>> 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, where we're in the NetApp booth and talking with lots of partners and we're excited to welcome back to theCUBE, distinguished alumni Jim McHugh from NVIDIA, you are the VP and GM of Deep Learnings and "other stuff" as you said in the keynote. (all laugh) >> Yeah, and other stuff. That's a lot of responsibility! That other stuff, that, you know, that can really pile up! >> That can kill ya. 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're really helping SAP figure out and drive the development of their SAP Leonardo machine learning services so, machine learning, as we saw in the keynote today, with Haaso as a key component of it, and really what it's doing is it's automating a lot of the standard processes that people did, in the interactions, 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 and do that instantaneously, so you can get a continuous close. Things like service ticketing, so when a service ticket comes in, you know, we all know, you pick up the phone, you call 'em 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 a email, because now I know a lot about you when you send me an email I know who you are, know what company you're with, I know your problem 'cause you stated it, and I can route it, using machine learning, to the appropriate person. I can not only route it to the appropriate person I can look up in a knowledge database and say hey, have we seen this answer a 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, 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 at that and we're like you know that, 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? >> So most enterprises, if you're just jumpin' in and tryin' to figure it out, you would need all these people, you'd need a data scientist and someone to go through the process. 'Cause 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 'em code for nine months and out pops software, you know, eventually. We give 'em 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 scientists, 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 wanna actually take advantage of the SAP service for service ticketing or, you know, I wanna figure out how I can do my invoice processing better, or I'm an HR representative, and I don't wanna spend 60% of my time reading resumes, I wanna actually 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 wanna develop a custom app or other some things 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 wanna take it off, NVIDIA's happy to work with you directly and figure out how to solve different problems. And most of our customers are in all three of those, Right? They're consuming the services 'cause 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, now there's been a lot of news recently of tech for not-so-good and data privacy, GDPR, you know, compliance going into affect 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 at 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, a 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, the technician and the doctor would flip through a textbook from the 1950's, to determine it. Well Massachusetts General automated all that where they actually trained a neural network on all these different scans and all these different components and now you find out in minutes. So it greatly reduces the stress, right? And there's plenty of other project 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, whether you have Malaria, Dengue fever, so things like, you know what, maybe you shouldn't be around anywhere where you're gonna get bit by a mosquito and it's gonna 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, you know, 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 to be removed 'cause it's just bad for the whole component, or that's a diseased plant and I'm gonna 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 centers so it doesn't sit as long, right, so that we can actually have drones flying through the warehouses and knowing what needs to be moved first, 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 deliver it. So if you think that whole distribution center and just being in the pipeline as 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 dollar 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 small components, smaller components, you end up with these scenarios, these edge scenarios, use cases where, you know, whether it's data frequency, data value, or data latency, we have to push to 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 solutions? >> So it's, the world of IOT is changing as well, right, the compute power has to be where it's needed, right, and in any form, so whether that's cloud based, data center based, or at the edge and we have a great customer that is actually doing inspection, oil refineries, bridges, you know, where they spot a crack or some sort of mark 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, you know, on a truck and taking it back to your data center or 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 vans 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 you know, technicians and people who understand it and a guy who 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, you know, cracks in 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 and what you 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 gotta remember, enterprise isn't just like the corporate office whatever, enterprises are many different things, alright. Public safety, if you can think about that, that's a big thing we focus on. A really amazing thing that's going on, thinking about using drones for first responders they actually can know what's going on at 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, chance of survival is so much higher. All of that is, you know, leveraging the exact same technology that we do for looking at our business processes, right, and it's not as, you know, dramatic, it's not gonna show up on the evening news, but honestly, streamlining our business processes, making it happen so much faster and more efficient makes businesses more efficient, you know, it's better for the company, it's better for the employees as well. >> So let's talk about, something that's, that's taboo, financial services, making money with data, or with analytics or machine learning from data, again we have to, John Furrier is here, and we have someone from NVIDIA here, and if we don't bring up blockchain in some type of way he's gonna throw something at his team, so, >> Let's give a shout out to John Furrier. (laughing) >> Give a shout out to John. But from a practical sense, let's subtract the digital currency part of machine, of blockchain, do you see applications for blockchain from a machine learning perspective? >> Yeah, I mean well, if you just boil blockchain down or for trusted networks, right? And you know you heard Bill McDermott say that on stage he called his marketplaces, or areas that he could do for an exchange, it makes total sense. If I can have a trusted way of doing things where I 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 gotta get to the practical imitation 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, you know, where we come in with our technology capabilities, ensuring to people that it's reliable and work, SAP comes in with the customer relationships and trusted in what they've been doing in helping people run their business for years, and then it becomes cultural. Like all things, we can kid ourselves in technology that we'll just solve everything, it's a cultural change. I'm gonna share that common ledger, I'm gonna share that common network and feel confident in it, it's something that people have to do and, you know, 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 the grocery store their phone number, right, 'cause they would track their food, right? And today we're just like okay yeah here's my phone number. (Keith laughing) >> So. (laughs) >> Give you a 30 cent discount, here's my number. >> Exactly. We're so cheap. (laughing) >> So we're in the NetApp booth and you guys recently announced a reference, 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, right, 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're doing trainings, I have a product that's called DGX-1, DGX-1 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've worked with NetApp is actually pool together 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 gonna work together seamlessly and deliver that data to the training process. And then when you create that model, we use something that's called inference, you put it in production, and again same time, when you're having that inference running you wanna make sure that data can get to it and 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, etc. 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 that your infrastructure had a hiccup, and took your job down. So we're working really hard to make sure that never happens >> So I have this theory that, well I don't have the theory, David Curry 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 what the hyper scalers are doing in their data center is 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, put it on the road, and do these massive pieces of injection on the edge, sounds like we're pushin' 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 of it, the training takes an incredible amount of data that's why they would have to take a super computer and put it there so they could do the retraining, but, when you think about when you can have the pro-- something the size of a credit card, which is our Jetson solution, and you can install it in a drone or you can put in cameras for public safety, etc. Which is, has incredible, think about looking for a lost child or parents with Alzheimer's, you can scan through video real quick and find them, right? All because of a credit card sized 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 a 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 a store, understanding their buying/purchasing power, etc. That's extremely important in retail, because, if you wanna interact with someone and give them that, you know when they're doing self checkout, try to sell 'em one more thing, you know, did you forget the batteries that 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 a 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 with my White Sox hat and then they recommend the matching jersey. I'm gonna look, gonna come lookin' for you guys at NVIDIA like wa-hey! I don't have money for a jersey, but things like that, yeah. >> We're just behind the scenes somewhere. >> Well, you title VP and GM of Deep Learning and stuff, there's a lot of stuff. (all laugh) 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 wanna thank you for watching theCUBE, Lisa Martin with Keith Townsend from SAP SAPPHIRE 2018, thanks for watching. (bubbly music)