Ep 234: Driving the Future – NVIDIA’s Vision for AI-powered Transportation

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Accelerating Innovation in Transportation with AI - Insights from NVIDIA

The future of transportation is on the verge of a major transformation, thanks to the integration of artificial intelligence (AI) into the industry. From self-driving technology to advanced safety features, AI is poised to revolutionize the way we think about mobility. Business leaders and decision makers need to understand the impact of AI on transportation and how it can drive innovation and growth within their organizations.

AI's Role in Revolutionizing Transportation Industry

AI is playing a pivotal role in revolutionizing the transportation industry. This technology is not only making vehicles safer and more reliable but is also leading to groundbreaking advancements in the design, engineering, and manufacturing of vehicles. AI-powered systems are being integrated into vehicles to enhance safety, enable autonomous driving, and streamline the overall transportation experience for consumers. This integration has ushered in a new era of mobility, where vehicles are becoming smart, connected, and capable of learning from their surroundings.

The Power of AI in Driving Innovation

AI's impact on transportation extends beyond the vehicles themselves. It has the potential to drive innovation across the entire automotive ecosystem – from design and engineering to retail and after-sales service. By leveraging AI, businesses in the transportation industry can create more innovative designs, optimize manufacturing processes, enhance retail experiences, and facilitate predictive maintenance. The ability to harness the power of AI can set businesses apart in a competitive market and allow for the development of cutting-edge products and services that cater to evolving consumer needs.

AI-Powered Simulation and Digital Twins

One of the most transformative applications of AI in transportation is the development of simulation and digital twin technology. With advancements in AI, businesses can create virtual replicas of vehicles, manufacturing plants, and entire factory environments. This technology enables designers and engineers to collaborate seamlessly, optimize design processes, perform virtual testing, and predict real-world outcomes with a high degree of accuracy. The use of digital twins not only expedites the design and manufacturing phases but also enhances operational efficiency and paves the way for continuous improvement in the transportation industry.

End-to-End Approach and Continuous Improvement

The integration of AI in transportation represents an end-to-end approach to innovation. From the design phase to retail and after-sales service, AI empowers businesses to continuously improve their products and services. This dynamic approach allows for the enhancement of vehicle functionalities through regular software updates, ensuring that consumer experiences evolve alongside technological advancements. By embracing AI's capacity for continuous improvement, businesses can set a new standard for innovation and customer satisfaction within the transportation industry.

Maximizing Safety and Efficiency

AI's role in transportation extends beyond innovation; it also plays a crucial role in enhancing safety and efficiency. With AI-powered systems, vehicles can analyze vast amounts of data in real-time, detect potential hazards, and autonomously make split-second decisions to ensure the safety of passengers and other road users. Additionally, the predictive capabilities of AI enable businesses to optimize operational efficiency, reduce downtime, and deliver a superior driving experience to consumers. The fusion of AI and transportation has the potential to minimize accidents, enhance operational effectiveness, and ultimately revolutionize the way we perceive safety and efficiency on the road.

Conclusion

The integration of AI in transportation represents a transformative force that is driving innovation and redefining the dynamics of the industry. Business leaders and decision makers have the opportunity to embrace AI and harness its potential to shape the future of transportation. By leveraging the power of AI, businesses can drive innovation, maximize safety, and deliver exceptional value to consumers, positioning themselves as pioneers in the evolution of transportation technology. As we move forward, the seamless integration of AI into the transportation industry will not only enhance the way we experience mobility but also pave the way for a new era of innovation and progress.

Topics Covered in This Episode

1.  NVIDIA's Role in the Automotive Space
2. Impact of Increased Computational Power in Vehicles
3. Impact of Generative AI in Vehicles
4. Automotive Simulations and Safety Measures
5. Future of Transportation


Podcast Transcript

Jordan Wilson [00:00:02]:
What does the future of transportation look like, especially when AI is getting more and more involved? Well, you know, are we going to have flying cars? Is everything going to be self driving? I don't know. I don't have all the answers. But today I have a very special guest who is going to be able to tell us what the future of AI powered transportation looks like. All right. What's going on? Welcome, everyone. Thanks for joining us. My name is Jordan Wilson and I'm the host of Everyday AI. We're a daily livestream podcast and free daily newsletter helping everyday people like you and me not just learn what's going on in the world of generative AI, but how we can all leverage it to understand the world around us and to grow our companies and to grow our careers.

Jordan Wilson [00:00:47]:
So if you're joining us on the podcast, thank you. If you're joining us on the livestream, it's great to have you at this special time, not our normal time, but we're doing double duty here because we are reporting live from NVIDIA's GTC conference here in San Jose. I'm extremely excited. I'm looking over the show floor. The future of AI is happening literally right outside this window at NVIDIA GTC. And if you feel like you missed out, don't worry. You can still sign up right now and attend the conference for free virtually. If you missed out on some great sessions like CEO Jensen's, keynote, you can catch that as well as so many other great sessions and workshops from world and industry leaders in all different aspects of artificial intelligence.

Jordan Wilson [00:01:33]:
And if you do sign up, make sure to check out the link in our show notes so you can enter as well to win, an NVIDIA GeForce GPU as well as, DLI Learning credits. Like, y'all, like, there there's literally the experts leading experts in the world like our guest today that you can go learn from, so make sure to check that out. But, I'm excited now to talk about the future of transportation and AI. So, please help me. Welcome to the show. Let's bring them on. There we go. We have Danny Shapiro, the vice president of automotive at NVIDIA.

Jordan Wilson [00:02:08]:
Danny, thank you so much for joining the Everyday AI Show. Alright. So sorry. I think I think we lost your audio, Danny. We'll do it one more time. Thanks for joining the show.

Danny Shapiro [00:02:20]:
It's it's great to be here. Alright. Hey. Can you yeah.

Jordan Wilson [00:02:23]:
Can you tell us a little bit about what you do, in your role as vice president of automotive at NVIDIA?

Danny Shapiro [00:02:31]:
And so our our team is really focused on revolutionizing the transportation industry. And so we're working with 100 of carmakers, truckmakers, robo taxi companies, shuttle companies, to make transportation safer and more reliable. We're bringing supercomputing into the vehicles and, adding extra safety and convenience features through sensors and and supercomputing. And, it's just it's really a lot of fun what we get to do. We work with a lot of different companies. But, the reality is the roads are dangerous, and AI is gonna make us all safer.

Jordan Wilson [00:03:11]:
Yeah. And and, Danny, can you maybe explain this a little bit? Because, you know, I'm thinking I'm like, hey. My, you know, 18 year old Honda probably doesn't have, any NVIDIA parts in it. Right? But but how, you know, specifically is NVIDIA? Because a lot of people still think of NVIDIA, as a as a GPU company, and it's obviously so much more than that. But how specifically does NVIDIA work in the automotive space? You know? Is it the the the newer cars, self driving? How exactly does that work?

Danny Shapiro [00:03:38]:
That's a it's a great question. So we started working with the the auto industry more than 2 decades ago, and and it really is kind of the types of things you're alluding to originally is graphics. Right? People would design cars using NVIDIA graphics. The engineers would use their, you know, computer aided design tools running on a workstation with NVIDIA inside, and we would do all kinds of other, you know, crash tests and things like that in simulation. So that was really the early days. Then we started bringing our graphics inside the car. And so back in the the early 2000, you know, this push of consumer electronics was taking off the iPhone, tablets, but the electronics inside vehicles were pretty antiquated by comparison. So we helped pioneer bringing the touch screens to vehicles, digital instrument clusters, rear seat entertainment, the head up displays.

Danny Shapiro [00:04:29]:
Again, anywhere there were pixels, you'd look to NVIDIA to bring those into the car. Now one of the things we had to do was really transform ourselves as a company into an automotive grade company. Meaning, if you have a mobile phone and you leave it on the dashboard of your car on a hot summer day, it won't operate. Right? You get that little alert. But inside of a vehicle, whether it's super cold temperatures in the winter, hot summer days, it has to operate. And so there's also a lot of other harsh conditions inside of a vehicle, the shock and vibration and dust. So we basically create a version of our products. And in this case, it's the NVIDIA DRIVE product that is a system on a chip, we call it SOC, that integrates a CPU, a GPU, and a number of other processors on a single die to handle all the different types of computation that you'd want to do in a car.

Danny Shapiro [00:05:21]:
It's a little different, of course, than a phone or or a laptop or a data center, but that's the flexibility of NVIDIA to have this architecture that can scale across many different platforms.

Jordan Wilson [00:05:33]:
Can you, can you tell us, you you know, what is what is new? What is happening? You know, I'm I'm here at the conference, you know, in San Jose, and I do see, you know, vehicles all over here with with the NVIDIA signs. I mean, what is what is new and what has just been announced, you know, with NVIDIA here at GTC in the automotive space?

Danny Shapiro [00:05:53]:
Again, another great question. So, yeah, we have vehicles, all around the show floor. There's a semi truck parked in front of the convention center powered by NVIDIA. We have Mercedes Benz, in the lobby. Their brand new CLA concept car, which is the precursor to their next generation of vehicles that will all have NVIDIA Processors inside. Lucid is there, Volvo, Polestar. These are all brand new cars, that will start shipping later this year. And so what what we have then is essentially this supercomputer, an AI brain inside the vehicle.

Danny Shapiro [00:06:25]:
It's connected to different sensors, cameras, front facing, rear, side cameras, radar, lidar, which is a laser scanner in many cases. And so all of that information is helping the vehicle understand what's around it. It can sense the lane markings, of course, see signs, street lights, other cars, bicyclists, pedestrians, fixed objects. And it takes all that data, brings that into the NVIDIA DRIVE system, which then has to figure out where it's safe to drive and ultimately control the vehicle steering, accelerating, and braking. And and,

Jordan Wilson [00:07:03]:
hey, if your son

Danny Shapiro [00:07:05]:
Go ahead.

Jordan Wilson [00:07:06]:
No. No. No. Yeah. Sorry. Sorry. Go ahead. Yeah.

Jordan Wilson [00:07:07]:
Talk about, Danny, what you were just saying about announcements.

Danny Shapiro [00:07:10]:
Yeah. So at at the keynote, we announced that BYD, which is the world's largest EV maker, is adopting our newest product, NVIDIA DRIVE THOR. So that's our next generation supercomputer. It's 4 times more powerful than the one we have in production right now. It'll be available next year. And this is gonna enable all kinds of new AI capabilities for sensing outside the vehicle as well as inside the vehicle. So knowing who's in the car, are they paying attention or not, detecting if there's anything left behind in the car. So there's just a whole new development going on with respect to software, that will be able to control many different aspects of the car.

Danny Shapiro [00:07:53]:
Yeah.

Jordan Wilson [00:07:53]:
And, hey, for for any of our livestream audience like Douglas here who's interested in automotive, go ahead. Ask a question. You know, we'll, we we we have one of the leaders in the automotive industry joining us live today. But, you know, one thing, Danny, that you brought up there is, you know, a lot of these new advancements with NVIDIA's chips. So you said that Thor is, you know, giving you about 4 x the power. You know, we even saw on on the, kind of consumer side with with the Blackwell and, you know, the how much more powerful, you know, even this generation of chips. NVIDIA was already the leader. And then you come out with chips that are, you know, exponentially more powerful.

Jordan Wilson [00:08:28]:
So, I mean, what does that even mean for, you know, the future of transportation? Right. Like, is there a certain limit, right, where where your consumer vehicles can't even use the power of these chips? Or does this just mean that we are gonna be seeing new capabilities in our cars that maybe we just had no clue were possible?

Danny Shapiro [00:08:48]:
No. You're you're absolutely right. I think if you look at the history of computing, whether it's personal computers, supercomputers, mobile devices, There's never enough compute. Right? The software gets developed to take on new features, new capabilities, but it always then hits that upper limit, and then the next processor comes out. And so that's where, you know, we continue to innovate, create higher performance systems, lower energy consuming systems as well because that's really key, especially in an electric car. But, I think the the thing to to realize here is more computation equals greater safety. So as you have more cameras or more sensors on the car, higher resolution sensors, more complex algorithms, There's just a lot of diversity and redundancy required to ensure safety. So the more compute you have, the safer the vehicle can be.

Danny Shapiro [00:09:41]:
And so for example, we can have a system that's detecting pedestrians and that's gonna take up a certain amount of computation, but then we also have a neural net that's looking for what we call free space, the absence of objects. And so these are 2 different algorithms, but together, they ensure it's like a double check. Right? We make sure there's nothing in front of us, and if we see things, we know to avoid it. And that just requires double the computation then because we're running 2 algorithms. Then multiply that by lane, marking detection, sign recognition, pedestrian detection. So there's dozens and dozens of these neural networks that are running in the car to make sure that we're able to drive safely.

Jordan Wilson [00:10:22]:
You know you know, Denny, speaking of that, you you know, adding, you know, these this this new function, functionality on the road that maybe wasn't possible before. It seems like the industry has really been working, you know, toward the the the full self driving and autonomous driving for a while and, you know, different companies are are finding different levels of success. Do you think now that maybe, you know, compute is is maybe less of an issue for for what is capable? Do you think that over the, you know, the coming months and, you know, year or 2, are we finally gonna see the point now where, you know, full self driving and autonomous vehicles are more of the norm versus the exception? I I

Danny Shapiro [00:11:03]:
think so. I think that it still is gonna take some time to ramp up. But the industry really underestimated the complexity here. If you go back 10 years, let's say we're in 2014, 2015, all the automakers were predicting by 2020, we're gonna have full self driving on the road. And so we were driving towards that goal. It's hard. It's really, really hard. This is perhaps one of the most complex AI challenges, in the world, and lives are at stake here.

Danny Shapiro [00:11:32]:
So safety is key. So we wanna make sure that that we get it right. And so we're really not pushing for a specific timeline. We're we're pushing for a level of safety that far exceeds what humans, can achieve. And so there's a number of tests going on in different regions with car companies, with trucking companies, with robo taxi companies, and we're seeing great success. And it's it's a global thing. We're showing WeRides robo bus on the show floor, and they're operating autonomously in Beijing, in Shanghai, in Abu Dhabi. So there's different trials going on, around the world.

Danny Shapiro [00:12:09]:
Aurora, their truck is driving autonomously, in Texas on on the highways. In the Bay Area, there's a ton of tests going on from a a wide range of companies here. You know, it's all powered by NVIDIA. But I think that the key thing is to ensure that, you know, that we can handle the basics. It's those things that almost never happen that will, you know, cause the system to trip up. So one of the things that we're doing is using simulation to be able to train as well as test these vehicles. So it kinda goes back to our roots a little bit in in video game technology, but we'll create virtual worlds that are digital twins of the environment. So the roads, the buildings, other objects that are fixed, plus the other cars, trucks, pedestrians, bicyclists, motorcycles are all in the simulation.

Danny Shapiro [00:12:58]:
And so we simulate the actual signals that the cameras, the radar, the lidar would be generating the real car. And we run that through the computer that's in the data center, but it's a digital twin of what would be in the real world. And so the computer that's in the data center doesn't know it's not on the road. It thinks it's driving on the road and making driving decisions. And so that way, we can create all kinds of hazardous scenarios, without putting anyone in harm's way. We can test what happens in different weather conditions, different road conditions, you know, having a a virtual child run out in front of the car at night Mhmm. And do we detect that? So, it gives us amazing ability to ensure the safety of vehicles and the software before we put it on the road.

Jordan Wilson [00:13:44]:
And and and, Danny, can we talk about that a little more? Because, I think it at least even for me personally, my eyes are on the omniverse here. Right? And and and the keynote, you know, seeing so many great, you know, real world applications for for safety, you know, climate change, you know, so many different applications that are that are positive. But can you just talk a little bit about for our audience that maybe isn't super familiar with the Omniverse and also now with increased computes? You know, because now I'm guessing you can run way, way more, you know, simulations simultaneously because you have you know, more computing power. So you can you talk a little bit about what you're even capable to do on the safety side with with this increased functionality of the Omniverse?

Danny Shapiro [00:14:27]:
Yes. So Omniverse is our platform for digital twins and and simulation. And so we can, make it we do make it available basically to every single department in let's just take an an automaker, for example. The designers can be working in a variety of different tools and bring their designs into Omniverse and be able to collaborate. So somebody can be working on the wheels in one software application. Someone else could be working on the body designer than others. Maybe somebody's working on the headlights. And so Omniverse lets them bring it all together and collaborate and see what everything looks like.

Danny Shapiro [00:15:00]:
So this is in photorealistic rendering and using real time ray tracing. So you you get all the reflections and and it just it looks beautiful, the materials. Right? And so that then can be shared with the engineers who are trying to figure out, well, how do you build this thing? And then so they can have a common design and design, and they can then go back and forth and collaborate. We can bring that into, all kinds of structural engineering and bring it into a, a virtual wind tunnel simulation. So a designer can create something, and you can see what's the coefficient of drag, how how fuel efficient will this design be, and And, you know, how easy is the manufacture? We're working with a number of automakers, BMW, Mercedes Benz who are creating factories. The entire factory is going into Omniverse now. And so we can model the layout of the factory before the factory is even built. We can see how the robots are all going to interact.

Danny Shapiro [00:15:57]:
We we do a simulation of the car being built. And, so, yeah, you can see just a still here showing this entire factory. It's a massive amount of data. Every robot here now is following its programmed path and, will operate. And so we can make sure that there are 2 robots as they swing, they don't collide. So we do that in simulation first as opposed to then realizing on the factory floor that there's a problem and then you have to move things around or move a wall or or make the ceiling taller or whatever it is. So this is just an amazing tool. And the amazing additional part is that that factory runs in simulation so we can see the vehicles being built in the actual simulation.

Danny Shapiro [00:16:40]:
And so then in the real world, it makes it very easy to to bring the factory up.

Jordan Wilson [00:16:44]:
Yeah. And and, you know, if you're listening on the podcast, we just have a a photo here that kind of shows the concept of, you know, what, you know, this can look like, you know, in the Omniverse, you know, kind of being able to replicate what is actually happening, you know, in a real world manufacturing plant and and being able to replicate that digitally in in the omniverse. So, you know, I'm I'm wondering, you know, Danny, like, what's and and we don't have to go into specifics, but, you know, in general, how much of the decision making behind the future of of automotive? Because, you know, obviously, you know, NVIDIA is not making cars, but you're working with, you know, some of the world's largest car manufacturers. But but how much of the actual, development of of of future, you know, safety, future AI technology, how much of that is actually, you know, originating from the the omniverse verse versus maybe, you know, kind of real world testing?

Danny Shapiro [00:17:41]:
No. I it's a really good question, and it's moving in the direction of everything will be simulated first. You know, that's that's the principles we're using at NVIDIA in making our chips we simulate it all. Our our new building, you were just at our headquarters, that that opened recently. That whole building, it's, you know, it's a smart building. It's kind of a living entity. It's software defined. There's all kinds of systems monitoring.

Danny Shapiro [00:18:06]:
We simulated everything in that building before it was built. The angle of the glass and the the position of the sun, it's designed to let maximum light in, but still not create a lot of heat inside the building. And so we're able to model the physics of systems now in Omniverse and use that to build better products.

Jordan Wilson [00:18:28]:
And, hey, a great a great question here from from Douglas. So, Douglas, thanks for this question. So he's asking, how does the AI for a vehicle differ for on premise versus a connected vehicle? If I drive a vehicle in the mountains, will the AI stop? That's that's I wasn't even thinking about that. But, yeah, Danny, how does that work? Right? Like, if if is most of all the AI being on yeah. Go ahead.

Danny Shapiro [00:18:51]:
It's a super good question. So I think it's maybe a little bit of a misconception too. So connected cars, everyone talks connected cars, and and I think that's great. Right? You wanna have a a cellular connection because you get your nav updates. You can check the weather. You can talk to Siri if you want to. You can stream Spotify. For autonomous operation, though, and for safety, nothing goes to the cloud.

Danny Shapiro [00:19:14]:
It's all it has to all be on board. So you can't rely on, you know, your pedestrian detection system having to go to the cloud to decide if that's a pedestrian or not. But it's it takes too long, and the connection is too unreliable. So all the sensor data goes straight into the supercomputer, our NVIDIA DRIVE system onboard, and those decisions are made in a fraction of a second. Right? We're looking at video, which is 30 frames in a second. So we have 1 30th of a second to analyze all the pixels in those frames of video and decide, what's a pedestrian or what's a car or what's a lane. The connectivity piece then is software updates. Your car is parked overnight, and you could get an update just like your phone, which would add new features and new capabilities.

Danny Shapiro [00:19:59]:
And then if you if you're trying to use a system and say, hey. I'm interested in finding a a good Japanese restaurant, then it you know, it'll go to the cloud and and get that data and come back.

Jordan Wilson [00:20:09]:
Yeah. It's it's something that I I I don't really think about a lot, Danny, but it sounds like, you know, for the most part, especially if you have a a newer connected car, you know, powered by NVIDIA, that your your your car is probably smarter than your, you know, maybe your computer if you're using a basic laptop or a standard cell phone. It sounds like, you know, whatever is is the the the AI powering your your car might be just as powerful or maybe more powerful. Is is that the case?

Danny Shapiro [00:20:37]:
Oh, absolutely. Again, it's it's tuned for specific applications. You know, it has the the software. It's designed for, you know, getting sensor processing in in this case. But our new Drive 4, it's using our Blackwell architecture. So those really powerful GPUs that are going into the data center, that same architecture is leveraged and goes into drive Thor. And this will be a 1,000 TOPS processor. So that's 1,000 times a trillion operations per second.

Danny Shapiro [00:21:10]:
So it's a massive amount of compute for artificial intelligence and sensor processing.

Jordan Wilson [00:21:16]:
Yeah. And and and and one thing that I'm, you know, thinking about now, Danny, because I've I've even heard it from, you know, certain people I've been talking out to on this exhibit floor, and people are saying, you know, hey. Now with Blackwell and, you know, all this additional compute, you know, some people are are are rethinking, you know, what's possible with with with their product or or service. You know, I'm even curious for you. You've obviously, you know, known about what's going on, you know, at NVIDIA. So you've had time to think and to process what all this additional compute that's been announced here at GDC actually means. But what do you think that it means outside of, you know, what's what's directly in front of you? You know, people are always saying, oh, are we gonna have, you know, flying cars? Is every single vehicle gonna be, you know, fully self driving? But what does the the the future beyond tomorrow look like with all of these new, a AI powered announcements?

Danny Shapiro [00:22:08]:
Yeah. So, obviously, the the big trend and it's kind of taking over all industries is generative AI now. The ability to have artificial intelligence, not just do, you know, pattern recognition, which was kind of essentially the the past way, being able to identify things. But now, take content and create new versions of it. So chat g p t, you type in text and text comes out. We're gonna see a lot of large language models running in the car. So you'll be able to speak naturally to your car and your car will answer intelligently and that will happen locally on the car. So we'll get trained on everything with your car, the brand.

Danny Shapiro [00:22:48]:
You could ask it any kind of question. It can also maybe, tell you about things that it it's it's sensing in the car. Maybe there's a new rattle or something. And so it can understand what's going on and maybe alert you or go ahead and just book a service appointment. And if it is self driving while you're sleeping, it would drive the car back to the garage and and have that repaired made, and then it'll come back to your house in the morning. We do believe that everything ultimately will be automated and autonomous, and it's it's a process that's gonna take a long time. There's lots of lots of cars in the world, and a lot of them don't get refreshed, you know, for many, many, many years. But, we're working with a lot of automakers to to bring this technology to market, bring it to consumers, and make it affordable.

Jordan Wilson [00:23:31]:
So so so, Danny, in, you know, your role at NVIDIA, you know, you have your hands on really the the future of transportation. You're right. Like NVIDIA is working with the largest, you know, hardware and other software manufacturers in the transportation industry. So I'm curious, what are you most personally, excited about in the future of transportation?

Danny Shapiro [00:23:51]:
I think, you know, you look at your phone and you get, you know, periodic software updates, and all of a sudden, the interface is better. There's there's some new apps, and it's like, it's kind of you get a sense of joy from getting, things getting better. And I think that's really the what's gonna happen with your car. It's not the, the old model where it was the best it's ever going to be the day you drive it off the lot. But moving forward, you have a software defined vehicle. You have NVIDIA DRIVE inside a very powerful computer, and it will just get better and better with each software update. So your next car you may buy, and it it doesn't have this fully autonomous capability, but during its life, as the software is enhanced, as regulation changes and autonomy becomes, you know, the norm, your car can get a software update if it has the sensors and the compute on board, to handle it. So maybe first it'd be a highway autopilot, then maybe an urban autopilot.

Danny Shapiro [00:24:45]:
Maybe there's valet parking. You can go to the restaurant, go to the movies. You get out of the curb, and the car is just gonna go park itself.

Jordan Wilson [00:24:53]:
You know, we've we've covered just about, you know, so many different things, you know, both from, recent advancements that NVIDIA has been making, you know, the future of of full driving autonomous vehicles. But, you know, Danny, as we wrap it up here, what's what's the one take home, you know, piece of advice or message that you really want people to know specifically about how NVIDIA is powering the future of transportation with AI?

Danny Shapiro [00:25:18]:
Yeah. We we call it, you know, an end to end approach. And it really starts from that design of the vehicles. We're helping, automakers and truck makers create more innovative designs, more efficient designs, and and safety designs through the engineering and manufacturing, the testing, the simulation, and even retail, being able to take, all that information through to the consumer and help them make purchasing decisions and, you know, streaming a car configurate or in a web browser on their mobile device and even take a virtual test drive. So it's the buying experience and then even the service and maintenance. Omniverse is gonna play a a large role there too. And, so we're just really excited by the technology that that we're bringing to the market and seeing that now our customers and partners, build on it and really create cool products of the future. I think that many people are potentially afraid of autonomous driving.

Danny Shapiro [00:26:13]:
There's a lot of sensationalized stories in the press, but the reality is that there's, there's a lot of hazards and dangers on our roads and a lot of accidents, injuries, and fatalities every day. And as a society, we have come to accept it. But I believe this technology, is gonna dramatically reduce those figures and and make the world and our roads much safer.

Jordan Wilson [00:26:36]:
Danny, you've helped us so much better understand what's happening in the transportation and automotive industry and even walked us through everything new here. All the new announcements with AI and in at NVIDIA GTC. Thank you so much for your time and joining the Everyday AI Show. We very much appreciate it.

Danny Shapiro [00:26:54]:
It's a pleasure. Thanks speaking with you guys.

Jordan Wilson [00:26:57]:
Alright. And, hey, there was a lot there. Don't worry if you missed it, if you're out, you know, on your walk and maybe your reception cut off for a second. We recap every single artic every single, episode on our website. So make sure to go to your everyday ai.com. We're still gonna have more information in there about what's happening here at NVIDIA GTC. There's so much we couldn't get to, so we're gonna have more in each and every newsletter throughout the rest of this week and probably early next week. So if you haven't already, go to your everydayai.com, sign up for that free daily newsletter, and we'll see you back tomorrow and every day for more everyday AI.

Jordan Wilson [00:27:31]:
Thanks, y'all.

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