 From Orlando, Florida, it's theCUBE. Covering SAP Sapphire Now 2018, brought to you by NetApp. Welcome to theCUBE. I'm Lisa Martin with Keith Townsend and we are in Orlando at SAP Sapphire Now 2018. We're in the NetApp booth and talking with lots of partners and we're excited to welcome back to theCUBE our distinguished alumni, Jim McHugh. From Nvidia, you are the VP and GM of deep learnings and other stuff, as you said a minute ago. That's a lot of responsibility. That other stuff, that can really pile up. Yeah, exactly. So here we are at Sapphire. You've been working with SAP in various forms for a long time. This event is enormous. Lots of momentum at Nvidia. What is Nvidia doing with SAP? We are really helping SAP figure out and drive the development of their SAP Leonardo machine learning services. So machine learning as we saw on the keynote today with Hasso is a key component of it. And really what it's doing is it's automating a lot of the standard processes that people did with the interaction. So whether it's closing your invoices at the end of the quarter and that can take weeks to go through it manually, you can actually do machine learning and deep learning can do that instantaneously. So you can get a continuous close. Things like service ticketing. So when a service ticket comes in, we all know, you pick up the phone, you call them and they collect your information and then they pass you on to someone else that wants to confirm the information. All that can be handled just in an email because now I know a lot about you when you send me an email. I know who you are. I know what company you're with. I know your problem because you stated it and I can route it using machine learning to the appropriate person. I can not only route to the appropriate person, I can look up in a knowledge database and say, hey, have we seen this answer or question before, feed that to the customer service representative and when they start interacting with the customer, they already have a lot of information about them and it's already well underway. So from a practical technology perspective, we hear a lot about AI and machine learning. NVIDIA obviously leading the way with GPUs and enabling development frameworks to take advantage of machine learning and that compute power. But the enterprise, we'll look at that and we'll like, you know, we see obvious value but I need a data scientist. I need a programmer. I need all this capability from a technical staff perspective to take advantage of it. How is NVIDIA SAP making that easier to consume? Yeah, so most enterprises, if you're just jumping in and trying to figure it out, you would need all these people. You need a data scientist and someone to go through the process. Because AIs, it's a new way of writing software and you're using data to train the software. So we don't have, we don't put programmers in a room anymore and let them code for nine months and now pop software, you know, eventually. We give them more and more data and the data scientist is training it. Well, the good news is, we're working with SAP and they have the data scientist. They know how SAP apps work. They know how the integration works. They know the workflows of their customers. So they're building the models and then making it available as a service, right? So when you go to the SAP cloud, you're saying, I want to actually take advantage of the SAP service for service ticketing. Or, you know, I want to figure out how I can do my invoice processing better. Or, I'm an HR representative and I don't want to spend 60% of my time reading resumes. I actually want to have an AI do it for me and then it's a service that you can consume. There, that we do make it possible. Like, if you have a developer in your enterprise and you say, you know what, I'm a big SAP user but I actually want to develop a custom app or are there some things that I might do, then SAP makes available the Leonardo Machine Learning Foundation and you can take advantage of that and develop a custom app. And if you have a really big problem and you want to take it off, NVIDIA's happy to work with you directly and figure out how to solve, you know, different problems. And most of our customers are in all three of those, right? They're consuming the services because they automate things today. They're figuring out what are the custom apps they need to build around SAP. And then they're, you know, they're figuring out some of the product building products or something else that's a much bigger machine learning, deep learning problem. So yesterday, during Bill McDermott's keynote, he talked about tech for good and there's been a lot of news recently of tech for not so good and data privacy, GDPR, you know, finance going into effect last week. NVIDIA really has been an integral part of this AI renaissance. You talked about, you know, you can help loads of different customers. There's so much potential with AI. As Bill McDermott said yesterday, AI to augment humanity. I can imagine, you know, life and death situations like in healthcare. Can you give us an example of what you guys are doing with SAP that, you know, maybe is transforming healthcare in a particular hospital? Yeah. So one of the great examples I was just talking about is what Massachusetts General is doing. Massachusetts General is one of the largest research hospitals in the United States and they're doing a lot of work in AI to really automate processes that, you know, when you would take your child in to figure out the bone density scan, which basically tells you the bone age of your child and they compare it to your biological age and that can tell you a lot of things. Is it just a, you know, the growth problem or is there something more serious to be concerned about? Well, they would do these MRIs and then you would have to wait for days while the technician and the doctor would flip through a textbook from the 1950s to terminate. Well, Massachusetts General automated all that where they actually trained a neural network on all these different scans and all the different components and now you find out minutes. So it greatly reduces the stress, right? And there's plenty of other projects going on and you can see it in determination if that's a cancer cell or, you know, so many different aspects of it. Your retina happens to be an incredible venue into whether you have hypertension, though, whether you have malaria, dengue fever, so things like, you know what, you probably shouldn't be around anywhere where you're going to give it by a mosquito and it's going to pass it to your family. All that can now be handled and you don't need expensive healthcare. You can actually take it to a clinician out in the field. So we love all that, but if you think about the world of SAP, which is the, you know, controls the data records of most companies, right? Their supply chain information, their resource information about what they have available, all that's being automated. So if we think from the production of food where we're having tractors now that they have the ability to go over a plant and say, you know what, that needs insecticide or that needs weeds, you know, to be removed because it's just bad for the whole component or that's a disease plant and I'm going to remove it or it just needs water so it can grow, right? That is increasing the production of food in an organic way. Then we improve the distribution center so that doesn't sit as long, right? So we can actually have drones flying through the warehouses and knowing what needs to be moved first and go from there. We're moving to autonomous driving vehicles and where deliveries can happen at night when there's not so much traffic and then we can get the food as fresh as possible and delivered. So if you think that whole distribution center just being and the pipeline is being automated, it's doing an incredible amount of good. And then jumping into the world of autonomous driving vehicles, it's a $10 trillion business that's being changed radically. So as we think about these super complex systems that we're trying to improve, we start to break them down into smaller components. When you end up with these scenarios, these edge scenarios, use cases where whether it's data frequency, data volume or data latency, we have to push the compute out to the edge. Can you talk about use cases where NVIDIA has pushed the technology far out to the edge to take in massive amounts of data that effectively can't be sent back to the core or to the data center for processing? What are some of these use cases and solutions? The world of IoT is changing as well. The compute power has to be where it's needed in any form. So whether it's cloud-based, data center-based or at the edge. And we have a great customer that is actually doing inspection, oil refineries, bridges, where they spot a crack or some sort of mark and then where they have to go look at it. Well, traditionally what you do is you send out a whole team and they build up scaffolding or they have people repel down to try to inspect it. Well, now what we're doing is flying drones and sending wall crawlers up. So they find something, they get data, and then instead of actually, like you said, putting it on a truck and taking it back to your data center trying to figure out how to have enough bandwidth to get there, they're taking one of our products, which is a DGX station. It's basically the equivalent of a half a row of servers, but it's in a single box, water-cooled. And they're putting it in band, sitting out in remote areas of Alaska and retraining the model there on site. So they get the latest model, they get more intelligence and they just collect it and they can resend the drones up and then discover more about it. So it really, really is saving, and that saves a lot of money. So you have a group of really smart technicians and people understand it and a guy can do the neural network capability instead of a whole team coming up and setting up scaffolding that would cost millions of dollars. That reminds me of that commercial that they showed yesterday during general session, SAP commercial with Clive Owen, the actor, talking about, you mentioned cracks and oil wells and things like that. It just reminded me of that and what they talked about in that video was really how invisible software like SAP is transforming industries, saving lives. I think I saw on their website an example of how they're leveraging AI and technology to reduce water scarcity in India or save the rhino conservation. What you've just described with NVIDIA seems to be quite in alignment with the direction that SAP is going. Oh, absolutely, yeah. I mean, we believe in SAP's view of the intelligent enterprise and people got to remember enterprises in the corporate office, whatever. Enterprises are many different things, right? Public safety, if you could think of it like that, that's a big thing we focus on. Really amazing thing that's going on, thinking about using drones for first responders. They actually can know what's going on in the scene and when the other people are showing up, they know what kind of area they're going into. Or for search and rescue, drones can cover a lot of territory and detect a human faster than a human can, right? And if you can actually find someone within the first 24 hours, the chance of survival is so much higher. All of that is leveraging the exact same technology that we do for looking at our business processes, right? And it's not as dramatic, it's not going to show up in the evening news, but honestly, streamlining our business processes, making it happen so much faster and more efficient, makes businesses more efficient. It's better for the company, it's better for the employees as well. So let's talk about something that's taboo, but financial services, making money with data or with analytics or machine learning from data. Again, we have to, John Furrier isn't here and we have someone from NVIDIA here and if we don't bring up blockchain in some type of way, he's going to throw something at his TV. So let's give a shout out to John Furrier. Let's give a shout out to John. But from a practical sense, let's subtract the digital currency part of a blockchain. Do you see application for blockchain from a machine learning perspective? Yeah, well, so if you just boil blockchain down or for trusted networks, right? And you heard Bill McDermott say that on stage, right? He calls marketplaces or areas that he can do for an exchange. It makes total sense. If I can have a trusted way of doing things where I can have a common ledger between companies and we know that it's valid, that we can each interchange with, yeah, it makes complete sense, right? Now we got to get to the practical implementation of that and we have to build the trust of the companies to understand, okay, this technology can take you there. And that's where I think, where we come in with our technology capabilities, ensuring people that it's reliable and work, SAP comes in with the customer relationships and trust that what they've been doing to help people run their business for years and then it becomes cultural. Like all things, we can kid ourselves in technology that we're just solve everything. It's a cultural change. I'm going to share that common ledger. I'm going to share that common network and feel confident in it. It's something that people have to do. And my take on that always is when the accuracy is so much better, when the efficiency is so much better, when the return is so much better, we get a lot more comfortable. People used to be nervous about giving their grocery or their phone number, because they would track their food. And today we're just like, oh yeah, here's my phone. Give me the 30 cent discount, here's my number. Exactly, we're so cheap. So we're in the NetApp booth and you guys recently announced a combined reference AI reference architecture with NetApp. Tell us a little bit more about that. Yeah, well the little secret behind all the things we just talked about, there's an incredible amount of data. And as you collect this data, it's really important to store it in a way that it's accessible when you need it. And when you are doing trainings, I have a product that's called DJX One. DJX One takes an incredible amount of data that helps us train these neural networks and it's fast and it has an insatiable desire for data. So what we worked with NetApp is actually pulling out a reference architecture so that when a data scientist who is a very valuable resource is working on this, he's ensured that the infrastructures are going to work together seamlessly and deliver that data to the training process. And then when you create that model, we do something that's called inference. You put it in production. And again, same time, when you're having that inference running, you want to make sure that data can get to it and it can interact with the data seamlessly and the reference architectures play out there as well. So our goal is start knocking off one by one, what do the customers need to be successful? And we put a lot of effort into the GPUs. We put a lot of effort into the deep learning software that runs on top of that. We put a lot of effort into, you know, what's the models they need to use, et cetera. And now we have to spend a lot more time of what's their infrastructure and make sure that's reliable because you would hate to do all that work only to find out your infrastructure had a hiccup and took your job down. So we're working really hard to make sure that it never happens. So I have this theory that, well, I don't have a theory. David McCurry came out with this theory of data has gravity. But I've come up with this additional theory. Now that we look at AI and the capability of AI and what people are and the hyperscalers are doing in their data centers that individual companies think have a challenge replicating in their own data center, this AI and compute now has gravity. You know, I can't, well, at least before today, I didn't think, well, I can take my data center and put it on a roll and do this massive pieces of injection on the edge. It sounds like that we're pushing back on that a little bit and saying that, you know what? Sure, if it's, I don't know what the limits are and I guess that's the question. What are the limits of what we can do on the edge when it comes to the amount of data and portable AI to that edge? Well, so there's, again, the two aspects. The training takes an incredible amount of data and that's why they would have to take a supercomputer and put it there so they can do the retraining. But when you think about when you can have the, something at the size of a credit card, which is our Jetson solution and you can install it in a drone or you could put it in cameras for, you know, public safety, et cetera, which is, has incredible. Think about looking for a lost child or a parents with Alzheimer. You can scan through video real quick and find them, right? All because of a credit card size processor. That's pretty impressive. But that's what's happening at the edge. We're now writing applications that are much more intelligent using AI. There are AI applications sitting at the edge that instead of just processing the data in a way where I'm getting average, average number of people who walked into my store, right? That's what we used to do five years ago. Now we're actually using intelligent applications that are making calculated decisions. It's understanding who's coming in the store, understanding they're buying, purchasing power, et cetera. That's extremely important in retail because if you want to interact with someone and give them that, you know, when they're doing self-checkout, try to sell them one more thing or try to, you know, did you forget the batteries they go with that or whatever you want it to be, you only have a few seconds, right? And so you must be able to process that and have something really intelligent doing that instead of just trying to do the law of average and get it directionally correct. And we've known this. Anytime you've been on your webpage or whatever and someone recommends something, you're like, that doesn't have anything to do with me. And then all of a sudden it started getting really good. That's where they're getting more intelligent. When I walk into the store of my wife's house hat and then they recommend their Max and Jersey, I'm going to look, I'm going to look, I'm looking for you guys in the video. Like, hey, I don't have money for a Jersey, but thanks a lot. We're just behind the scenes somewhere. There you go. Well, your title, VP and GM of deep learning and stuff, there's a lot of stuff. Jim, thanks so much for coming back on theCUBE, sharing with us what's new at Nvidia. It sounds like the world of possibilities is endless. So exciting. Yeah, it is an exciting time. Thank you. Thanks for your time. We want to thank you for watching theCUBE. Lisa Martin with Keith Townsend from SAP Sapphire 2018. Thanks for watching.