Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 145: NVIDIA Leader Talks GenAI + Data: Unlocking new ways to interact with our world

Episode Date: November 15, 2023

Today we have the opportunity to sit down with a leader from one of the world's largest companies helping to push GenAI forward.  Adam Scraba, Director of Marketing at NVIDIA, joins us to discuss how GenAI and data can unlock new ways for us to interact with our world.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Adam and Jordan questions about AI and dataUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:[00:01:30] Daily AI news[00:04:15] About Adam Scraba and NVIDIA[00:05:55] How AI and data will change our world[00:10:20] NVIDIA's role in AI[00:20:00] Ways NVIDIA uses data to improve daily lives[00:24:30] NVIDIA's GPUs and their impact[00:28:30] How NVIDIA creates safety in communities[00:30:40] Smart factories and autonomous robots[00:34:45] Adam's final takeawayTopics Covered in This Episode:1. NVIDIA's Role in AI and Automation2. Gen AI and its Implications4. How NVIDIA Uses AI and Data To Improve Daily Lives5. NVIDIA's Role in SafetyKeywords:GPUs, computer chips, graphical processing units, NVIDIA, advanced, efficient, AI, generative AI, gaming, devices, software company, software stack, public safety, understanding, dangerous situations, efficiency, safety, autonomous robots, smart factories, warehouses, shipping centers, everyday workplaces, accessibility, healthcare sector, sensors, collaborates, optimizing physical processes, retail experiences, manufacturing processes, streamlining operations, increasing productivity, natural and automated interactions, accelerated computing company.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

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. Meet Firefly AI Assistant, now live and Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. 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. 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's Jordan Wilson, and I'm the host of Everyday AI. And this is your daily live stream podcasts 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, 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 news. 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 AI arm, DeepMine, and they recently released early results from their testing. So this model is called Graphcast, and it's able to make precise forecast 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 forecast for extreme weather events. And I'm excited about that, right? Like, how many times do we just open up 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 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 candidates are employing AI to create images in 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 deep fake video. This is especially timely here in the U.S. 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 nonstop.

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 Open AI 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 and signups that happened after Deb day last week. And the platform is unstable right now.

There's too many people. It's crashing. So yeah, if you want to get onto chat GPT Plus and you're not yet, you're going to have to apparently wait. But wait no, no longer. We're not going to wait any longer to bring on our guests. 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 have to give the VIP award to anyone that joins us from the West Coast. You know, so to wake up 530 local time to talk generative AI. Thanks, thanks for that, Adam. Absolutely, absolutely. All right. 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 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 in instrumenting and automating really important, the world's most important physical transactions and processes. And so my role is in helping tell the story, evangelize the platform, connect the dots, build the ecosystem, 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 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 GPD. 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, at least from my world, I mean, you know, as you and I spoke a little bit, but before this, we think about, when I talk about automating infrastructure, we think about there's a lot of sensors and we make sense of it. I 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 perthieves it reasons and it acts. And so in the old days, you know, when we before sort of gen AI with convolutional neural networks and sort of the old school deep learning approaches, we could really think we could really tackle the what just happened or what's happening now. Well, what's most important is what's about to happen and how can we improve upon it and how can we improve upon the outcome. And I think that's where Gen. AI 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 interact in a natural way. And so, I mean, there's there's so much there's so much there.

in terms of developing the solutions or developing any of these applications, Dan AI is going to have a major impact on the development journey. So just, I mean, if you're coding, if you're developing these solutions, Gen AI, you know, 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 in a warehouse in a distribution center.

You know, we're talking about this 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 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. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's Creative Agent, Firefly AI Assistant lets you start with your vision, just describe. what you want and shape the outcome as it takes form with the assistant. The assistant orchestrates multi-step workflows, drawing on 60 plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premiere, Lightroom Express,

and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adobie.com.

Yeah, and maybe Adam, let's dive in a little bit deeper on that exact example because I love it. Are we getting to the point, 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 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, 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. 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 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 tools will get to the point where all of the sensing is going to be hopefully automated in 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 obviously, well, not obviously. I'll explain in the not obvious way. So Nvidia is an accelerated computing company. And so we think about or 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. 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 that, spread the love, spread that capability and deploy it wherever, you know, is most appropriate, whether it's cloud or edge and distributed, like I said. And in terms of the AI, you know, 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, you know, GPUs, the parallel nature of GPUs was beautifully built for this kind of application. It's not just GPUs in 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 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 chat GPT. That's, you know, that's the inference, 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 literally brains. These are really complex things. And all of this requires supercomputing. And the super computing, you know, every time you mentioned, you know, being overloaded with queries, these, every query is a huge workload. And so that's also, you know, where we're headed. And also with Jenny, I, 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 creates a model based on that. But that's great.

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 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? retrieval augmented work. And all of that work, all the tuning is also a workload that Invidia 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 does the go to market? And so that's a lot of the work that I do and that we do is and think about how do we

actually go and solve really important problems that have never been solved before and work 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 do this kind of work, you really have to, you know, 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 that's what we do. Oh, don't forget me. We got to talk to 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 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 do want to ask you a follow-up here because it's fascinating, right? So when you talk about 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 usually a lot that goes on behind the scenes. So what is that process like, you know, whether it's for you personally, you know,

your team's 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. are chips or sensors to solve these huge problems, energy, climate, et cetera. What does that process like and how does it play out? Yeah, I think personally for me, 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.

in our case, ROS sensor data into insights and into hopefully valuable bits of information. And I'll get to the point. But I think we spent a lot of time honing things like detection and classification engines and,

you know, and the baseline of AI. But what we're, where we're headed with Gen AI, it finally, it truly closes, it closes the loop. I talked a little bit about automation and perceiving and reasoning and acting. And the perception and the, you know, 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 know, you had to build, you know, there's a lot of, I mentioned rigid tools. There's rigid tools that did something very specific. And if you could hack, not hack into it, but if you could, you know, crack open the hood and, you know, literally pull out the insights and integrate that 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 Gen.

of AI, in terms of 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 really going to, you know, in terms of the exponential adoption 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 want to get back to that. But hey, this is a live show. We have questions coming in. So I want to throw one out there already midway through the show here. So Jay asking, how is InVidia leveraging its expertise in AI in automation to address

challenges in the health care sector? That's, you know, 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, yeah, as I mentioned, you know, our team really 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 in 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, many aspects of the healthcare market. And so they're heavily using AI. But yeah, I mean, from our point of view, putting, you know, helping address patient safety, you know, those are just, you know, some of the things that we can do. And so turning hospitals into smart hospitals, that's one area that, you know, that we've

been involved in. But yeah, it's a huge area. And I mean, it spans physical infrastructure. It spans medical imaging. It spends, you know, automated, you know, surgery. And it's, it's incredible. It's incredible space. Yeah.

It's almost like where are you not playing, right? Where are you not helping? 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 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, chips and sensors are in probably every single product or powering.

all of the different websites that we use, how does InVidia 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 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 to just address the, the aspect that is, we work with thousands of partners to make this happen, obviously. So it's not, you know, this isn't exactly, you know, in video of 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 and the deploy. But from the point of view, how does this help on a day-to-day basis? You know, I mentioned a little bit about our team really thinking a lot about these really important transactions. and there's an efficiency aspect and there's a safety aspect. So one way we look at the world and we encourage a lot of our partners to look at them or frankly they've probably taught us to think about this 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 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 incomprehensible. It's not something that we should allow happen. And so there's all sorts of initiatives. One is called Vision Zero that aimed. 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 trying to get through an intersection, I mean, 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 cables at the end of those in some data centers is just dangling, and they're just dangling there. What we're trying to do is to take those cables, connect them into computing 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 this is. 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 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 the real world, right? You're 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 in 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 is, 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. I was thinking the other day, 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 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 that maybe people know about CUDA. Kuda 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, it made, it made. things exponentially faster. It made super computing faster and weather modeling and 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 a you know a 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 gaming. And now GPUs are everywhere. They're in the cloud. They're in your laptop. And that allowed us to enter, you know, and 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, So these are effectively general purpose computing devices.

These are nodes that are software defined. And so they are extremely flexible. So a GPU that used to do gaming can also do CNN work and deep learning and now can do gen. And it's the same device. And that's the power. The power of the company really has been in leverage, is leverage. It's a single word leverage. We leverage as much as we can of our relationships, of the GPUs of Kuda.

And that's how we've been able 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 that's what we do. We are much more a software company than we are hardware. People, I think, know us 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 jets and devices that are like, you know, 10 watts, seven watts. And we have, you know,

devices that are in laptops, my laptop right now. We have, you know, in workstations are gaining 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 wants deploy anywhere model. And that's another example of important leverage. So yeah, I'm trying to kind of give you, I'm trying to, you know, shed light on kind of what we've done, but that's, that's, that's part of the magic. No, you knock that one out.

It's like, hey, Adam, here is the, like the broadest question possible since Nvidia's everywhere. How does it all work, right? Another, hey, another great question here from Cecilia. So Cecilia asking, are you using the sensors in 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 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 that, you know, this earlier, there's two 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, yeah, 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, you know, that alone, you know, that's a huge area that a lot of our ecosystems focused on. So the answer is yes. And I think it's also really important to note. And I think it might not be intuitive. But I mentioned, you know, like dollar bills flowing through infrastructure, that is very, you know, that sounds very economic and very value-based. However, you know, 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, and, you know, better roadways, safety and safety and efficiency go hand. hand in hand. 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 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. Um, 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 in 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 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 so the answer is, the answer is yes. I'll give you an example of actually,

you know, say some of you might be traveling today or this week. You check in, you go through security and you're waiting at the gate and it's and your flight might be delayed. Behind the scenes, uh, the flight from Chicago, you know, that dropped Jordan off is now sitting, you know, at the gate. And there's this incredible orchestration, this, this, this, this, this wildly complex set of events on half to unfold. Getting people off the plane, refueling, cleaning, waiting for this, getting, you know, 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 this 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, um, of, um, of orchestration. What airlines think about is as soon as the gate drops Jordan off, the time is taking every second that that aircraft, that piece of asset, that piece of infrastructure sitting idle is, is money spilling out of their pockets. And so that what they try to do is they take this, 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,

they compress that time to get you from point A to point B faster, and it saves money, and it saves people being, 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, we talked about retail. The same thing's going to happen with retail. And the same things, maybe in, 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 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 airline a little less, 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, Adam, 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 using data to make things safer and more efficient. But as we wrap up today's show, maybe what is, you know, if there's a business leader out there, someone that is, you know, trying to push their

organization forward 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, it's a, it's a, it's a, it's a, it's a, it's a, it's a, it's a, it's, and it's, and it's, and it really does take a mind shifts to kind of, to, to think about the application of it. Because it's so new, I would say just, um, for everyone, you know, just really start to, 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 us. But just get started. Start somewhere. And I know a lot of people say that, you know, just get started. And it's, but it is true. Because this 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 every industry to work with to figure out and to learn and connect with and engage with. So if you are those champions, fantastic,

you should connect with us. But I would say just get started. And there's a lot of resources. And you mentioned chat GPT. Just start to use the tools and start to immerse yourself in it. And hopefully it'll become maybe clear one vector that you can go off and truly transform, your business having having having having having been in in business development you know and 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 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 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 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 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 back tomorrow and every day for more everyday AI. Thanks, y'all. Meet Firefly AI Assistant. Now live in Adobe Firefly, the Allman One Creative AI Studio. Just describe what you want to create in your own words and the assistant handles the rest,

orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome while the assistant accelerates execution. Stand control with the ability to step in and refine at any time. See it today at Firefly. 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 Your EverydayAI.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.