, where there's nearly $50 billion of opportunity in the entire industry, nearly $500 million that could be available to you to apply AI upfront, maximize design and engineering collaboration. An exciting new space around generative design. We already have several customers who are experimenting with this. Imagine, if you were to say, show me 2 different brands and how -- if they were to have a vehicle together, what might that vehicle look like? Bring in different aspects of designs from different brands or makes and models and get an early jump start on what that vehicle design could look like, and then iterate from there, cut time out of the design process. And then absolutely significant opportunities over the years for the HPC for CAE and CFD and CSM, as I've already described. In manufacturing, a raft of opportunity here, almost $300 billion of opportunity across the industry. Visual quality inspection is an area where we're seeing increasing application of AI. A number of partners of NVIDIA, by the way, creating terrific software opportunity, and NVIDIA accelerating the use of those kinds of applications. And huge opportunities from driving down warranty and quality costs. And then there's, of course, predicting not just the maintenance of vehicles in connected vehicle, but predicting the right time to do maintenance to minimize equipment downtime in every one of those machines on the factory floors. And finally, autonomous robots. In that BMW example, they've applied both synthetic data generation as well as train the robots to be able to traverse the plants, and did it before the plant was built because they could build that digital twin. So again, terrific opportunities for AI, whether you already have the factory and you want to introduce new robots, or you want to apply it all the way up front in a digital twin before the factory is even built. Then one final art of the possible opportunity is around the AI for supply chain. With today's number of makes and models, and now the mix of ICE and EV vehicles, forecasting demand is more challenging than ever before. We're seeing terrific opportunities to apply AI to improve the forecasting models that we may have traditionally built in the past. Logistics is important, whether it's starting at the factory level and being able to have the right parts planning, in what to order upfront based on the optimal configurations, and what's expected to be ordered from these forecasts, to then being able to have logistics applied within the plant. And ultimately to routing, and whether that's routing in a plant or routing for mobility, terrific opportunities here. NVIDIA has introduced a new software capability we call cuOpt that solves for this really difficult logistics optimization challenge. And then lastly, there's so much. Again, with all the different configurations that we have, so much opportunity to optimize, whether it's upfront, the price of the vehicle and the options on the vehicle, including what's the optimal price for autonomous subscription, to optimizing for parts, to optimizing on spend. So as you can see, there is a variety and a broad set of applications for AI and automotive. And I've only scratched the surface here. What we'd love to do for any of you who are interested is have a deep dive with you to understand your use cases and how we can accelerate the use of AI to drive value to the brand, but also increase the speed at which you can apply AI to your business. So some concluding thoughts to wrap things up, and then we'll open this up to be able to take some Q&A and be able to have a good 10, maybe even 15 minutes, of Q&A. So first of all, don't wait. Get started now. When we think about digital twins and immersive experiences, one of the first places to start is identifying, first, what are the most important use cases for you? Do you start with design and engineering? Do you start with the factory? Are you going to start with a configurator? Any or all of them are going to require design assets, the vehicle assets. But your use case will define which data assets to start with first. So determine, what's your path, what will you start with, what will you go to next? And that will allow us to look at collaboration across the use cases, but the right data assets to get started and start building your solution. Ensure that you're injecting physically accurate simulations into those digital twins. And whether that's a physically accurate twin of the vehicle that's driving so that we can test every one of the up to 100 million different types of simulations for an autonomous vehicle, to testing for the operations of the assembly line, to the human ergonomics. Ensure that you're applying physically accurate simulations and get the most out of synthetic data. Once you've identified the digital twin, think about where can I infuse AI into that digital twin? And independent of digital twins, what are the variety of different areas that you could apply AI? I gave you just a few examples from connected vehicle to connected service, connected dealer. For cockpits, think connected customer. AI for design and HPC manufacturing and supply chain. Stack rank and ask yourself, where is my highest value to my business to apply AI, and what's my highest ability to execute? And do a simple 2x2 quadrant of where should I get going on my AI journey? And we're happy to help you think through that ability to execute, how can we make you successful? When it comes to Omniverse and digital twins, develop anywhere and -- develop once and deploy it anywhere. So you can work on the workstation, you can work in the data center, you can deploy on the cloud. And with NVIDIA's launch plan, everything can be available to you easily, whether that's Omniverse or all the SDKs that are resident within NVIDIA AI Enterprise. So our team at automotive is here to help. I hope I've whet your appetite for AV simulation and synthetic data generation, the metaverse for digital twins and immersive experiences and the art of the possible for AI. And with that, we'll open it up to questions.

[Operator Instructions] Let's get started. Norm, the floor is yours.

Hello, everyone, and thanks for so many questions. We've got nearly 15 minutes of Q&A, so I'll start by taking some questions, and I'll provide the question to you and then the answer. So one question that we got was, can you elaborate on the similarities/differences between software in the loop versus the virtual vehicle simulation? It was one of the slides that we had presented around NVIDIA DRIVE Sim in the end-to-end simulation platform. So software in the loop is primarily going to be used to test the entire AV driving stack. And the reason why many customers, and ourselves for that matter, use software in the loop testing, is it has the advantage of being able to test at real time, but that can be done in hardware in the loop testing. But in software in the loop, we can also test in superreal time. So run some of the scenarios at say, 2x, 3x speed, to be able to test many scenarios quickly. And for very, very complex scenarios, we can also test them in slower than real time. And so software in the loop, again, we're testing the entire AV, so the driving software stack. Now in virtual vehicle simulation, one of the things that we can do there is we can see, for example, a simulation of what AR heads-up display might look like. Where you could imagine seeing bounding boxes or lanes lighting up so that we can get a sense of -- in this case, it's a virtual vehicle way of seeing, is the AI detecting objects? Is it detecting lanes? But where a virtual vehicle goes beyond testing the AV stack is a couple of ways. One, this is where we can actually, in a simulated environment, test in-cabin AI, like distracted and drowsy driver. So that's one example of something we could do for virtual vehicle simulation. Another one is that we can actually apply a new tool from NVIDIA built on top of Omniverse called Drive IX Sim. And we can, in the early upfront design of the cockpit, we can be testing different types of AR heads-up displays, what the head unit looks like. We can test how will it work in Android, for example. We can test if we're going to have GeForce NOW in the back seats and maybe other things in the back seats, what that looks like, and what the interaction would be in the cockpit. And we can do all that testing and simulation before we would invest significant amount of funds and money into building that cockpit. So this is a different use case than AV per se. It's actually using design collaboration, design and engineering collaboration, even in a VR/AR experience, to be able to test the interior design of the vehicle. So quite different than software in the loop in that capacity. So another question. Quite a number of them. I'll stay on the simulation track. One of the questions we got was, can synthetic data be used for perception development and validation? The short answer is it can be used for both. I will tell you what we're seeing for ourselves, but also for the industry. Is that there is a great deal of interest primarily right now in using synthetic data to augment, complement, bootstrap ground truth data for perception development. So the obvious is, of course, as you heard me talk about in the webinar, adding in rare and dangerous corner cases like vulnerable road users, infants and toddlers, and driveways, garages, parking lots, the type of thing that you wouldn't send a data collection car out in the real world for. And then the other example that I gave, traffic lights, sign, et cetera. So largely, synthetic data for perception development tends to be the largest use case for it so that we can improve the efficacy of the AI models. Again, as I mentioned during the webinar, it's all about safety, right, and being able to ensure that we have diversity of data, which can include not just the rare and dangerous corner cases, but ensure we have great coverage for all the possible driving scenarios, including ones that are unique in different regions, for example. Now it can also be used for validation, in particular for those rare dangerous corner cases. Now I expand beyond the question the way it was written and just talk more broadly about how we're seeing the various modes of simulation being done, we're seeing -- and again, this is directional because it varies across customers throughout the industry. Let's say roughly 80% of the testing is being done in software in the loop, in part for those reasons I gave earlier, the ability to run across various modes from real time to superreal time to slower than real time. And then 10% to 15% of testing done in hardware in the loop, where we have the end-state driving computer in a fleet in the data center so we can ensure that we have perfect bit accuracy with all the validation that's being done across all the global driving scenarios. And then we're still seeing customers wanting to do 5% to as many as 10% of testing on the road themselves. So back to the original question, synthetic data perception development and validation. Yes for both. But largely right now, the primary use that we're seeing customers and ourselves is to help augment and accelerate the perception development and the efficacy of the models. Bear with me. Many, many questions coming in live right now. So I got a related question around Omniverse. Can we build large-scale simulations using NVIDIA Omniverse? How can we scale up big study simulations? So this is an interesting one. In fact, at NVIDIA, we even have a use case with Omniverse, where we're actually stimulating the entire earth, looking at weather patterns, dangerous events like hurricanes and tornadoes, et cetera, and being able to apply in a digital twin all of the history of these events and to be able to see how those would play out in a synthetic world. So absolutely, Omniverse can scale up. For AV simulation, for example, just to give you a sense of scope, we're talking about as many as 100 million global driving scenarios that could be tested in AV simulation. For example, there could be 100 base driving scenarios. I think NHTSA has roughly 50 driving scenarios to be tested, NCAP roughly 50. But then we need to multiply those out across all of the environmental conditions, rain, snow, wind, et cetera; transient situations like cut-ins and fault injections. So absolutely, we expect, both in Omniverse as well as DRIVE Sim, the scale to be not infinite, but certainly, we're talking about hundreds of millions of scenarios. So another question on simulation. So the question is, for DRIVE sim, how do you guarantee that synthetic data generation, SDG, is similar enough to real images to uphold some kind of explainable AI and/or generalization to unknown scenes? So the answer to that is that, what we'll do is use the synthetic data, again, to be able to improve the efficacy of the model. But what we can do is compare and contrast. We'll know what the efficacy of the model was built with ground truth data alone. And we'll be able to compare by adding in those images, say adding in net Europe traffic sign. And now with synthetic data, what was the efficacy of the model? And so we can compare ground truth only to ground truth and synthetic data. Quite frankly, if you wanted to, you could build the model on synthetic data only and then compare all 3 and be able to see ground truth data asset have x efficacy, ground truth and synthetic have y, and synthetic only has z, so that you would have a sense of the efficacy of the models, and to the question, be able to explain. Okay. Sorry about that. You can tell I'm fighting off. It's the season for cough and cold, so bear with me as I took a brief sip of water there. Another question, it's pertaining to service. Most reports, including J.D. Power at this point, are that, often, 20% of public EV charging stations can be down at any given time. Could there be opportunities to analyze and simulate the issues and improve the uptime using Omniverse? There's actually -- there's a variety of ways to handle this. Omniverse, so to that earlier question about simulating the earth, you can imagine we could plot every one of the charging stations that are out there and simulate traffic patterns in Omniverse and create a digital twin of the environment. That's one possibility. I talked about infusing AI into the digital twin, though. And one of the things that -- there's a variety of different AI routines that can be used to actually predict, much like you would do if you're making decisions around where to put a dealership, where to put a gas station, not in automotive, but where to put a McDonald's, a Burger King, a Wendy's. We can use similar AI to plot where we should have a charging station based on anticipated traffic, even at what time of day. And of course, depending upon the time of day, we would expect the usage to vary. And further, we have logistics optimization capabilities from NVIDIA that could be used in combination with the AI and the digital twin. So we can tackle it from a digital twin only or we can infuse AI into the digital twin, the network optimization type models, to be able to predict where best to place those charging stations. And again, even logistics and optimization models can be applied. And it can all converge and run within the digital twin as well. One simple question we were asked is, how do I get access to NVIDIA Omniverse? And the answer is through NVIDIA LaunchPad. You can create your experience there. You can also submit and download access to Omniverse. But go to NVIDIA LaunchPad is what I would suggest. For that matter, in the presentation, I also talked about NVIDIA AI Enterprise. And all our SDKs are available today via NGC. But you can also test a LaunchPad experience with access to NVIDIA AI Enterprise as well, including some of the important SDKs, like Riva for conversational AI, Merlin for recommenders. Got a question about some of our customers that are using these various use cases. Well, certainly, you saw the BMW example as a factory of the future. So that's an obvious one. The other announcement that we made at CES was around Mercedes' adoption of Omniverse for work that they're going to be doing for digital first, simulation first. Now there are a raft of other customers that we're working with on design collaboration. General Motors is another one that publicly has announced their collaboration with us back at GTC. But there's a large number that I can't disclose yet. But certainly, in time, I think you're going to hear more and more from us on -- that are using Omniverse for factory use cases, design collaboration. And increasingly, car configurators is another very interesting use case. I think we're down to a minute, Rick, did you have any closing comments you needed to make?

Yes. Thanks, Norm. So once again, I just want to say thank you for joining our webinar. We hope you found this webinar informative. And don't forget that NVIDIA will be hosting GTC, our Developers Conference, from March 20 to 23 this year. We have some phenomenal speakers lined up, so you won't want to miss it. If you'd like to replay this webinar, an on-demand version of it will be available in approximately 1 hour and can be accessed using the same link. Thanks again for joining, and we hope you have a great day.