Ep 145: NVIDIA Leader Talks GenAI + Data: Unlocking new ways to interact with our world
Join the discussion: Ask Adam and Jordan questions about AI and data
Check out the upcoming Everyday AI Livestream lineup
Connect with Adam Scraba: LinkedIn Profile
In today’s fast-paced business landscape, staying ahead of the curve is crucial for success. As technology continues to evolve at an unprecedented pace, harnessing the power of artificial intelligence (AI) and data is essential for organizations to remain competitive and innovative. In a recent episode of the “Everyday AI” podcast, the hosts explore how NVIDIA, a leader in GPU technology, is unlocking new ways to interact with our world through their revolutionary Gen AI and data solutions.
Efficiency and Safety at the Forefront:
With their state-of-the-art GPU chips, NVIDIA has been at the forefront of technological advancements, setting new industry standards for efficiency and performance. Harnessing the power of generative AI, NVIDIA’s GPU chips have propelled the evolution of AI by enabling faster and more accurate data processing. Their technology not only enhances business operations but also contributes to public safety efforts by providing crucial insights into potentially dangerous situations.
From Gaming to Everyday Applications:
NVIDIA initially made waves in the gaming industry with their advanced GPUs, revolutionizing the gaming experience. However, their expertise extends far beyond gaming. Today, their GPUs are utilized in various devices and industries, playing a vital role in the development of autonomous robots, smart factories, and even healthcare facilities. This widespread adoption underscores the versatility and impact that NVIDIA’s technology offers across multiple sectors.
Unlocking the Power of Data:
Central to NVIDIA’s success is their software stack, which enables the seamless integration of their GPU chips into various applications. By leveraging the power of data, NVIDIA’s Gen AI solutions democratize access to valuable insights, making them accessible to everyone. This stands in stark contrast to early-stage AI work that required specialized skills, making it challenging for many businesses to leverage.
Transforming Everyday Interactions:
NVIDIA’s commitment to streamlining operations and automating workflows is reshaping the way we interact with technology and navigate our physical environment. Through the optimization of physical processes, such as reducing traffic congestion or improving retail experiences, NVIDIA’s AI-powered solutions enhance efficiency, safety, and productivity. Furthermore, their focus on transforming rigid application tools into more natural and automated interactions paves the way for a seamless user experience across industries.
The Future Ahead:
NVIDIA’s vision goes beyond simply improving existing processes – they are constantly pushing the boundaries of what AI and data can achieve. Their focus on customized AI models for enterprises and solving unique business challenges highlights their commitment to continuous innovation. As more organizations embrace these technologies, the potential for positive change in the way we interact with the world becomes increasingly exciting.
In a world driven by data and AI, NVIDIA’s Gen AI and data solutions stand as a beacon of innovation. By unleashing the power of GPU technology, NVIDIA is revolutionizing how we interact with our physical surroundings. With an unwavering commitment to efficiency, safety, and transformative experiences, NVIDIA empowers businesses to unlock new possibilities, reimagine processes, and drive growth. As we navigate the fourth industrial revolution, business leaders must seize this opportunity to leverage the potential of AI and data, setting the stage for a future full of limitless possibilities.
Topics Covered in This Episode
1. NVIDIA’s Role in AI and Automation
2. Gen AI and its Implications
4. How NVIDIA Uses AI and Data To Improve Daily Lives
5. NVIDIA’s Role in Safety
Jordan Wilson [00:00:24]:
Sometimes we talk with solopreneurs or small business owners or start up 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 and welcome if this is your 1st time, my name is Jordan Wilson, and I’m the host of Everyday AI. And this is your daily livestream podcast and 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.
Daily AI news
Jordan Wilson [00:01:26]:
If you’re joining us live, awesome. Get your questions in. What do you wanna 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 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 forecast for extreme events and can be evaluated in minutes on a small computer.
Jordan Wilson [00:02:16]:
That part is key because this model, because of the small form factor, has 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 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 1st 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 and Major presidential elections where both candidates are openly using AI generated imagery and videos in their campaigns. So the 2 campaigns are, the 2 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 1 campaign did release a deep fake video. This is especially timely here in the US as, hey, even though it’s a year away from the 2024, you know, election, we’re gonna be seeing these and AI generated images and ads nonstop.
Jordan Wilson [00:03:30]:
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 gbt 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 Check GPT plus subscribers. So OpenAI CEO Sam Altman just announced on Twitter that the company would not be taking on any new and Paid chat GPT plus subscribers as they cannot keep up with the surge in sign ups that happened after dev day last week, and it’s The platform is unstable right now.
About Adam Scraba and NVIDIA
Jordan Wilson [00:04:11]:
There’s too many people. It’s crashing. So, yeah, if you want to, you know, get on to 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 guests.
Jordan Wilson [00:04:25]:
I’m I’m super excited. And, hey, if you’re joining us live, Let me know where are you joining us from, and what do you wanna know about generative AI and data. I’m very excited. Let’s bring on our guest For today. There we go. Alright. We have joining us Adam Scraba, the Director of Marketing at NVIDIA. Adam, thank you for joining us.
Adam Scraba [00:04:44]:
Thanks for having me, Jordan. Thanks so much.
Jordan Wilson [00:04:46]:
Hey. And always, I I I have to give the VIP award to anyone that joins us from the West Coast. Wake up 5:30 local time to talk generative AI, thanks thanks for that, Adam.
Adam Scraba [00:04:58]:
Jordan Wilson [00:05:00]:
Alright. 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 a little bit kind of what your role entails.
How AI and data will change our world
Adam Scraba [00:05:15]:
Yeah. Sure. So I lead a group of people within NVIDIA that, we focus a lot on we focus on applying AI to, in infrastructure automation, problems, whether it’s in manufacturing, retail, smart cities. We’ve been at this for about 9 years with this team focusing on leveraging AI and and, and instrumenting and automating really important the world’s most important physical Transactions and processes. And, and so my my role is is in helping tell the story, evangelize the the platform, connect The dots build the ecosystem and, and hopefully, you know, solve some really, really important problems, within our, you know, within a within a few years, within our lifetime.
Jordan Wilson [00:05:59]:
You know, what are some things, Adam? You you know, I’m just I’m just gonna skip skip to the end here. You 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 BingChat or a MidJourney or a chat GPT, but how is generative AI And in data, specifically, how is it gonna change how we interact in our day to day lives in the physical world?
Adam Scraba [00:06:31]:
Yeah. We could spend all day talking about this for sure. I mean so, you know, I’ll 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 as you and I spoke a little bit, but before this, we think about, when when I when I talk about Automating infrastructure. We think about, there’s a lot of sensors, and we we make sense of it. I know we happen to use A particular type of sensor we use, LiDAR and and cameras, and and and a lot of these sensors are are really powerful. But in terms of automating things, we think a lot about Kinda like turning infrastructure into a robot, and so the robot perceives, it reasons, and it acts. And, and so in the old days, You know, when we before sort of Gen AI with with convolutional neural neural networks and 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.
Adam Scraba [00:07:27]:
But what’s most important is what’s about to happen and how can we approve improve upon it, and how can how can we improve upon the the outcome. And I think that’s where Gen AI is really gonna be super, super powerful is that We can finally get beyond the you know, we with the perceiving and the reasoning, and now we can actually act upon a lot of this stuff and 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 the the solutions or developing any of these applications. Gen AI is gonna 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, Jenny, I you know, we we that we can talk a little bit about how the development journey is gonna change, but it but just how we interact with it, which is kind of your question. For the 1st time, we can interact with natural language and ask questions like, you know, literally, like, you know, 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 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.
Adam Scraba [00:08:36]:
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. Mhmm. And now We can open this up and be much more natural. Like, we’re really talking to to agents, and and the agency of of that the agents Will be, how we interact with a lot of these applications, and that’s completely transformational.
Jordan Wilson [00:08:59]:
Yeah. And 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 2 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 and Automated. That’s that’s part 1 of the question. And part 2, can you explain a little bit? Because, you know, I’m a dork.
Jordan Wilson [00:09:25]:
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
Adam Scraba [00:09:43]:
That is entirely the goal. Is the entire the entire goal is to streamline our operations and and 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 and to get to That point with what 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 a supply chain manager. I mean, the the tools will get to the point where All of the sensing is gonna be hopefully automated in a lot of the tools, and and the superhuman vision will be codified and 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.
NVIDIA’s role in AI
But to get back to to the second question about, you know, how does NVIDIA play in this? So, so, obviously well, not obviously. I I’ll explain in in the not obvious way.
Adam Scraba [00:10:38]:
So NVIDIA is a is an accelerated computing company, and So we think about or we we we focus our efforts on building, end to end stack, software, and hardware that enables, some of the world’s most complex, and and a lot of them AI, workloads to be accelerated, whether it’s at the edge, in In the cloud, a combination, distribute distributed. And so, when I say end to end, that means we help People develop the tools, and so we we we help. We give people the the the the the tools to be able to create incredible applications And make them run incredibly fast Mhmm. And allow them to spread that spread the love, spread the spread that that that that capability, And and and deploy wherever, you know, that is most appropriate, whether it’s, you know, cloud or or edge and and distribute it 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. It’s not just great for training neural networks, but it’s also incredibly good at at running these neural networks.
Adam Scraba [00:11:57]:
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’ve talked a little bit about chat gbt. That’s, you know, that’s that’s in 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, and all of this requires Supercomputing. And the supercomputing you know, every time you mentioned, you know, being overloaded with with with with queries, these, every query is a huge workload. So so that’s also, you know, where we’re headed.
Adam Scraba [00:12:42]:
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, an enterprise, I don’t know, pick any enterprise that you think of that doesn’t maybe publish There are 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 of things like RAG. Right? You know, retrieval augmented, you know, work. And that and all of that work, all the tuning is also, A workload that that NVIDIA focuses on.
Adam Scraba [00:13:28]:
You and I spoke a little bit about this earlier, and I think it’s important to note that that the work that that I do and that that we do generally within NVIDIA is is we’re, our mission isn’t to make things A little bit better or a little bit cheaper. We’re we’re really trying to to solve problems that have never been solved before and and almost create businesses and and verticals that have never existed before. So when we when you go and 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, and so that’s a lot of the work that I do is is and that we do is is, and think about how do we actually go and and solve really important problems that have never been solved before and work with, You know, with with everyone. You 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 it’s not enough to just throw out some GPUs and and say, you know, good luck to 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.
Jordan Wilson [00:14:36]:
Oh, don’t don’t forget me. We gotta talk GPUs. But, hey, as a reminder, everyone joining us Hi. We have Adam Scraba, the director of marketing at NVIDIA. So what do you wanna 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 wanna ask you a a follow-up here because it’s it’s and Fascinating. Right? So when you talk about, you know, using generative AI to create solutions for real important problems It may be problems that most of us don’t even know exist yet. Even I I wanna know this even for you personally.
Jordan Wilson [00:15:13]:
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 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 gonna, you know, use Gen AI in our, You know, chips or sensors to solve these huge problems, energy, climate, etcetera. What is that process like, and and how does it play out?
Adam Scraba [00:15:50]:
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 a decade building up these incredible capabilities of, of converting, you know, raw raw in in our case, raw sensor data into insights and and into hopefully valuable bits of information. And, and I and I’ll I’ll I’ll I’ll get to the point, but but I think, We we spend a lot of time honing things like detection and classification engines and and, you know, and the the baseline of of AI. But what what we’re where we’re headed with with Gen AI, it finally it truly closes it it that closes the loop. You know, I we we I’ve talked a little bit about automation and perceiving and reasoning and acting. And, the the perception and and the the the you know, taking taking raw data and turning it into something that’s meaningful, that’s great.
Adam Scraba [00:16:51]:
But but with GenAI, we can finally close the loop and make The data accessible, make the, make the insights useful to everyone. And and 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. It would 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 crack open the hood and and 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 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.
Adam Scraba [00:18:04]:
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 gonna be a a a huge deal, and we’re already seeing it.
Jordan Wilson [00:18:14]:
Yeah. And I do I do wanna get back to that, but, hey, this is a live show. We have questions Coming in. So I wanna I wanna throw 1 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 health care sector. That’s you know, I don’t 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 health care sector?
Adam Scraba [00:18:44]:
Sure. Sure. Sure. Sure. So so, Yeah. As I as I mentioned, you know, our team is really thinks of thinks about infrastructure, most important processes, and, you know, health. There there perhaps a more important, you know, transaction process than than keeping people healthy in in in health care. So, you know, in terms of, you I we can talk a little bit about sort of smart hospitals.
Adam Scraba [00:19:04]:
I think there’s, you know, these initiatives to make, health care facilities more more more safe, more more smart Using sensors, that’s certainly something that that we’re we’re we’re working with. You know, we have an incredible health care team within NVIDIA that that that focuses on Many, many aspects of the of the health care market, and, and so they’re they’re heavily using, AI. But, yeah, I mean, from from from our point of view, putting, you know, helping, helping address patient safety. You know, those are just 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 that we’ve been involved in. But, yeah, it’s it’s a huge area, and, I mean, it it spends, physical infrastructure. It spans, medical imaging. It spans, you know, automated, you know, surgery, and it’s an incredible space.
Ways NVIDIA uses data to improve daily lives
Jordan Wilson [00:19:57]:
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 a a microphone here in front of me. I have a a a camera, a Phone, computers. I have websites open.
Jordan Wilson [00:20:17]:
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 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 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?
Adam Scraba [00:20:59]:
Yeah. Sure. I 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 of companies to make this stuff happen and to and to, you know, help build their applications and to deploy. But but from the 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 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.
Adam Scraba [00:21:53]:
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 in traffic congestion. That is a huge waste, and that is dollars literally flowing through trained infrastructure, which is our roadways. There’s also a safety component to it. Traffic fatalities happen to also be in 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 that that that we should that we should allow happen, and so there’s all sorts of initiatives. One is called Vision 0 that that aims to reduce traffic fatalities to 0, and that’s entirely gonna be using AI and cameras to help solve To understand how to design and and manage our roadways better, pedestrians and cyclists.
Adam Scraba [00:22:39]:
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 gonna see probably between 4 to 8 cameras, Those cameras are actually dangling. The the the cables at the end of those in some data centers are just dangling, and they’re just dangling there. What we’re trying 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.
Adam Scraba [00:23:19]:
Well, your wallet is in your pocket. Again, dollar bills flowing through constrained infrastructure. You know, you you’re literally walking through, and maybe a customer service is terrible, and you don’t know where this 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 and it’s and it’s And 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 a way to elucidate what we’re trying to what we’re really trying to do with this with this stuff.
NVIDIA’s GPUs and their impact
Jordan Wilson [00:24:08]:
No. I love that because that’s that’s the real world. Right? You 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 when traffic is heavy. But I wanna take it from from the real world, Adam, if we can, to the computer world, right, to 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.
Jordan Wilson [00:24:37]:
Right? They’re graphical, graphics processing units. And 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 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? Mhmm. 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 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 So can you talk a little bit about that and how that really helps the development cycle as well? You you know, these GPU chips of all the products and services we use in the generative AI space.
Adam Scraba [00:25:25]:
Sure. Sure. Sure. So so first off, I mean, you mentioned GPUs and 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 is getting You know, and that 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 it made things exponentially faster. It made supercomputing 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.
Adam Scraba [00:26:07]:
And they 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 a, you know, an a gaming is an gaming is a is a is a simulation Engine. Right? The the truly we simulate how, you know, 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, and to and to to sort of accelerate AI because now GPUs have are are are everywhere.
Adam Scraba [00:26:42]:
It’s not an exotic Device. It’s kind of everywhere. And, and then we work, you know, end to end on a stack to enable, and to to make these workloads run really, really fast. And and, also, well, it’s these are these are effectively general purpose computing devices. These are nodes that are software defined, and 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.
Adam Scraba [00:27:18]:
It’s a single word leverage. We leverage as much as we can of our relationships of 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 in this world. So, I mean, to your your question’s extremely broad, and I’m trying to 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 jets and, you know, jets and devices that are, like, you know, 10 watts, 7 watts, We have, you know, devices that are in laptops, my laptop right now. We have, you know, in workstations or gaming PCs all the way into cloud nodes into supercomputing, you know, data centers, and the same code runs on any of that stuff.
Adam Scraba [00:28:16]:
So it’s kind of like this code once Deploy anywhere model, and that’s another example of of important leverage. So, yeah, I’m I’m trying to kinda 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.
How NVIDIA creates safety in communities
Jordan Wilson [00:28:30]:
No. You knocked that one out. It’s like, hey, Adam. Here is the, like, the broadest question possible since NVIDIA’s everywhere. And 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, Cecile.
Jordan Wilson [00:28:49]:
Yeah. Adam, how does that work, and and is is this something that NVIDIA is doing?
Adam Scraba [00:28:53]:
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 with public safety, and and it was partly because, we have a lot of cameras that are deployed, and I mentioned, you know, this earlier. There’s 2,000,000,000 cameras deployed worldwide, and it’s growing at an incredible rate. And And and the and and and that was one of the first places that we sort of we we really spend a lot of time focusing on. And, yeah, the answer is yes.
Adam Scraba [00:29:25]:
We can help, in in a lot of ways, in in 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, traffic safety, and and and pedestrians, and And, you know, that that that alone, you know, that that’s a huge area that we know a lot of our ecosystem is focused on. So the answer is yes. And I I think it’s also really important to 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 You know, when when we talk about efficiency and safety, efficiency and safety go hand in hand.
Adam Scraba [00:30:09]:
And so when, you know, almost in every scenario where you talk about Efficiency, whether it’s on a manufacturing floor or safety, whether it’s, you know, pedestrians in in smart cities and and and and, you know, Better roadways, safety, and efficiency go hand in hand. So we really think a lot about that, and 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%.
Smart factories and autonomous robots
Jordan Wilson [00:30:35]:
Yeah. It’s it’s it’s a great, fringe benefit. Right? Like, To make it like, if if efficiency is the goal, everything’s safe, or if if safety is the goal, everything’s more efficient. Yeah.
Adam Scraba [00:30:44]:
And they’re not they’re not separate. They’re really I think they’re very tied together.
Jordan Wilson [00:30:47]:
Yeah. For sure. You know, I wanna I wanna go full circle a little bit here and 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 we we talked a little bit about, you you know, the work that NVIDIA is doing, and I know that, you you know, your autonomous robots and smart factories are A huge, piece of of what you all are working on moving forward. How does I mean, are we going to be seeing that even more In nonwarehouse. Like, I think everyone thinks of, you know, oh, smart factories and autonomous robots. Those are gonna be in factories and and shipping centers. Are we gonna 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.
Jordan Wilson [00:31:42]:
Is that something that we might be heading toward?
Adam Scraba [00:31:45]:
Ab absolutely. Absolutely. I mentioned it earlier. I think it 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 them 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 of actually, you know, say say, some of you might be traveling today or this week.
Adam Scraba [00:32:17]:
The, you you check-in, you go through security, and and you’re you’re waiting at the gate and 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, you know, at the gate, And there’s this incredible orchestration, this this this this wildly complex set of events on have to unfold, Getting people off the plane, refueling, cleaning, waiting for this, getting, you know, the the luggage, and there’s 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 the the chart is incredibly long. I’ve seen it, and it’s incredibly complex, and all of this is this really complex set of of, of 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 their pockets. And so that what they try to do is they take this this chart of orchestration from time 0 to time, whatever, 30 minutes, and they try to compress it. And today, that kind of thing is very manual.
Adam Scraba [00:33:23]:
With cameras, with AI, we’re able to completely with high granularity, with fine granularity, instrument in process and figure out and and and when we we do this, we have partners that do this that compress that time to get you from point a to point b b faster, and it saves money, And it saves people being, you know, you know, someone being super upset and 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. It’s it’s gonna be almost a robot, and it’s gonna be automated. But we give you know, you and I gave we talked about retail. The same thing’s gonna happen with retail and things, things. Maybe in if you go to the hospital, a lot of these things will 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 gonna be in your daily life, all over the place.
Adam Scraba [00:34:21]:
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.
Adam’s final takeaway
Jordan Wilson [00:34:29]:
Yes. Well, hey. Thank you from all of us for making that error That you know, sitting in the air, 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 2. So so, Adam, we’ve 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 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 1 piece, which I know is hard, but maybe what’s that 1 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.
Adam Scraba [00:35:24]:
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 to kinda to to think about the application of it because it’s so new. I would say just, for everyone, you know, just really start to to experiment and 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 to learn from or connect with with us.
Adam Scraba [00:35:55]:
But just get started. Start somewhere. And and I know a lot of people say that, you know, just get started. And and it’s and it 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, 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.
Adam Scraba [00:36:26]:
You know, you should connect with us. But I would say just get started and and and and, and there’s a lot of resources. And you mentioned chat gbt. Just start to to use the tools and start to, you know, immerse in it, and and it’ll, hopefully, it’ll become maybe clear one vector that you can go off and and truly transform your your business. Having having having been in in business development, you know, in in a lot of these roles before, sometimes you really need that 1 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 and they they spot an amazing opportunity. Those are the people we’re really looking for. And if you are one of those champions, I encourage you, you know, you know, you have you have the opportunity to really change your company, your business, your team, and, I’m excited, and we’d we’d love to work with these people.
Adam Scraba [00:37:22]:
This is this is these are these are how this is how it all starts.
Jordan Wilson [00:37:26]:
Absolutely. Gosh, man. I mean, Adam, in a in a world full of of data and sensors and the never ending development cycle of 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.
Adam Scraba [00:37:43]:
Excellent. Thanks so much for having me.
Jordan Wilson [00:37:44]:
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 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.