Ep 145: NVIDIA Leader Talks GenAI + Data: Unlocking new ways to interact with our world

This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life.

Sometimes we talk with solopreneurs or small business owners or startup companies building AI tools. Today we're lucky enough to host a leader from one of the world's largest companies that is really driving and helping to push the generative AI kind of movement forward. So we have the Director of Marketing from Nvidia joining Everyday AI today to talk about how generative AI and data can unlock new ways for us to interact with our world. So I'm extremely excited for today's show and welcome. If this is your first time, my name is Jordan Wilson and I'm the host of Everyday AI. And this is your daily livestream podcast and free daily newsletter helping everyday people like you and me not just learn AI, but how we can all leverage it and how we can make it make sense for us and to grow our companies and to grow our careers. So if you're listening on the podcast, thank you as always. Check out the show notes, a ton of other resources there if you're joining us live. Awesome. Get your questions in. What do you want to know? It's not every day that we can, you know, know, talk with a leader of one of the biggest companies in the world. So before we get to that, as we do every single day, let's do a quick recap of what's going on in the world of AI news. So first, something we probably don't think about AI a lot, but better weather is on the way. So Google has created an artificial intelligence weather prediction model that outperforms traditional government models and accuracy and efficiency. So this is from Google's a Google's AI arm, DeepMind, and they recently released early results from their testing. So this model is called graphcast and it's able to make precise forecasts for extreme events and can be evaluated in minutes on a small computer. That part is key because this model, because of the small form factor, has the potential to save costs and improve forecasts for extreme weather events. And I'm excited about that. Right, like how many times do we just open up the the weather app and it's kind of like a coin flip. So thank you, AI. I can't wait to hopefully see this coming to to my phone soon. Next, Argentina may be the first real world AI election. So it's still real people, still casting real votes. But a super interesting story this morning in the New York Times that goes in depth about how Argentina is going to be one of the first major presidential elections where both candidates are openly using AI generated imagery and videos in their campaigns. So the two campaigns are, the two candidates are employing AI to create images and video to promote themselves and attack each other. So the candidates have kind of tiptoe the line between creative generative AI use but also disinformation, as one campaign did release a deepfake video. This is especially timely here in the US as, hey, even though it's a year away from the 2024 election, we're going to be seeing these AI generated images and ads non stop. Luckily, you know, some companies such as Meta have introduced policies against or at least, you know, requiring political parties and campaigns to disclose when they use AI. Last but not least, Chat GPT is overloaded. Yes, I get it. You're hitting me up in the DMs. I'm just as frustrated. Right. But OpenAI is apparently no longer taking on new chat GPT plus subscribers. So OpenAI CEO Sam Altman just announced on Twitter that the company would not be taking on any new paid Chat GPT plus subscribers as they cannot keep up with the surge in signups that happened after Dev day last week. And it's, the platform is unstable right now. There's too many people, it's crashing. So, yeah, if you want to, you know, get onto Chat GPT plus and you're not yet, you're gonna have to apparently wait. But wait, no, no, like no longer. We're not gonna wait any longer to bring on our guest. I'm, I'm super excited. And hey, if you're joining this live, let me know. Where are you joining us from and what do you want to know about Generative AI and data? I'm very excited. Let's bring on our guest for today. There we go. All right, we have joining us Adam Scrabba, the director of marketing at Nvidia. Adam, thank you for joining us.

Thanks for having me. Jordan. Thanks so much.

Hey, and always I, I have to give the VIP award to anyone that joins us from the west coast, you know, so to wake up 5:30 local time to talk generative AI. Thanks. Thanks for that, Adam.

Absolutely, absolutely.

All right, so, so, so maybe Adam, like tell us a little bit about what your role entails as Director of Marketing at Nvidia. Because if you don't know Nvidia like you all do a little bit of everything, so maybe explain a little bit kind of what your role entails.

Yeah, sure. So I lead, I Lead a group of people within Nvidia that we focus a lot on. We focus on applying AI to infrastructure automation problems whether it's in manufacturing, retail, smart cities. We've been at this for about nine years with this team focusing on leveraging AI and instrumenting and automating really important, the world's most important physical transactions and processes. And, and so my role is, is in helping tell the story, evangelize the platform, connect the dots, build the ecosystem and, and hopefully you know, solve some really, really important problems within our, you know, within it, within a few years, within our lifetime.

You know, what are some things, Adam? You know I'm just, I'm just going to skip, skip to the end here. You know, as, as we talk about generative AI and data and how we interact with our world.

Right?

Because I think that's something that not everyone thinks about. I think people think of generative AI as hey, when I go down and I open you know, a Microsoft Bing Chat or a mid journey or a Chat GPT. But how is generative AI and data specifically how is it going to change how we interact in our day to day lives in the physical world?

Yeah, we could spend all day talking about this for sure. I mean so you know I'll give you, I'll give you a very simple example that, that at least from, from my world, I mean you know, as you and I spoke a little bit but before this we think about, when I, when I talk about automating infrastructure we think about there's a lot of sensors and we, we make sense of it. You know, we happen to use a particular type of sensor, we use LIDAR and cameras and a lot of these sensors are really powerful. But in terms of automating things we think a lot about kind of like turning infrastructure into a robot. And so the robot perceives, it reasons and it acts. And so in the old days before sort of gen AI with convolutional neural networks and sort of the old school deep learning approaches, we could really tackle what just happened or what's happening now. But what's most important is what's about to happen and how can we improve, improve upon it and how can, how can we improve upon the outcome? And I think that's where Jenny is really going to be super, super powerful is that we can finally get beyond the, you know, the perceiving and the reasoning and now we can actually act upon a lot of this stuff and, and interact in a natural way. And, and, and so I mean there's, there's there's so much, there's so much there in terms of developing the solutions or developing any of these applications. Gen AI is going to have a major impact on the development journey. So just, I mean if you're coding, if you're developing, if you're developing these solutions, Genai, we can talk a little bit about how the development journey is going to change, but just how we interact with it, which is kind of your question. For the first time we can interact with natural language and ask questions like, you know, literally like, you know, you and I were talking, whether it's a retailer or a supply chain manager on the, on, on in a warehouse, in a distribution center. You know, we're talking about, this is with, with natural language. Is the truck at the loading dock or when the truck gets to the loading dock, send me a note so I can do something. I mean this, this is, this is, this is transformational and that's so different from the old days. The old days were very rigid tools. If this, then that and now we can open this up and be much more natural. Like we're really talking to agents and the agency of that, the agents will be how we interact with a lot of these applications and that's completely transformational.

Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more but can't really get traction to find ROI on j Gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or, or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on Genai. Yeah, and maybe Adam, let's, let's dive in a little bit deeper on that exact example because I love it. Are we getting to the point? Maybe, maybe a two part question here. Are we getting to the point where that whole process May even be automated where you don't even have to tell the system, hey, when this truck gets here, alert this, will it all be automated? That's, that's part one of the question. And part two, can you explain a little bit? Because, you know, I'm a dork, I follow this stuff, but not everyone does. But even specifically what role do, you know, Nvidia's, you know, you know, products and services play in that scenario? You know, because it's like, you know, there's the chips and the sensors and all of these different things. So yeah, maybe let's just dive in a little bit deeper on that one.

Sure. Yeah, sure. So I mean, to answer your first question, that is entirely. The goal is the entire, the entire goal is to streamline our operations and streamline enterprises, operations, government agency operations such that we don't have to spend a lot of time doing the very rigid rule based things. And to get to that point with that, you're saying, which is much more automated, you know, the agents now learning about what's important to me as a, as a, as a, as an operator, as a manager, as a, I don't know, you know, like we're talking about a supply chain manager. I mean the, the tools will get to the point where all of the sensing is going to be hopefully automated and a lot of the tools and the superhuman vision will be codified and turned into, you know, metadata insights. But that workflow of how we work should be automated and that's, that's definitely where we're headed. But to get back to the second question about, you know, how does Nvidia play in this? So, so obviously. Well, not obviously, I, I'll explain in the not obvious way. So Nvidia is a, is an accelerated computing company. And so we think about, or we, we focus our efforts on building end to end stack software and hardware that enables some of the world's most complex and a lot of them AI workloads to be accelerated, whether it's at the edge, in the cloud, a combination distributed, distributed. And so when I say end to end, that means we help people develop the tools. And so we help, we give people the tools to be able to create incredible applications and make them run incredibly fast and allow them to spread the love, spread, spread that capability and deploy it wherever is most appropriate, whether it's cloud or edge and distributed, like I said. And in terms of the AI, in terms of where we are with AI, a lot of where we began the journey was helping people train neural networks and that's an incredibly compute intensive thing. It just so happens it's beautifully GPUs. The parallel nature of GPUs was beautifully built built for this kind of application. It's not just GPUs and our stack is not just great for training neural networks, but it's also incredibly good at running these neural networks. And as we move to so, so, so that's, you know, we help develop and then we help deploy these applications and we power a lot of what you're seeing. You talked a little bit about ChatGPT. That's, you know, that's, that's the inference. The, the application aspect of it is also is run on GPUs. And as we head towards Gen AI and these incredibly complex, incredibly capable models, the models themselves, as you can imagine, these are little brains. These are really complex things. And all of this requires supercomputing and the supercomputing, every time you mentioned being overloaded with queries, every query is a huge workload. And so that's also where we're headed. And, and, and, and also with Gen AI, you know, I think we can touch really quickly upon things like foundation models and transfer learning. A lot of these LLMs are, you know, takes the entirety of the Internet and, and creates a model based on that. But that's great. But, but, but if you are a company and enterprise, I don't know, pick any enterprise that you think of that doesn't maybe publish their standard operating procedures or the, or the way operators think about their daily work. A lot of that stuff is very bespoke that isn't encapsulated in an LLM. So now we have the world of things like rag, right, you know, retrieval, augmented work and all of that work, all the tuning is also a workload that Nvidia focuses on. You and I spoke a little bit about this earlier and I think it's important to note that the work that I do and that we do generally within Nvidia is we are. Our mission isn't to make things a little bit better or a little bit cheaper. We're really trying to solve problems that have never been solved before and almost create businesses and verticals that have never existed before. So when you go and you endeavor to do that, you have to go all the way and you have to go all the way to end customers and you have to work your way back and figure out what is the ecosystem, what is the go to market. And so that's a lot of the work that I do and that we do is, is and think about how do we actually go and solve really important problems that have never been solved before and work with, you know, with, with everyone. You were laughing earlier about just the fact that, that, that Nvidia is kind of everywhere and we work with everyone and you have to, when you, when you do this kind of work, you really have to, you know, it's, we, it's not enough to just throw out some GPUs and say, you know, good luck everyone, have fun and let me know how it goes. You have to work, you have to be in the trenches with everyone and, and that's, that's what we do.

Oh, don't, don't forget me. We got to talk GPUs. But hey, as a reminder everyone, joining us live, we have Adam Scrabba, the director of marketing at Nvidia. So what do you want to know with, with how Nvidia is really helping to push this generative AI movement forward, how we can use data to interact better interact with the world. Adam, I, I do want to ask you a follow up here because it's, it's fascinating, right? So when you talk about, you know, using generative AI to create solutions for real important problems and maybe problems that most of us don't even know exist yet even I want to know this, even for you personally, is that exciting? Is that daunting, right? Because you know, any great innovation that has helped solve, you know, especially when it comes to technology that has come out in the previous years, there's, there's usually a lot that goes on behind the scenes. So we, what is that process like? You know, whether it's for you personally, you know, your teams at Nvidia that to have to use and to kind of not be charged with, but the ones leading the way to say, hey, we're going to, you know, use gen AI in our, you know, chips or sensors to solve these huge problems, energy, climate, etc. What is that process like and how does it play out?

Yeah, I think, I think personally for me it's, it's incredibly exciting. It's incredibly exciting because I think what, you know, to connect all the dots, we've spent, you know, probably a decade building up these incredible capabilities of, of converting, you know, raw Ross, in our case, raw sensor data into insights and into hopefully valuable bits of information. And I'll get to the point, but I think we spend a lot of time honing things like detection and classification engines and you know, and the baseline of AI. But where we're Headed with Gen AI, it finally, it truly closes the loop. You know, I talked a little bit about automation and perceiving and reasoning and acting and the perception and taking raw data and turning it into something that's meaningful. That's great. But with Gen AI, we can finally close the loop and make the data accessible, make the insights useful to everyone. And so I think that's what personally is very exciting for me because a lot of the amazing early stage AI work was really valuable for only the people that could unlock it because it was very difficult to access. You had to build. There's a lot of, I mentioned rigid tools. There's rigid tools that did something very specific. Specific. And if you could hack, not hack into it, but if you could, you know, crap, crack open the hood and, and ex and you know, literally pull out the insights and integrate that with, with your ERP system, with your business operations systems. That's, that was, that was very powerful. But it took a very special set of cast of characters to do this kind of work. But with Gen AI and Generative AI, you know, in terms of the interactive, the interaction with this stuff, we can finally use connectors that are natural language that allow us to access, anyone can access these insights. I think that's what's, that's what's really going to, you know, in terms of the exponential adoption of, of these technologies and these capabilities. That's what we're seeing. That's what I'm personally very excited about. Taking all the work that we've done and finally putting it into everyone's hands. I think that's really going to be a huge deal and we're already seeing it.

Yeah, and I do, I do want to get back to that. But hey, this is a live show. We have questions coming in, so I want to, I want to throw one out there already midway through the show here. So Jay asking how is Nvidia leveraging its expertise in AI and automation to address challenges in the healthcare sector? That's, you know, I don't, I don't know if this is an area, Adam, that you have, you know, expertise in, but maybe just speaking generally, you know, how can Nvidia kind of leverage that to help out and maybe what are you doing in the healthcare sector?

Sure, sure, sure, sure. So, so yeah, as I mentioned, you know, our team is really thinks of, thinks about infrastructure, most important processes and you know, health, there perhaps isn't a more important, you know, transaction process than keeping people healthy and health care. So, you know, in terms of, you know, we can talk a little bit about sort of smart hospitals. I think there's these initiatives to make healthcare facilities more safe, more smart using sensors. That's certainly something that we're working with. We have an incredible healthcare team within Nvidia that focuses on many aspects of the healthcare market. And so they're heavily using AI. But yeah, I mean, from our point of view, putting, helping, helping address patient safety. You know, those are just, you know, some, some of the things that we can do. And, and so turning hospitals into smart hospitals, that's, that's one area that, you know, that we've been involved in. But yeah, it's, it's a huge area. And I mean it spans physical infrastructure, it spans medical imaging, it spans, you know, automated, you know, surgery. And it's, it's, it's incredible, it's incredible space.

Yeah, it's, it's almost like where, where are you not playing? Right? Where are you not helping? That's, that's, that's probably the bigger question. And you know, so even getting back to what we were just talking about, so I'm even right like sitting here, I'm interviewing you. I have a microphone here in front of me. I have a, a camera, a phone, computers. I have websites open, you know, out my window. You know, there's a business over there that I'm sure has cameras and sensors. Right. So how does Nvidia kind of use all of this information? Because it's in, you know, the chips and sensors are in probably every single product or powering all of the different websites that we use. How does Nvidia really take all of this information and help us improve our daily lives with how we interface with the world? Right. So when we're talking about the Internet of things and we're talking about sensors and you know, physical locations, how does that all come together to improve our day to day lives? To make maybe our commutes better, to make buildings safer? How does it all work and come together?

Yeah, sure. I think it's important first, first to just address the, the, the, the aspect that is we work with thousands of partners to make this happen, obviously. So, so it's not, you know, this isn't exactly, you know, Nvidia driving these solutions. We work with, you know, literally thousands of companies to make this stuff happen and to, and to, you know, help build their applications in the deploy. But, but from, from the point of view of how does this help on a day to day basis? You know, I, I mentioned a little bit about our team really thinking a lot about these really important transactions. And there's a, there's a, there's an efficiency aspect and there's a safety aspect. So one, one way we look at the world and we encourage a lot of our partners to look at them and, or frankly they've probably taught us to, to think about this is, is, you know, particularly with physical processes and using cameras and sensors, is to think about dollar bills. Dollar bills flowing through constrained infrastructure would be one, one way to think about it. So I'll give you a couple examples. You are stuck in traffic and you're trying to get to work or you're trying to go buy something. Your time is money and you're stuck in traffic congestion. That is a huge waste and that is dollars literally flowing through constrained infrastructure, which is our roadways. There's also a safety component to it. Traffic fatalities happen to also be in the United States, the number one cause of death. And that's, it's, it's incomprehensible. It's, it's not, it's not a, it's not something that we should, that we should allow happen. And so there's all sorts of initiatives. One is called Vision Zero that aims to reduce traffic fatalities to zero. And that's entirely going to be using AI and cameras to help solve, to understand how to design and manage our roadways better pedestrians and cyclists. And it's all cameras. And if you think about, if you think about trying to get through an intersection, I mean, I, I encourage any of you to look up when you pass an intersection today and you look up and you're going to see probably between four to eight cameras. Those cameras are actually dangling. The, the cables at the end of those in some data centers just dangling. And they're just dangling there. What we're training to do is to take those cables, connect them into computing, into, into GPUs, turn on the AI, and to hopefully create all the insights that allowed the dots of the world to design better roadways. So that's a very specific example. Think about going to a retail environment. You're going into a store and you're thinking, you know, the future is maybe frictionless shopping. Well, your wallet is in your pocket. Again, dollar bills flowing through constrained infrastructure. You know, you're literally walking through and maybe the customer service is terrible and you don't know where the stuff is. That's another example of where AI and these processes can really streamline a very important and a very valuable process. So it's endless and it's and it's. And then, then you go to, like, manufacturing. And now you're also a product going down a conveyor belt in a manufacturing facility with a lot of cameras, trying to understand where the defect is on that PCB or that shoe or whatever that might be. And again, it's, it's constrained infrastructure. These are dollar bills flowing through. And we can use AI to really transform that process. That's, that's sort of a way to elucidate what we're trying to. What we're really trying to do with this, with this stuff.

No, I love that because that's, that's the real world, right? Like you were saying, stuck in traffic, going to work, going to the grocery store. I'm like, oh, man, I feel that it's like I live a half mile from the grocery store. And sometimes it can take 20 minutes, you know, here, here in Chicago, and traffic is heavy. But I want to take it from, from the real world, Adam, if we can, to the computer world, right. To the virtual. So you said one of my. One of my favorite words, you know, talking about GPUs. So, you know, if you don't know, GPUs are essentially computer chips, right? They're graphical graphics processing units. And Adam, I know you said you work with thousands and thousands of partners out there, but can you talk a little bit about. And, you know, I'll just tell people like, Nvidia's, GPU chips are so far more advanced, more efficient, faster than everyone else's. Right. But can you talk a little bit about how, like, through those GPUs, and through all those partnerships, you're really able to help push this whole generative AI movement forward? You know, I, I was thinking the other day, I'm. All these different softwares I'm using, and I'm looking up the companies, I'm like, they're all using Nvidia GPUs. So can you talk a little bit about that and how that really helps the development cycle as well? You know, these GPU chips of all the products and services we use in the generative AI space.

Sure, sure, sure. So. So first off, I mean, you mentioned GPUs, you mentioned the hardware. And I think people, like, they probably understand, maybe they think about this physical card, this thing that maybe is in your PC right now or it's in your laptop. That's sort of where. That's where the journey really began, is getting, you know, and maybe people know about Cuda. Cuda is, is the. It was sort of the, is the, almost the, the, the, the OS of running parallel computing kernels and workloads on a gpu. And it made exponent, it made things exponentially faster, it made super computing faster and weather modeling and, and, and then, and then, and then AI. AI happens to be incredibly. All these algorithms are really, really parallel and they work really, really well and they're incredibly fast on gpu. So we, we have, we, we started with a lot of people maybe know us from gaming. Gaming was, was, you know, gaming is an, gaming is a, is a, is a simulation engine right that truly we simulate how light reflects off of things in real time. Perhaps one of the most power intensive, the most compute intensive workloads that the world has seen is, is gaming. And now GPUs are everywhere. They're in the cloud, they're in your laptop. And that allowed us to enter, you know, into, to sort of accelerate AI because now GPUs have, are everywhere. It's not an exotic device, it's kind of everywhere. And then we work, you know, end to end on a stack to enable and to make these workloads run really, really fast. And also these are effectively general purpose computing devices. These are nodes that are software defined and so they, they're extremely flexible. So a GPU that used to do gaming can also do CNN work and deep learning and now can do gen AI. And, and it's the same device. And that's the power, the power of the company really is, has been in leverage. Is, is leverage the word, it's a single word, leverage. We leverage as much as we can of our relationships of the GPUs of CUDA and that's how we've been able to, to sort of, that's one of the, one of the reasons why we've been successful in this world. So I mean your question is extremely broad and I'm trying to tackle it but, but, but that's what we do. We do. We are much more a software company than we are hardware People I think know us from, from our hardware. But it takes the entire software stack to be able to make it work, make it flexible, make it run on any device. We have devices, we have Jetson, you know, jets and devices that are like, you know, 10 watts, 7 watts. And we have, you know, devices that are in laptops, my laptop right now we have, you know, in workstations are gaming PCs, all the way into cloud nodes, into super computing, you know, data centers. And the same code runs on any of that stuff. So it's Kind of like this code once, deploy anywhere model. And that's another example of important leverage. So, yeah, yeah, I'm, I'm trying to kind of give you, I'm trying to, you know, shed light on, on kind of what we've done. But that's, that's, that's part of the, the magic.

No, you knocked that one out. It's like, hey, Adam, here is the, like the broadest question possible since Nvidia is everywhere. How does it all work? Right? Another, hey, another great question here from Cecilia. So Cecilia asking, are you using the sensors and infrastructure to create safety in communities? Oh, great question, Cecilia. Adam, how does that work? And is this something that Nvidia is doing?

Yeah, it really is. It's really important. In fact, you know, that, that really when we started this journey within, you know, sort of the Metropolis effort within Nvidia, you know, we really, we really started with public safety and it was partly because we have a lot of cameras that are deployed. And I mentioned, you know, this earlier, there's 2 billion cameras deployed worldwide and it's growing at an incredible rate. And, and that was one of the first places that we sort of, we really spent a lot of time focusing on. And yeah, the answer is yes, we can help in a lot of ways in that, I mean, you know, understanding where there's dangerous situations that might be unfolding, you know, you name it. I mentioned traffic safety, you know, traffic safety and pedestrians. And, and you know, that, that, that alone, you know that that's a huge area that a lot of our ecosystem is focused on. So the answer is yes. And I think it's also really important to, to note and I think it might not be intuitive, but, but the, I mentioned, you know, like dollar bills flowing through infrastructure. That is very, you know, that is sounds very economic and very value based. However, you know, when, when we talk about efficiency and safety, efficiency and safety go hand in hand. And so when, you know, almost in every scenario where you talk about efficiency, whether it's on a manufacturing floor, worker safety, whether it's, you know, pedestrians in smart cities and, you know, better roadways, safety and efficiency go hand in hand. And so we really think a lot about that and that's, you know, you might improve safety and efficiency goes up or you might focus on efficiency and safety goes up. So 100%.

Yeah, it's a great fringe benefit, right? Like to make it like if, if efficiency is the goal, everything's safer. If safety is the goal, everything's more.

Efficient and they're not they're not separate. They're really. I think they're very tied together.

Yeah, for sure. You know, I want to. I want to go full circle a little bit here and kind of go back to kind of where we started. Because, you know, Adam, we talked a little bit about kind of the Internet of things and how it's affecting or impacting how we interact in the real world. So, you know, obviously we talked a little bit about, you know, the work that Nvidia is doing. And I know that, you know, your autonomous robots and smart factories are a huge piece of what you all are working on moving forward. How does. I mean, are we going to be seeing that even more in non warehouse? Like I think everyone thinks of, you know, oh, smart factories and autonomous robots, those are going to be in factories and shipping centers. Are we going to start to see those things and, you know, kind of the quote unquote, you know, everyday workplace, smarter workplaces, autonomous, you know, kind of physical objects in the workplace. Is that something that we might be heading toward?

Absolutely, absolutely. I mentioned it earlier. I think it sounds a little. It might sound a little crazy, but. But I. But I promise it's. It's not. If we think about a lot of these pieces of infrastructure, an office, an airport, these are all going to be. A lot of these processes will be automated and you can think about them like a robot. And. And so the answer is. The answer is yes. I'll give you an example of actually, you know, say. Say some of you might be traveling today or this week. The you, you check in, you go through security, and you're waiting at the gate and it's. And you know, your flight might be delayed. Behind the scenes, the flight from Chicago, you know, that dropped Jordan off is now sitting at the gate. And there's this incredible orchestration, this wildly complex set of events have to unfold. Getting people off the plane, refueling, cleaning, waiting for this, getting the luggage. And there's the guy doing this. And there's like, there's 18 things, you know, that are happening. If you look at that on a chart, there's like, the chart is incredibly long. I've seen it. And it's incredibly complex. And all of this is this really complex set of orchestration. What airlines think about is as soon as the gate drops Jordan off, the time is ticking. Every second that that aircraft, that piece of asset, that piece of infrastructure sitting idle is money spilling out of their pockets. And so what they try to do is they take this chart of orchestration from time zero to time, whatever, 30 minutes, and they try to compress it. And today that kind of thing is very manual with cameras, with AI, we're able to completely, with high granularity, with fine granularity, instrument that process and figure out. And when we do this, we have partners that do this, that compress that time to get you from point A to point B faster. And it saves money and it saves people being, you know, you know, someone being super upset and late. And so that's an example of, that's just one example of where the process of aircraft, literally, it's called aircraft turnaround, is going to be almost a robot and it's going to be automated. But we gave, you know, you and I gave, we talked about retail. The same thing is going to happen with retail and things, same things. Maybe if you go to the hospital, a lot of these things will, will unfold that way. So, yeah, the answer is yes, it is coming out of the industrial, you know, out of the factory, out of an autonomous vehicle, and the same concepts, sensors, reasoning, you know, perceived reasoning and act, and it's going to be in your daily life all over the place. So the answer is yes, and it's happening now. And our team is focused on trying to accelerate that and, and hopefully add a lot of value to the world very quickly.

Yes. Well, hey, thank you from all of us for making that air that, you know, sitting in the air, in the airline a little less seated. You know, it's. That's good, right? No one wants to be stuck on the, on the airplane for an hour or two. So. So, Adam, we've, we've literally talked about so many things. We've talked about the Internet of things and gaming and deep learning and GPU chips, using data to make things safer and more efficient. But, you know, as we wrap up today's show, maybe what is, you know, if, if there's a business leader out there, someone that is, you know, trying to push their organization forward and, and trying to, you know, leverage data and leverage generative AI to, you know, improve their company or maybe make things safer and more efficient. What's that? Maybe one piece, which I know is hard, but maybe what's that one piece of advice that you can give to business leaders and decision makers out there on how they can use generative AI and data to better interact with the world and to improve their company's output?

Yeah, this is a, this is a, it's a, it's a, it's a good question. It's a tough one, I would say. That because this is so new. Because this is so new and it's, and it's, and it really does take a mind shift to kind of, to think about the application of it. Because it's so new. I would say just for everyone, you know, just really start to experiment and to learn. You know, Nvidia is actually an interesting, you know, vehicle. We've got a lot of great, you know, channels, a lot of great vehicles to learn from or connect with with us. But just get started, start somewhere. And I know a lot of people say that, you know, just get started. But it is true because is so new. It's not like there's a recipe. And I, you know, I think a lot of people might say, oh, it's just, you just have to do this, this, this, the reality is this is actually very new and we need, frankly, we need some, we need champions. We need some people in, in the, in, in every industry to, to work with, to figure out and to learn and connect with and engage with. So if you are those, if you're those champions, fantastic. You know, you should connect with us. But I would say just get started and, and, and there's a lot of resources and you mentioned chatbots. You just start to use the tools and start to, you know, immerse yourself in it and it'll hopefully it'll become maybe clear one vector that you can go off and, and truly transform your business. Having, having, having been in business development, you know, in a lot of these roles before, sometimes you really need that one champion and those, that one champion is, is, is somehow is. This is kind of a unicorn within an organization that thinks about things differently, but they see through the clutter and they see through the noise and they spot an amazing opportunity. Those are the people we're really looking for. And if you were one of those champions, I encourage you. You know, you, you have, you have the opportunity to really change your company, your business, your team. And I'm excited and we would love to work with these people. This is, this is, these are, these are how, this is how it all starts.

Absolutely. Gosh, man. I mean, Adam, in a world full of data and sensors and the never ending development cycle of generative AI, you really helped us all walk through this process together. So thank you so much for joining the everyday AI show. We really appreciate it.

Excellent. Thanks so much for having me.

Hey, and as a reminder, yes, we did cover a lot. This is, was almost an impossible episode to go through, if I'm being honest, because Nvidia has a very deep footprint in just about every single piece of hardware and software we use. So as a reminder, go to your everyday AI.com we always recap the interview each and every day, and we do this every day. So thank you for joining us and we hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'.

All. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.