 Howdy y'all and welcome back to Supercomputing 2022. We're theCUBE and we are live from Dallas, Texas. I'm joined by my co-host, David Nicholson. David, hello. Hello. We are going to be talking about data and enterprise AI at scale during this segment and we have the pleasure of being joined by both Dell and NVIDIA. Anthony and Bob, welcome to the show. How you both doing? Doing great. Great show so far. Love that enthusiasm, especially in the afternoon on day two, I think we all, what's in that cup? Is there something exciting in there that maybe we should all be sharing with you? Just say it's distilled. Yeah. Water. Yeah, yeah. I love that. So I want to make sure that, because we haven't talked about this at all during the show yet on theCUBE, I want to make sure that everyone's on the same page when we're talking about data, unstructured versus structured data. It's in your title, Anthony. Tell me, what's the difference? Well, look, the world has been based in analytics around rows and columns, spreadsheets, data warehouses, and we've made predictions around the forecast of sales, maintenance issues. But when we take computers and we give them eyes, ears and fingers, cameras, microphones, and temperature and vibration sensors, we now translate that into more human experience. But that kind of data, the sensor data, the video camera is unstructured or semi-structured. That's what that means. We live in a world of unstructured data. Unstructured is something we add to later after the fact. But the world that we see and the world that we experience is unstructured data. And one of the promises of AI is to be able to take advantage of everything that's going on around us and augment that, improve that, solve problems based on that. And so if we're going to do that job effectively, we can't just depend on structured data to get the problem done. We have to be able to incorporate everything that we can see here, taste, smell, touch, and use that as part of the problem solving. We want the chaos, bring it. Chaos has been a little bit of a theme of our show. It has been, yeah. And chaos is in the eye of the beholder. You think about the reason for structuring data. To a degree, we had limited processing horsepower back when everything was being structured as a way to allow us to be able to reason over it and gain insights. So it made sense to put things into rows and tables. How does, I'm curious, diving right into where NVIDIA fits into this puzzle, how does NVIDIA accelerate or enhance our ability to glean insight from or reason over unstructured data in particular? Yeah, great question. It's really all about, I would say it's all about AI. And NVIDIA is a leader in the AI space. We've been investing and focusing on AI since at least 2012, if not before. Accelerated computing that we do at NVIDIA is an important part of it. Really, we believe that AI is going to revolutionize nearly every aspect of computing, really nearly every aspect of problem solving, even nearly every aspect of programming. And one of the reasons is for what we're talking about now, which is being able to incorporate unstructured data into problem solving is really critical to being able to solve the next generation of problems. AI unlocks tools and methodologies that we can realistically do that with. It's not realistic to write procedural code that's going to look at a picture and solve all the problems that we need to solve if we're talking about a complex problem like autonomous driving. But with AI and its ability to naturally absorb unstructured data and make intelligent reason decisions based on it, it's really a breakthrough. And that's what NVIDIA has been focusing on for at least a decade or more. And how does NVIDIA fit into Dell's strategy? Well, I mean, look, we've been partners for many, many years, delivering beautiful experiences on workstations and laptops. But as we see the transition away from taking something that was designed to make something pretty on screen to being useful in solving problems in life sciences, manufacturing, and other places, we work together to provide integrated solutions. So take, for example, the DGX-A100 platform. Brilliant design, revolutionary bus technologies, but the rocket ship can't go to Mars without the fuel. And so you need a tank that can scale in performance at the same rate as you throw GPUs at it. And so that's where the relationship really comes alive. We enable people to curate the data, organize it, and then feed those algorithms that get the answers that Bob's been talking about. So as a gamer, I must say, your little shot at making things pretty on a screen. Come on, that was a low blow. That was a low blow. Sassy. Now what's in your cup? That's what I want to know. I apparently have the most boring cup of anyone on the field today. I don't know what happened. We're going to have to talk to the production team. I'm looking at all of you. We're going to have to make that better. One of the themes that's been on the show, and I love that you all embraced the chaos. We're seeing a lot of trend in the experimentation phase, or stage rather, and we're in an academic zone of it with AI. Companies are excited to adopt, but most companies haven't really rolled out their strategy. What is necessary for us to move from this kind of science experiment, science fiction in our heads to practical application at scale? Well let me take this, Bob. So I've noticed there's a pattern of three levels of maturity. The first level is just what you described. It's about having an experience, proof of value, getting stakeholders on board, and then just picking out what technology, what algorithm do I need, what's my data source. That's all fun, but it is chaos. Over time, people start actually making decisions based on it. This moves us into production, and what's important there is normality, predictability, commonality across, but hidden and embedded in that is a center of excellence. The community of data scientists and business intelligence professionals sharing a common platform. In the last stage, we get hungry to replicate those results to other use cases, throwing even more information at it to get better accuracy and precision, but to do this in a budget you can afford. And so how do you figure out all the knobs and dials to turn in order to take billions of parameters and process that? That's where- Casual. Casual decision matrix there with billions of parameters. Yeah. But you're right. That's exactly what we're on this continuum, and this is where I think the partnership does really well, is to marry high-performance, enterprise-grade scalability that provides the consistency, the audit trail, all of the things you need to make sure you don't get in trouble, plus all of the horsepower to get to the results. Bob, what would you add there? I think the thing that we've been talking about here is complexity, and there's complexity in the AI problem-solving space. There's complexity everywhere you look, and we talked about the idea that NVIDIA can help with some of that complexity from the architecture and the software development side of it, and Dell helps with that in a whole range of ways, not the least of which is the infrastructure and the server design, and everything that goes in to unlocking the performance of the technology that we have available to us today. So, even the center of excellence is an example of how do I take this incredibly complex problem and simplify it down so that the real world can absorb and use this, and that's really what Dell and NVIDIA are partnering together to do, and that's really what the center of excellence is. It's an idea to help us say, let's take this extremely complex problem and extract some good value out of it. So what is NVIDIA's superpower in this realm? I mean, look, we're in the era of, yeah, yeah, yeah, we're in a season of microprocessor manufacturers, one upping one another with their latest announcements. There's been an ebb and a flow in our industry between doing everything via the CPU versus offloading processes. NVIDIA comes up and says, hey, hold on a second. GPU, which, again, was focused on graphics processing originally doing something very, very specific. How does that translate today? What's the NVIDIA, again, what's the superpower? Because people will say, well, hey, I've got a CPU, why do I need you? I think our superpower is accelerated computing, and that's really a hardware and software thing. I think your question is slanted towards the hardware side, which is very typical. And we do make great processors, but the processor, the graphics processor that you talked about from 10 or 20 years ago was designed to solve a very complex task, and it was exquisitely designed to solve that task with the resources that we had available at that time. Now, fast forward 10 or 15 years, we're talking about a new class of problems called AI, and it requires both exquisite processor design, as well as very complex and exquisite software design sitting on top of it as well, and the systems and infrastructure knowledge, high performance storage and everything that we're talking about in the solution today. So NVIDIA's superpower is really about that accelerated computing stack. At the bottom you've got hardware, above that you've got systems, above that you have middleware and libraries, and above that you have what we call application SDKs that enable the simplification of this really complex problem to this domain or that domain or that domain, while still allowing you to take advantage of that processing horsepower that we put in that exquisitely designed thing called the GPU. Decreasing complexity and increasing speed to very key themes of the show, shocking no one. We all want to do more faster. Speaking of that, and I'm curious because you both serve a lot of different unique customers, verticals and use cases, is there a specific project that you're allowed to talk about? Or, I mean, you want to give us the scoop, that's totally cool too. We're here for the scoop on the cube, but is there a specific project or use case that has you personally excited? Look, I've always been a big fan of natural language processing. I don't know why, but to derive intent based on the word choices is very interesting to me. I think what complements that is natural language generation. So now we're having AI programs actually discover and describe what's inside of a package. It wouldn't surprise me that over time, we moved from doing the typical summary on the economics of the day or what happened in football and we start moving that towards more of the creative advertising and marketing arts where you are no longer needed because the AI is going to spit out the result. I don't think we're going to get there, but I really love this idea of human language and computational linguistics. What a marriage, I agree. I think it's fascinating. What about you, Bob? What's got you pumped? The thing that really excites me is the problem solving, sort of the tip of the spear in problem solving, the stuff that you've never seen before, the stuff that in a geeky way kind of takes your breath away. And I'm going to jump or pivot off of what Anthony said. Large language models are really one of those areas that are just, I think they're amazing and they're just kind of surprising everyone with what they can do. Here on the show floor, I was looking at a demonstration from a large language model startup basically, and they were showing that you could ask a question about some obscure news piece that was reported only in a German newspaper. It was about a little shipwreck that happened in a hardware. And I could type in a query to this system and it would immediately know where to find that information as if it read the article, summarized it for you. And it even could answer questions that you could only answer by looking at pictures in that article. Just amazing stuff that's going on. Just phenomenal stuff. That's a huge accessibility. That's right. And I geek out when I see stuff like that and that's where I feel like all this work that Dell and NVIDIA and many others are putting into this space is really starting to show potential in ways that we wouldn't have dreamed of really five years ago. Just really amazing stuff. And we see this in media and entertainment. So in broadcasting, you have a sudden event. Someone leaves this planet where they discover something new or they get a divorce and they're a major quarterback. You want to go back somewhere in all of your archives to find that footage. That's a very laborious project. But if you can use AI technology to categorize that and provide the metadata tags so it's searchable, then we're off to better productions, more interesting content and a much richer viewer experience. And a much more dynamic picture of what's really going on. Factoring all of that in. I love that. I mean, David and I are both nerds and I know we've had take our breath away moments so I appreciate that you just brought that up. Don't worry, you're in good company in terms of the geek squad over here. I think actually maybe this entire show for us. Yeah, exactly. I mean, we were talking about how steampunk some of the liquid cooling stuff is and this is the only place on earth really or the only show where you would come and see it at this level in scale and it's just, yeah, it's very exciting. How important for the future of innovation in HPC are partnerships like the one that Nivedia and Dell have? You want to start? Sure, I would just, I mean, I'm going to be bold and brash and arrogant and say they're essential. Yeah. You do not want to try and roll this on your own. This is, even if we just zoomed in to one little piece of the technology, the software stack to do modern accelerated deep learning is incredibly complicated. There can be easily 20 or 30 components that all have to be the right version with the right buttons pushed, built the right way, assembled the right way. And we've got lots of technologies to help with that but you do not want to be trying to pull that off on your own. That's just one little piece of the complexity that we talked about and we really need, as technology providers in this space, we really need to do as much as we do to try to unlock the potential, we have to do a lot to make it usable and capable as well. I had a question for Anthony. All right, so in your role, and I'm sort of projecting here, but I think your superpower personally is likely in the realm of being able to connect those dots between technology and the value that that technology holds in a variety of contexts, whether it's business or whatever, okay? Now, it's critical to have people like you to connect those dots today. In the era of pervasive AI, how important will it be to have AI have to explain its answer? In other words, should I trust the information the AI is giving me if I am a decision maker? Should I just trust it on face value? Or am I going to want a demand of the AI, kind of what you deliver today, which is, no, no, no, no, no, no. You need to explain this to me. How did you arrive at that conclusion? How important will that be for people to move forward and trust the results? We can all say, oh, hey, just trust us. Hey, it's AI, it's great, it's got NVIDIA acceleration and it's Dell, you can trust us. But come on, so many variables in the background, it's an interesting one, and explainability is a big function of AI. People want to know how the black box works, because I don't know if you have an AI engine that's looking for potential maladies in an X-ray, but it misses it, do you sue the hospital, the doctor, or the software company? Right. And so that accountability element is huge. I think as we progress and we trust it to be part of our everyday decision making, it's as simply as a recommendation engine. It isn't actually doing all of the decisions, it's supporting us. We still have, after decades of advanced technology, algorithms that have been proven, we can't predict what the market price of any object is going to be tomorrow. And you know why? You know why? Human beings, we are so unpredictable. How we feel in the moment is radically different, and whereas we can extrapolate for a population to an individual choice, we can't do that. So humans and computers will not be separate. It's a joint partnership. But I want to get back to your point, and I think this is a very fundamental to the philosophy of both companies. Yeah. It's about a community. It's always about the people sharing ideas, getting the best, and anytime you have a center of excellence, an algorithm that works for sales forecasting may actually be really interesting for churn analysis to make sure the employees or students don't leave the institution. So it's that community of interest that I think is unparalleled at other conferences. This is the place where a lot of that happens. I totally agree with that. We've felt that on the show. I think that's a beautiful note to close on. Anthony, Bob, thank you so much for being here. I'm sure everyone feels more educated and perhaps more at peace with the chaos. David, thanks for sitting next to me asking the best questions of any host on theCUBE. And thank you all for being a part of our community. Speaking of community here on theCUBE, we're live from Dallas, Texas. It's super computing all week. My name is Savannah Peterson, and I'm grateful you're here.