(gentle music playing) >> For centuries, we've been captivated by the concept of machines doing the job of humans. And over the past decade or so, we've really focused on AI and the possibility of intelligent machines that can perform cognitive tasks. Now in the past few years, with the popularity of machine learning models ranging from recent ChatGPT to Bert, we're starting to see how AI is changing the way we interact with the world. How is AI transforming the way we do business? And what does the future hold for us there. At theCube, we've covered Oracle's AI and ML strategy for years, which has really been used to drive automation into Oracle's autonomous database. We've talked a lot about MySQL HeatWave in database machine learning, and AI pushed into Oracle's business apps. Oracle, it tends to lead in AI, but not competing as a direct AI player per se, but rather embedding AI and machine learning into its portfolio to enhance its existing products, and bring new services and offerings to the market. Now, last October at Cloud World in Las Vegas, Oracle partnered with Nvidia, which is the go-to AI silicon provider for vendors. And they announced an investment, a pretty significant investment to deploy tens of thousands more Nvidia GPUs to OCI, the Oracle Cloud Infrastructure and build out Oracle's infrastructure for enterprise scale AI. Now, Oracle CEO, Safra Catz said something to the effect of this alliance is going to help customers across industries from healthcare, manufacturing, telecoms, and financial services to overcome the multitude of challenges they face. Presumably she was talking about just driving more automation and more productivity. Now, to learn more about Oracle's plans for AI, we'd like to welcome in Elad Ziklik, who's the vice president of AI services at Oracle. Elad, great to see you. Welcome to the show. >> Thank you. Thanks for having me. >> You're very welcome. So first let's talk about Oracle's path to AI. I mean, it's the hottest topic going for years you've been incorporating machine learning into your products and services, you know, could you tell us what you've been working on, how you got here? >> So great question. So as you mentioned, I think most of the original four-way into AI was on embedding AI and using AI to make our applications, and databases better. So inside mySQL HeatWave, inside our autonomous database in power, we've been driving AI, all of course are SaaS apps. So Fusion, our large enterprise business suite for HR applications and CRM and ELP, and whatnot has built in AI inside it. Most recently, NetSuite, our small medium business SaaS suite started using AI for things like automated invoice processing and whatnot. And most recently, over the last, I would say two years, we've started exposing and bringing these capabilities into the broader OCI Oracle Cloud infrastructure. So the developers, and ISVs and customers can start using our AI capabilities to make their apps better and their experiences and business workflow better, and not just consume these as embedded inside Oracle. And this recent partnership that you mentioned with Nvidia is another step in bringing the best AI infrastructure capabilities into this platform so you can actually build any type of machine learning workflow or AI model that you want on Oracle Cloud. >> So when I look at the market, I see companies out there like DataRobot or C3 AI, there's maybe a half dozen that sort of pop up on my radar anyway. And my premise has always been that most customers, they don't want to become AI experts, they want to buy applications and have AI embedded or they want AI to manage their infrastructure. So my question to you is, how does Oracle help its OCI customers support their business with AI? >> So it's a great question. So I think what most customers want is business AI. They want AI that works for the business. They want AI that works for the enterprise. I call it the last mile of AI. And they want this thing to work. The majority of them don't want to hire a large and expensive data science teams to go and build everything from scratch. They just want the business problem solved by applying AI to it. My best analogy is Lego. So if you think of Lego, Lego has these millions Lego blocks that you can use to build anything that you want. But the majority of people like me or like my kids, they want the Lego death style kit or the Lego Eiffel Tower thing. They want a thing that just works, and it's very easy to use. And still Lego blocks, you still need to build some things together, which just works for the scenario that you're looking for. So that's our focus. Our focus is making it easy for customers to apply AI where they need to, in the right business context. So whether it's embedding it inside the business applications, like adding forecasting capabilities to your supply chain management or financial planning software, whether it's adding chat bots into the line of business applications, integrating these things into your analytics dashboard, even all the way to, we have a new platform piece we call ML applications that allows you to take a machine learning model, and scale it for the thousands of tenants that you would be. 'Cause this is a big problem for most of the ML use cases. It's very easy to build something for a proof of concept or a pilot or a demo. But then if you need to take this and then deploy it across your thousands of customers or your thousands of regions or facilities, then it becomes messy. So this is where we spend our time making it easy to take these things into production in the context of your business application or your business use case that you're interested in right now. >> So you mentioned chat bots, and I want to talk about ChatGPT, but my question here is different, we'll talk about that in a minute. So when you think about these chat bots, the ones that are conversational, my experience anyway is they're just meh, they're not that great. But the ones that actually work pretty well, they have a conditioned response. Now they're limited, but they say, which of the following is your problem? And then if that's one of the following is your problem, you can maybe solve your problem. But this is clearly a trend and it helps the line of business. How does Oracle think about these use cases for your customers? >> Yeah, so I think the key here is exactly what you said. It's about task completion. The general purpose bots are interesting, but as you said, like are still limited. They're getting much better, I'm sure we'll talk about ChatGPT. But I think what most enterprises want is around task completion. I want to automate my expense report processing. So today inside Oracle we have a chat bot where I submit my expenses the bot ask a couple of question, I answer them, and then I'm done. Like I don't need to go to our fancy application, and manually submit an expense report. I do this via Slack. And the key is around managing the right expectations of what this thing is capable of doing. Like, I have a story from I think five, six years ago when technology was much inferior than it is today. Well, one of the telco providers I was working with wanted to roll a chat bot that does realtime translation. So it was for a support center for of the call centers. And what they wanted do is, Hey, we have English speaking employees, whatever, 24/7, if somebody's calling, and the native tongue is different like Hebrew in my case, or Chinese or whatnot, then we'll give them a chat bot that they will interact with and will translate this on the fly and everything would work. And when they rolled it out, the feedback from customers was horrendous. Customers said, the technology sucks. It's not good. I hate it, I hate your company, I hate your support. And what they've done is they've changed the narrative. Instead of, you go to a support center, and you assume you're going to talk to a human, and instead you get a crappy chat bot, they're like, Hey, if you want to talk to a Hebrew speaking person, there's a four hour wait, please leave your phone and we'll call you back. Or you can try a new amazing Hebrew speaking AI powered bot and it may help your use case. Do you want to try it out? And some people said, yeah, let's try it out. Plus one to try it out. And the feedback, even though it was the exact same technology was amazing. People were like, oh my God, this is so innovative, this is great. Even though it was the exact same experience that they hated a few weeks earlier on. So I think the key lesson that I picked from this experience is it's all about setting the right expectations, and working around the right use case. If you are replacing a human, the level is different than if you are just helping or augmenting something that otherwise would take a lot of time. And I think this is the focus that we are doing, picking up the tasks that people want to accomplish or that enterprise want to accomplish for the customers, for the employees. And using chat bots to make those specific ones better rather than, hey, this is going to replace all humans everywhere, and just be better than that. >> Yeah, I mean, to the point you mentioned expense reports. I'm in a Twitter thread and one guy says, my favorite part of business travel is filling out expense reports. It's an hour of excitement to figure out which receipts won't scan. We can all relate to that. It's just the worst. When you think about companies that are building custom AI driven apps, what can they do on OCI? What are the best options for them? Do they need to hire an army of machine intelligence experts and AI specialists? Help us understand your point of view there. >> So over the last, I would say the two or three years we've developed a full suite of machine learning and AI services for, I would say probably much every use case that you would expect right now from applying natural language processing to understanding customer support tickets or social media, or whatnot to computer vision platforms or computer vision services that can understand and detect objects, and count objects on shelves or detect cracks in the pipe or defecting parts, all the way to speech services. It can actually transcribe human speech. And most recently we've launched a new document AI service. That can actually look at unstructured documents like receipts or invoices or government IDs or even proprietary documents, loan application, student application forms, patient ingestion and whatnot and completely automate them using AI. So if you want to do one of the things that are, I would say common bread and butter for any industry, whether it's financial services or healthcare or manufacturing, we have a suite of services that any developer can go, and use easily customized with their own data. You don't need to be an expert in deep learning or large language models. You could just use our automobile capabilities, and build your own version of the models. Just go ahead and use them. And if you do have proprietary complex scenarios that you need customer from scratch, we actually have the most cost effective platform for that. So we have the OCI data science as well as built-in machine learning platform inside the databases inside the Oracle database, and mySQL HeatWave that allow data scientists, python welding people that actually like to build and tweak and control and improve, have everything that they need to go and build the machine learning models from scratch, deploy them, monitor and manage them at scale in production environment. And most of it is brand new. So we did not have these technologies four or five years ago and we've started building them and they're now at enterprise scale over the last couple of years. >> So what are some of the state-of-the-art tools, that AI specialists and data scientists need if they're going to go out and develop these new models? >> So I think it's on three layers. I think there's an infrastructure layer where the Nvidia's of the world come into play. For some of these things, you want massively efficient, massively scaled infrastructure place. So we are the most cost effective and performant large scale GPU training environment today. We're going to be first to onboard the new Nvidia H100s. These are the new super powerful GPU's for large language model training. So we have that covered for you in case you need this 'cause you want to build these ginormous things. You need a data science platform, a platform where you can open a Python notebook, and just use all these fancy open source frameworks and create the models that you want, and then click on a button and deploy it. And it infinitely scales wherever you need it. And in many cases you just need the, what I call the applied AI services. You need the Lego sets, the Lego death style, Lego Eiffel Tower. So we have a suite of these sets for typical scenarios, whether it's cognitive services of like, again, understanding images, or documents all the way to solving particular business problems. So an anomaly detection service, demand focusing service that will be the equivalent of these Lego sets. So if this is the business problem that you're looking to solve, we have services out there where we can bring your data, call an API, train a model, get the model and use it in your production environment. So wherever you want to play, all the way into embedding this thing, inside this applications, obviously, wherever you want to play, we have the tools for you to go and engage from infrastructure to SaaS at the top, and everything in the middle. >> So when you think about the data pipeline, and the data life cycle, and the specialized roles that came out of kind of the (indistinct) era if you will. I want to focus on two developers and data scientists. So the developers, they hate dealing with infrastructure and they got to deal with infrastructure. Now they're being asked to secure the infrastructure, they just want to write code. And a data scientist, they're spending all their time trying to figure out, okay, what's the data quality? And they're wrangling data and they don't spend enough time doing what they want to do. So there's been a lack of collaboration. Have you seen that change, are these approaches allowing collaboration between data scientists and developers on a single platform? Can you talk about that a little bit? >> Yeah, that is a great question. One of the biggest set of scars that I have on my back from for building these platforms in other companies is exactly that. Every persona had a set of tools, and these tools didn't talk to each other and the handoff was painful. And most of the machine learning things evaporate or die on the floor because of this problem. It's very rarely that they are unsuccessful because the algorithm wasn't good enough. In most cases it's somebody builds something, and then you can't take it to production, you can't integrate it into your business application. You can't take the data out, train, create an endpoint and integrate it back like it's too painful. So the way we are approaching this is focused on this problem exactly. We have a single set of tools that if you publish a model as a data scientist and developers, and even business analysts that are seeing a inside of business application could be able to consume it. We have a single model store, a single feature store, a single management experience across the various personas that need to play in this. And we spend a lot of time building, and borrowing a word that cellular folks used, and I really liked it, building inside highways to make it easier to bring these insights into where you need them inside applications, both inside our applications, inside our SaaS applications, but also inside custom third party and even first party applications. And this is where a lot of our focus goes to just because we have dealt with so much pain doing this inside our own SaaS that we now have built the tools, and we're making them available for others to make this process of building a machine learning outcome driven insight in your app easier. And it's not just the model development, and it's not just the deployment, it's the entire journey of taking the data, building the model, training it, deploying it, looking at the real data that comes from the app, and creating this feedback loop in a more efficient way. And that's our focus area. Exactly this problem. >> Well thank you for that. So, last week we had our super cloud two event, and I had Juan Loza on and he spent a lot of time talking about how open Oracle is in its philosophy, and I got a lot of feedback. They were like, Oracle open, I don't really think, but the truth is if you think about database Oracle database, it never met a hardware platform that it didn't like. So in that sense it's open. So, but my point is, a big part of of machine learning and AI is driven by open source tools, frameworks, what's your open source strategy? What do you support from an open source standpoint? >> So I'm a strong believer that you don't actually know, nobody knows where the next slip fog or the next industry shifting innovation in AI is going to come from. If you look six months ago, nobody foreseen Dali, the magical text to image generation and the exploding brought into just art and design type of experiences. If you look six weeks ago, I don't think anybody's seen ChatGPT, and what it can do for a whole bunch of industries. So to me, assuming that a customer or partner or developer would want to lock themselves into only the tools that a specific vendor can produce is ridiculous. 'Cause nobody knows, if anybody claims that they know where the innovation is going to come from in a year or two, let alone in five or 10, they're just wrong or lying. So our strategy for Oracle is to, I call this the Netflix of AI. So if you think about Netflix, they produced a bunch of high quality shows on their own. A few years ago it was House of Cards. Last month my wife and I binge watched Ginny and Georgie, but they also curated a lot of shows that they found around the world and bought them to their customers. So it started with things like Seinfeld or Friends and most recently it was Squid games and those are famous Israeli TV series called Founder that Netflix bought in, and they bought it as is and they gave it the Netflix value. So you have captioning and you have the ability to speed the movie and you have it inside your app, and you can download it and watch it offline and everything, but nobody Netflix was involved in the production of these first seasons. Now if these things hunt and they're great, then the third season or the fourth season will get the full Netflix production value, high value budget, high value location shooting or whatever. But you as a customer, you don't care whether the producer and director, and screenplay writing is a Netflix employee or is somebody else's employee. It is fulfilled by Netflix. I believe that we will become, or we are looking to become the Netflix of AI. We are building a bunch of AI in a bunch of places where we think it's important and we have some competitive advantage like healthcare with Acellular partnership or whatnot. But I want to bring the best AI software and hardware to OCI and do a fulfillment by Oracle on that. So you'll get the Oracle security and identity and single bill and everything you'd expect from a company like Oracle. But we don't have to be building the data science, and the models for everything. So this means both open source recently announced a partnership with Anaconda, the leading provider of Python distribution in the data science ecosystem where we are are doing a joint strategic partnership of bringing all the goodness into Oracle customers as well as in the process of doing the same with Nvidia, and all those software libraries, not just the Hubble, both for other stuff like Triton, but also for healthcare specific stuff as well as other ISVs, other AI leading ISVs that we are in the process of partnering with to get their stuff into OCI and into Oracle so that you can truly consume the best AI hardware, and the best AI software in the world on Oracle. 'Cause that is what I believe our customers would want the ability to choose from any open source engine, and honestly from any ISV type of solution that is AI powered and they want to use it in their experiences. >> So you mentioned ChatGPT, I want to talk about some of the innovations that are coming. As an AI expert, you see ChatGPT on the one hand, I'm sure you weren't surprised. On the other hand, maybe the reaction in the market, and the hype is somewhat surprising. You know, they say that we tend to under or over-hype things in the early stages and under hype them long term, you kind of use the internet as example. What's your take on that premise? >> So. I think that this type of technology is going to be an inflection point in how software is being developed. I truly believe this. I think this is an internet style moment, and the way software interfaces, software applications are being developed will dramatically change over the next year two or three because of this type of technologies. I think there will be industries that will be shifted. I think education is a good example. I saw this thing opened on my son's laptop. So I think education is going to be transformed. Design industry like images or whatever, it's already been transformed. But I think that for mass adoption, like beyond the hype, beyond the peak of inflected expectations, if I'm using Gartner terminology, I think certain things need to go and happen. One is this thing needs to become more reliable. So right now it is a complete black box that sometimes produce magic, and sometimes produce just nonsense. And it needs to have better explainability and better lineage to, how did you get to this answer? 'Cause I think enterprises are going to really care about the things that they surface with the customers or use internally. So I think that is one thing that's going to come out. And the other thing that's going to come out is I think it's going to come industry specific large language models or industry specific ChatGPTs. Something like how OpenAI did co-pilot for writing code. I think we will start seeing this type of apps solving for specific business problems, understanding contracts, understanding healthcare, writing doctor's notes on behalf of doctors so they don't have to spend time manually recording and analyzing conversations. And I think that would become the sweet spot of this thing. There will be companies, whether it's OpenAI or Microsoft or Google or hopefully Oracle that will use this type of technology to solve for specific very high value business needs. And I think this will change how interfaces happen. So going back to your expense report, the world of, I'm going to go into an app, and I'm going to click on seven buttons in order to get some job done like this world is gone. Like I'm going to say, hey, please do this and that. And I expect an answer to come out. I've seen a recent demo about, marketing in sales. So a customer sends an email that is interested in something and then a ChatGPT powered thing just produces the answer. I think this is how the world is going to evolve. Like yes, there's a ton of hype, yes, it looks like magic and right now it is magic, but it's not yet productive for most enterprise scenarios. But in the next 6, 12, 24 months, this will start getting more dependable, and it's going to change how these industries are being managed. Like I think it's an internet level revolution. That's my take. >> It's very interesting. And it's going to change the way in which we have. Instead of accessing the data center through APIs, we're going to access it through natural language processing and that opens up technology to a huge audience. Last question, is a two part question. And the first part is what you guys are working on from the futures, but the second part of the question is, we got data scientists and developers in our audience. They love the new shiny toy. So give us a little glimpse of what you're working on in the future, and what would you say to them to persuade them to check out Oracle's AI services? >> Yep. So I think there's two main things that we're doing, one is around healthcare. With a new recent acquisition, we are spending a significant effort around revolutionizing healthcare with AI. Of course many scenarios from patient care using computer vision and cameras through automating, and making better insurance claims to research and pharma. We are making the best models from leading organizations, and internal available for hospitals and researchers, and insurance providers everywhere. And we truly are looking to become the leader in AI for healthcare. So I think that's a huge focus area. And the second part is, again, going back to the enterprise AI angle. Like we want to, if you have a business problem that you want to apply here to solve, we want to be your platform. Like you could use others if you want to build everything complicated and whatnot. We have a platform for that as well. But like, if you want to apply AI to solve a business problem, we want to be your platform. We want to be the, again, the Netflix of AI kind of a thing where we are the place for the greatest AI innovations accessible to any developer, any business analyst, any user, any data scientist on Oracle Cloud. And we're making a significant effort on these two fronts as well as developing a lot of the missing pieces, and building blocks that we see are needed in this space to make truly like a great experience for developers and data scientists. And what would I recommend? Get started, try it out. We actually have a shameless sales plug here. We have a free deal for all of our AI services. So it typically cost you nothing. I would highly recommend to just go, and try these things out. Go play with it. If you are a python welding developer, and you want to try a little bit of auto mail, go down that path. If you're not even there and you're just like, hey, I have these customer feedback things and I want to try out, if I can understand them and apply AI and visualize, and do some cool stuff, we have services for that. My recommendation is, and I think ChatGPT got us 'cause I see people that have nothing to do with AI, and can't even spell AI going and trying it out. I think this is the time. Go play with these things, go play with these technologies and find what AI can do to you or for you. And I think Oracle is a great place to start playing with these things. >> Elad, thank you. Appreciate you sharing your vision of making Oracle the Netflix of AI. Love that and really appreciate your time. >> Awesome. Thank you. Thank you for having me. >> Okay. Thanks for watching this Cube conversation. This is Dave Vellante. We'll see you next time. (gentle music playing)

>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.

(bright music) >> Well, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're here joined by Ian Buck, general manager and vice president of Accelerated Computing at NVIDIA. I'm John Furrrier, host of theCUBE. Ian, thanks for coming on. >> Oh, thanks for having me. >> So NVIDIA, obviously, great brand. Congratulations on all your continued success. Everyone who does anything in graphics knows that GPU's are hot, and you guys have a great brand, great success in the company. But AI and machine learning, we're seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing in ML and AI that's accelerating computing to the cloud? >> Yeah. I mean, AI is kind of driving breakthroughs and innovations across so many segments, so many different use cases. We see it showing up with things like credit card fraud prevention, and product and content recommendations. Really, it's the new engine behind search engines, is AI. People are applying AI to things like meeting transcriptions, virtual calls like this, using AI to actually capture what was said. And that gets applied in person-to-person interactions. We also see it in intelligence assistance for contact center automation, or chat bots, medical imaging, and intelligence stores, and warehouses, and everywhere. It's really amazing what AI has been demonstrating, what it can do, and its new use cases are showing up all the time. >> You know, Ian, I'd love to get your thoughts on how the world's evolved, just in the past few years alone, with cloud. And certainly, the pandemic's proven it. You had this whole kind of fullstack mindset, initially, and now you're seeing more of a horizontal scale, but yet, enabling this vertical specialization in applications. I mean, you mentioned some of those apps. The new enablers, this kind of, the horizontal play with enablement for, you know, specialization with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >> Yeah. The innovation's on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIs, as well as machine learning techniques, that are just being invented by researchers and the community at large, including Amazon. You know, it started with these convolutional neural networks, which are great for image processing, but has expanded more recently into recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic, graph neural networks, where the actual graph now is trained as a neural network. You have this underpinning of great AI technologies that are being invented around the world. NVIDIA's role is to try to productize that and provide a platform for people to do that innovation. And then, take the next step and innovate vertically. Take it and apply it to a particular field, like medical, like healthcare and medical imaging, applying AI so that radiologists can have an AI assistant with them and highlight different parts of the scan that may be troublesome or worrying, or require some more investigation. Using it for robotics, building virtual worlds where robots can be trained in a virtual environment, their AI being constantly trained and reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box. To activate that, we are creating different vertical solutions, vertical stacks, vertical products, that talk the languages of those businesses, of those users. In medical imaging, it's processing medical data, which is obviously a very complicated, large format data, often three-dimensional voxels. In robotics, it's building, combining both our graphics and simulation technologies, along with the AI training capabilities and difference capabilities, in order to run in real time. Those are just two simple- >> Yeah, no. I mean, it's just so cutting-edge, it's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just go back to the late 2000s, how unstructured data, or object storage created, a lot of people realized a lot of value out of that. Now you got graph value, you got network effect, you got all kinds of new patterns. You guys have this notion of graph neural networks that's out there. What is a graph neural network, and what does it actually mean from a deep learning and an AI perspective? >> Yeah. I mean, a graph is exactly what it sounds like. You have points that are connected to each other, that establish relationships. In the example of Amazon.com, you might have buyers, distributors, sellers, and all of them are buying, or recommending, or selling different products. And they're represented in a graph. If I buy something from you and from you, I'm connected to those endpoints, and likewise, more deeply across a supply chain, or warehouse, or other buyers and sellers across the network. What's new right now is, that those connections now can be treated and trained like a neural network, understanding the relationship, how strong is that connection between that buyer and seller, or the distributor and supplier, and then build up a network to figure out and understand patterns across them. For example, what products I may like, 'cause I have this connection in my graph, what other products may meet those requirements? Or, also, identifying things like fraud, When patterns and buying patterns don't match what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two, captured by the frequency of how often I buy things, or how I rate them or give them stars, or other such use cases. This application, graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, is very exciting to a new application of applying AI to optimizing business, to reducing fraud, and letting us, you know, get access to the products that we want. They have our recommendations be things that excite us and want us to buy things, and buy more. >> That's a great setup for the real conversation that's going on here at re:Invent, which is new kinds of workloads are changing the game, people are refactoring their business with, not just re-platforming, but actually using this to identify value. And also, your cloud scale allows you to have the compute power to, you know, look at a note in an arc and actually code that. It's all science, it's all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS, specifically? >> Yeah, AWS have been a great partner, and one of the first cloud providers to ever provide GPUs to the cloud. More recently, we've announced two new instances, the G5 instance, which is based on our A10G GPU, which supports the NVIDIA RTX technology, our rendering technology, for real-time ray tracing in graphics and game streaming. This is our highest performance graphics enhanced application, allows for those high-performance graphics applications to be directly hosted in the cloud. And, of course, runs everything else as well. It has access to our AI technology and runs all of our AI stacks. We also announced, with AWS, the G5 G instance. This is exciting because it's the first Graviton or Arm-based processor connected to a GPU and successful in the cloud. The focus here is Android gaming and machine learning inference. And we're excited to see the advancements that Amazon is making and AWS is making, with Arm in the cloud. And we're glad to be part of that journey. >> Well, congratulations. I remember, I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was teasing this out, that they're going to build their own, get in there, and build their own connections to take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new interfaces, and the new servers, new technology that you guys are doing, you're enabling applications. What do you see this enabling? As this new capability comes out, new speed, more performance, but also, now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >> Well, so first off, I think Arm is here to stay. We can see the growth and explosion of Arm, led of course, by Graviton and AWS, but many others. And by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to Arm, we can help bring that innovation that Arm allows, that open innovation, because there's an open architecture, to the entire ecosystem. We can help bring it forward to the state of the art in AI machine learning and graphics. All of our software that we release is both supportive, both on x86 and on Arm equally, and including all of our AI stacks. So most notably, for inference, the deployment of AI models, we have the NVIDIA Triton inference server. This is our inference serving software, where after you've trained a model, you want to deploy it at scale on any CPU, or GPU instance, for that matter. So we support both CPUs and GPUs with Triton. It's natively integrated with SageMaker and provides the benefit of all those performance optimizations. Features like dynamic batching, it supports all the different AI frameworks, from PyTorch to TensorFlow, even a generalized Python code. We're activating, and help activating, the Arm ecosystem, as well as bringing all those new AI use cases, and all those different performance levels with our partnership with AWS and all the different cloud instances. >> And you guys are making it really easy for people to use use the technology. That brings up the next, kind of, question I wanted to ask you. I mean, a lot of people are really going in, jumping in big-time into this. They're adopting AI, either they're moving it from prototype to production. There's always some gaps, whether it's, you know, knowledge, skills gaps, or whatever. But people are accelerating into the AI and leaning into it hard. What advancements has NVIDIA made to make it more accessible for people to move faster through the system, through the process? >> Yeah. It's one of the biggest challenges. You know, the promise of AI, all the publications that are coming out, all the great research, you know, how can you make it more accessible or easier to use by more people? Rather than just being an AI researcher, which is obviously a very challenging and interesting field, but not one that's directly connected to the business. NVIDIA is trying to provide a fullstack approach to AI. So as we discover or see these AI technologies become available, we produce SDKs to help activate them or connect them with developers around the world. We have over 150 different SDKs at this point, serving industries from gaming, to design, to life sciences, to earth sciences. We even have stuff to help simulate quantum computing. And of course, all the work we're doing with AI, 5G, and robotics. So we actually just introduced about 65 new updates, just this past month, on all those SDKs. Some of the newer stuff that's really exciting is the large language models. People are building some amazing AI that's capable of understanding the corpus of, like, human understanding. These language models that are trained on literally the content of the internet to provide general purpose or open-domain chatbots, so the customer is going to have a new kind of experience with the computer or the cloud. We're offering those large language models, as well as AI frameworks, to help companies take advantage of this new kind of technology. >> You know, Ian, every time I do an interview with NVIDIA or talk about NVIDIA, my kids and friends, first thing they say is, "Can you get me a good graphics card?" They all want the best thing in their rig. Obviously the gaming market's hot and known for that. But there's a huge software team behind NVIDIA. This is well-known. Your CEO is always talking about it on his keynotes. You're in the software business. And you do have hardware, you are integrating with Graviton and other things. But it's a software practice. This is software. This is all about software. >> Right. >> Can you share, kind of, more about how NVIDIA culture and their cloud culture, and specifically around the scale, I mean, you hit every use case. So what's the software culture there at NVIDIA? >> Yeah, NVIDIA's actually a bigger, we have more software people than hardware people. But people don't often realize this. And in fact, that it's because of, it just starts with the chip, and obviously, building great silicon is necessary to provide that level of innovation. But it's expanded dramatically from there. Not just the silicon and the GPU, but the server designs themselves. We actually do entire server designs ourselves, to help build out this infrastructure. We consume it and use it ourselves, and build our own supercomputers to use AI to improve our products. And then, all that software that we build on top, we make it available, as I mentioned before, as containers on our NGC container store, container registry, which is accessible from AWS, to connect to those vertical markets. Instead of just opening up the hardware and letting the ecosystem develop on it, they can, with the low-level and programmatic stacks that we provide with CUDA. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make them so available. >> And programmable software is so much easier. I want to get that plug in for, I think it's worth noting that you guys are heavy hardcore, especially on the AI side, and it's worth calling out. Getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about, and looking at how they're doing? >> Yeah. For training, it's all about time-to-solution. It's not the hardware that's the cost, it's the opportunity that AI can provide to your business, and the productivity of those data scientists which are developing them, which are not easy to come by. So what we hear from customers is they need a fast time-to-solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it. >> John Furrier: Often. >> So in training, it's time-to-solution. For inference, it's about your ability to deploy at scale. Often people need to have real-time requirements. They want to run in a certain amount of latency, in a certain amount of time. And typically, most companies don't have a single AI model. They have a collection of them they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure. Leveraging the Triton inference server, I mentioned before, can actually run multiple models on a single GPU saving costs, optimizing for efficiency, yet still meeting the requirements for latency and the real-time experience, so that our customers have a good interaction with the AI. >> Awesome. Great. Let's get into the customer examples. You guys have, obviously, great customers. Can you share some of the use cases examples with customers, notable customers? >> Yeah. One great part about working at NVIDIA is, as technology company, you get to engage with such amazing customers across many verticals. Some of the ones that are pretty exciting right now, Netflix is using the G4 instances to do a video effects and animation content from anywhere in the world, in the cloud, as a cloud creation content platform. We work in the energy field. Siemens energy is actually using AI combined with simulation to do predictive maintenance on their energy plants, preventing, or optimizing, onsite inspection activities and eliminating downtime, which is saving a lot of money for the energy industry. We have worked with Oxford University. Oxford University actually has over 20 million artifacts and specimens and collections, across its gardens and museums and libraries. They're actually using NVIDIA GPU's and Amazon to do enhanced image recognition to classify all these things, which would take literally years going through manually, each of these artifacts. Using AI, we can quickly catalog all of them and connect them with their users. Great stories across graphics, across industries, across research, that it's just so exciting to see what people are doing with our technology, together with Amazon. >> Ian, thank you so much for coming on theCUBE. I really appreciate it. A lot of great content there. We probably could go another hour. All the great stuff going on at NVIDIA. Any closing remarks you want to share, as we wrap this last minute up? >> You know, really what NVIDIA's about, is accelerating cloud computing. Whether it be AI, machine learning, graphics, or high-performance computing and simulation. And AWS was one of the first with this, in the beginning, and they continue to bring out great instances to help connect the cloud and accelerated computing with all the different opportunities. The integrations with EC2, with SageMaker, with EKS, and ECS. The new instances with G5 and G5 G. Very excited to see all the work that we're doing together. >> Ian Buck, general manager and vice president of Accelerated Computing. I mean, how can you not love that title? We want more power, more faster, come on. More computing. No one's going to complain with more computing. Ian, thanks for coming on. >> Thank you. >> Appreciate it. I'm John Furrier, host of theCUBE. You're watching Amazon coverage re:Invent 2021. Thanks for watching. (bright music)

>>Yeah. >>Hello. Welcome to the Special Cube conversation here in Palo Alto, California. I'm John for a host of the Cube. We have a conversation around a I for the enterprise. What this means I got two great guests. John Finelli, Vice President, virtual GPU at NVIDIA and Maurizio D V D C T o University of Pisa in Italy. Uh, Practitioner, customer partner, um, got VM world coming up. A lot of action happening in the enterprise. John. Great to see you. Nice to meet you. Remotely coming in from Italy for this remote. >>John. Thanks for having us on again. >>Yeah. Nice to meet >>you. I wish we could be in person face to face, but that's coming soon. Hopefully, John, you were talking. We were just talking about before we came on camera about AI for the enterprise. And the last time I saw you in person was in Cuba interview. We were talking about some of the work you guys were doing in AI. It's gotten so much stronger and broader and the execution of an video, the success you're having set the table for us. What is the ai for the enterprise conversation frame? >>Sure. So, um, we, uh we've been working with enterprises today on how they can deliver a I or explore AI or get involved in a I, um uh, in a standard way in the way that they're used to managing and operating their data centre. Um, writing on top of you know, they're Dell servers with B M or V sphere. Um, so that AI feels like a standard workload that night organisation can deliver to their engineers and data scientists. And then the flip side of that, of course, is ensuring that engineers and data scientists get the workloads position to them or have access to them in the way that they need them. So it's no longer a trouble ticket that you have to submit to, I t and you know, count the hours or days or weeks until you you can get new hardware, right By being able to pull it into the mainstream data centre. I can enable self service provisioning for those folks. So we actually we make a I more consumable or easier to manage for I t administrators and then for the engineers and the data scientists, etcetera. We make it easy for them to get access to those resources so they can get to their work right away. >>Quite progress in the past two years. Congratulations on that and looking. It's only the beginning is Day one Mercy. I want to ask you about what's going on as the CTO University piece of what's happening down there. Tell us a little bit about what's going on. You have the centre of excellence there. What does that mean? What does that include? >>Uh, you know, uh, University of Peace. Are you one of one of the biggest and oldest in Italy? Uh, if you have to give you some numbers is around 50 K students and 3000 staff between, uh, professors resurgence and that cabinet receive staff. So I we are looking into data operation of the centres and especially supports for scientific computing. And, uh, this is our our daily work. Let's say this, uh, taking us a lot of times, but, you know, we are able to, uh, reserve a merchant percentage of our time, Uh, for r and D, And this is where the centre of excellence is, Uh, is coming out. Uh, so we are always looking into new kinds of technologies that we can put together to build new solutions to do next generation computing gas. We always say we are looking for the right partners to do things together. And at the end of the day is the work that is good for us is good for our partners and typically, uh, ends in a production system for our university. So is the evolution of the scientific computing environment that we have. >>Yeah. And you guys have a great track record and reputation of, you know, R and D, testing software, hardware combinations and sharing those best practises, you know, with covid impact in the world. Certainly we see it on the supply chain side. Uh, and John, we heard Jensen, your CEO and video talk multiple keynotes. Now about software, uh, and video being a software company. Dell, you mentioned Dale and VM Ware. You know, Covid has brought this virtualisation world back. And now hybrid. Those are words that we used basically in the text industry. Now it's you're hearing hybrid and virtualisation kicked around in real world. So it's ironic that vm ware and El, uh, and the Cube eventually all of us together doing more virtual stuff. So with covid impacting the world, how does that change you guys? Because software is more important. You gotta leverage the hardware you got, Whether it's Dell or in the cloud, this is a huge change. >>Yeah. So, uh, as you mentioned organisations and enterprises, you know, they're looking at things differently now, Um, you know, the idea of hybrid. You know, when you talk to tech folks and we think about hybrid, we always think about you know, how the different technology works. Um, what we're hearing from customers is hybrid, you know, effectively translates into, you know, two days in the office, three days remote, you know, in the future when they actually start going back to the office. So hybrid work is actually driving the need for hybrid I t. Or or the ability to share resources more effectively. Um, And to think about having resources wherever you are, whether you're working from home or you're in the office that day, you need to have access to the same resources. And that's where you know the the ability to virtualize those resources and provide that access makes that hybrid part seamless >>mercy What's your world has really changed. You have students and faculty. You know, Things used to be easy in the old days. Physical in this network. That network now virtual there. You must really be having him having impact. >>Yeah, we have. We have. Of course. As you can imagine, a big impact, Uh, in any kind of the i t offering, uh, from, uh, design new networking technologies, deploying new networking technologies, uh, new kind of operation we find. We found it at them. We were not able anymore to do burr metal operations directly, but, uh, from the i t point of view, uh, we were how can I say prepared in the sense that, uh, we ran from three or four years parallel, uh, environment. We have bare metal and virtual. So as you can imagine, traditional bare metal HPC cluster D g d g X machines, uh, multi GPU s and so on. But in parallel, we have developed, uh, visual environment that at the beginning was, as you can imagine, used, uh, for traditional enterprise application, or VD. I, uh, we have a significant significant arise on a farm with the grid for remote desktop remote pull station that we are using for, for example, uh, developing a virtual classroom or visual go stations. And so this is was typical the typical operation that we did the individual world. But in the same infrastructure, we were able to develop first HPC individual borders of utilisation of the HPC resources for our researchers and, uh, at the end, ai ai offering and ai, uh, software for our for our researchers, you can imagine our vehicle infrastructure as a sort of white board where we are able to design new solution, uh, in a fast way without losing too much performance. And in the case of the AI, we will see that we the performance are almost the same at the bare metal. But with all the flexibility that we needed in the covid 19 world and in the future world, too. >>So a couple things that I want to get John's thoughts as well performance you mentioned you mentioned hybrid virtual. How does VM Ware and NVIDIA fit into all this as you put this together, okay, because you bring up performance. That's now table stakes. He's leading scale and performance are really on the table. everyone's looking at it. How does VM ware an NVIDIA John fit in with the university's work? >>Sure. So, um, I think you're right when it comes to, uh, you know, enterprises or mainstream enterprises beginning their initial foray into into a I, um there are, of course, as performance in scale and also kind of ease of use and familiarity are all kind of things that come into play in terms of when an enterprise starts to think about it. And, um, we have a history with VM Ware working on this technology. So in 2019, we introduced our virtual compute server with VM Ware, which allowed us to effectively virtual is the Cuda Compute driver at last year's VM World in 2020 the CEOs of both companies got together and made an announcement that we were going to bring a I R entire video AI platform to the Enterprise on top of the sphere. And we did that, Um, starting in March this year, we we we finalise that with the introduction of GM wears V, Sphere seven, update two and the early access at the time of NVIDIA ai Enterprise. And, um, we have now gone to production with both of those products. And so customers, Um, like the University of Pisa are now using our production capabilities. And, um, whenever you virtualize in particular and in something like a I where performances is really important. Um, the first question that comes up is, uh doesn't work and And how quickly does it work Or or, you know, from an I t audience? A lot of times you get the How much did it slow down? And and and so we We've worked really closely from an NVIDIA software perspective and a bm wear perspective. And we really talk about in media enterprise with these fair seven as optimist, certified and supported. And the net of that is, we've been able to run the standard industry benchmarks for single node as well as multi note performance, with about maybe potentially a 2% degradation in performance, depending on the workload. Of course, it's very different, but but effectively being able to trade that performance for the accessibility, the ease of use, um, and even using things like we realise, automation for self service for the data scientists, Um and so that's kind of how we've been pulling it together for the market. >>Great stuff. Well, I got to ask you. I mean, people have that reaction of about the performance. I think you're being polite. Um, around how you said that shows the expectation. It's kind of sceptical, uh, and so I got to ask you, the impact of this is pretty significant. What is it now that customers can do that? They couldn't or couldn't feel they had before? Because if the expectations as well as it worked well, I mean, there's a fast means. It works, but like performance is always concerned. What's different now? What what's the bottom line impact on what country do now that they couldn't do before. >>So the bottom line impact is that AI is now accessible for the enterprise across there. Called their mainstream data centre, enterprises typically use consistent building blocks like the Dell VX rail products, right where they have to use servers that are common standard across the data centre. And now, with NVIDIA Enterprise and B M R V sphere, they're able to manage their AI in the same way that they're used to managing their data centre today. So there's no retraining. There's no separate clusters. There isn't like a shadow I t. So this really allows an enterprise to efficiently deploy um, and cost effectively Deploy it, uh, it without because there's no performance degradation without compromising what their their their data scientists and researchers are looking for. And then the flip side is for the data science and researcher, um, using some of the self service automation that I spoke about earlier, they're able to get a virtual machine today that maybe as a half a GPU as their models grow, they do more exploring. They might get a full GPU or or to GPS in a virtual machine. And their environment doesn't change because it's all connected to the back end storage. And so for the for the developer and the researcher, um, it makes it seamless. So it's really kind of a win for both Nike and for the user. And again, University of Pisa is doing some amazing things in terms of the workloads that they're doing, Um, and, uh and, uh, and are validating that performance. >>Weigh in on this. Share your opinion on or your reaction to that, What you can do now that you couldn't do before. Could you share your experience? >>Our experience is, uh, of course, if you if you go to your, uh, data scientists or researchers, the idea of, uh, sacrificing four months to flexibility at the beginning is not so well accepted. It's okay for, uh, for the Eid management, As John was saying, you have people that is know how to deal with the virtual infrastructure, so nothing changed for them. But at the end of the day, we were able to, uh, uh, test with our data. Scientists are researchers veteran The performance of us almost similar around really 95% of the performance for the internal developer developer to our work clothes. So we are not dealing with benchmarks. We have some, uh, work clothes that are internally developed and apply to healthcare music generator or some other strange project that we have inside and were able to show that the performance on the beautiful and their metal world were almost the same. We, the addition that individual world, you are much more flexible. You are able to reconfigure every finger very fast. You are able to design solution for your researcher, uh, in a more flexible way. An effective way we are. We were able to use the latest technologies from Dell Technologies and Vidia. You can imagine from the latest power edge the latest cuts from NVIDIA. The latest network cards from NVIDIA, like the blue Field to the latest, uh, switches to set up an infrastructure that at the end of the day is our winning platform for our that aside, >>a great collaboration. Congratulations. Exciting. Um, get the latest and greatest and and get the new benchmarks out their new playbooks. New best practises. I do have to ask you marriage, if you don't mind me asking why Look at virtualizing ai workloads. What's the motivation? Why did you look at virtualizing ai work clothes? >>Oh, for the sake of flexibility Because, you know, uh, in the latest couple of years, the ai resources are never enough. So we are. If you go after the bare metal, uh, installation, you are going into, uh, a world that is developing very fastly. But of course, you can afford all the bare metal, uh, infrastructure that your data scientists are asking for. So, uh, we decided to integrate our view. Dual infrastructure with AI, uh, resources in order to be able to, uh, use in different ways in a more flexible way. Of course. Uh, we have a We have a two parallels world. We still have a bare metal infrastructure. We are growing the bare metal infrastructure. But at the same time, we are growing our vehicle infrastructure because it's flexible, because we because our our stuff, people are happy about how the platform behaviour and they know how to deal them so they don't have to learn anything new. So it's a sort of comfort zone for everybody. >>I mean, no one ever got hurt virtualizing things that makes it makes things go better faster building on on that workloads. John, I gotta ask you, you're on the end video side. You You see this real up close than video? Why do people look at virtualizing ai workloads is the unification benefit. I mean, ai implies a lot of things, implies you have access to data. It implies that silos don't exist. I mean, that doesn't mean that's hard. I mean, is this real people actually looking at this? How is it working? >>Yeah. So? So again, um you know for all the benefits and activity today AI brings a I can be pretty complex, right? It's complex software to set up and to manage. And, um, within the day I enterprise, we're really focusing in on ensuring that it's easier for organisations to use. For example Um, you know, I mentioned you know, we we had introduced a virtual compute server bcs, um uh, two years ago and and that that has seen some some really interesting adoption. Some, uh, enterprise use cases. But what we found is that at the driver level, um, it still wasn't accessible for the majority of enterprises. And so what we've done is we've built upon that with NVIDIA Enterprise and we're bringing in pre built containers that remove some of the complexities. You know, AI has a lot of open source components and trying to ensure that all the open source dependencies are resolved so you can get the AI developers and researchers and data scientists. Actually doing their work can be complex. And so what we've done is we've brought these pre built containers that allow you to do everything from your initial data preparation data science, using things like video rapids, um, to do your training, using pytorch and tensorflow to optimise those models using tensor rt and then to deploy them using what we call in video Triton Server Inference in server. Really helping that ai loop become accessible, that ai workflow as something that an enterprise can manage as part of their common core infrastructure >>having the performance and the tools available? It's just a huge godsend people love. That only makes them more productive and again scales of existing stuff. Okay, great stuff. Great insight. I have to ask, What's next one's collaboration? This is one of those better together situations. It's working. Um, Mauricio, what's next for your collaboration with Dell VM Ware and video? >>We will not be for sure. We will not stop here. Uh, we are just starting working on new things, looking for new development, uh, looking for the next beast. Come, uh, you know, the digital world is something that is moving very fast. Uh, and we are We will not We will not stop here because because they, um the outcome of this work has been a very big for for our research group. And what John was saying This the fact that all the software stock for AI are simplified is something that has been, uh, accepted. Very well, of course you can imagine researching is developing new things. But for people that needs, uh, integrated workflow. The work that NVIDIA has done in the development of software package in developing containers, that gives the end user, uh, the capabilities of running their workloads is really something that some years ago it was unbelievable. Now, everything is really is really easy to manage. >>John mentioned open source, obviously a big part of this. What are you going to? Quick, Quick follow if you don't mind. Are you going to share your results so people can can look at this so they can have an easier path to AI? >>Oh, yes, of course. All the all the work, The work that is done at an ideal level from University of Visa is here to be shared. So we we as, uh, as much as we have time to write down we are. We are trying to find a way to share the results of the work that we're doing with our partner, Dell and NVIDIA. So for sure will be shared >>well, except we'll get that link in the comments, John, your thoughts. Final thoughts on the on the on the collaboration, uh, with the University of Pisa and Delvian, where in the video is is all go next? >>Sure. So So with University of Pisa, We're you know, we're absolutely, uh, you know, grateful to Morocco and his team for the work they're doing and the feedback they're sharing with us. Um, we're learning a lot from them in terms of things we can do better and things that we can add to the product. So that's a fantastic collaboration. Um, I believe that Mauricio has a session at the M World. So if you want to actually learn about some of the workloads, um, you know, they're doing, like, music generation. They're doing, you know, covid 19 research. They're doing deep, multi level, uh, deep learning training. So there's some really interesting work there, and so we want to continue that partnership. University of Pisa, um, again, across all four of us, uh, university, NVIDIA, Dell and VM Ware. And then on the tech side, you know, for our enterprise customers, um, you know, one of the things that we actually didn't speak much about was, um I mentioned that the product is optimised certified and supported, and I think that support cannot be understated. Right? So as enterprises start to move into these new areas, they want to know that they can pick up the phone and call in video or VM ware. Adele, and they're going to get support for these new workloads as they're running them. Um, we were also continuing, uh, you know, to to think about we spent a lot of time today on, like, the developer side of things and developing ai. But the flip side of that, of course, is that when those ai apps are available or ai enhanced apps, right, Pretty much every enterprise app today is adding a I capabilities all of our partners in the enterprise software space and so you can think of a beady eye enterprises having a runtime component so that as you deploy your applications into the data centre, they're going to be automatically take advantage of the GPS that you have there. And so we're seeing this, uh, future as you're talking about the collaboration going forward, where the standard data centre building block still maintains and is going to be something like a VX rail two U server. But instead of just being CPU storage and RAM, they're all going to go with CPU, GPU, storage and RAM. And that's going to be the norm. And every enterprise application is going to be infused with AI and be able to take advantage of GPS in that scenario. >>Great stuff, ai for the enterprise. This is a great QB conversation. Just the beginning. We'll be having more of these virtualizing ai workloads is real impacts data scientists impacts that compute the edge, all aspects of the new environment we're all living in. John. Great to see you, Maurizio here to meet you and all the way in Italy looking for the meeting in person and good luck in your session. I just got a note here on the session. It's at VM World. Uh, it's session 22 63 I believe, um And so if anyone's watching, Want to check that out? Um, love to hear more. Thanks for coming on. Appreciate it. >>Thanks for having us. Thanks to >>its acute conversation. I'm John for your host. Thanks for watching. We'll talk to you soon. Yeah,

(computerized music) >> Announcer: Live from Las Vegas, it's theCUBE. Covering Fortinet Accelerate 18. Brought to you by Fortinet. (computerized music) >> Hi, welcome back to Fortinet Accelerate 2018. I'm Lisa Martin with theCUBE. Excited to be back here for our second year. I'm joined by my esteemed cohost Peter Burris. Peter and I are excited to be joined by the chief information security officer of Fortinet, Phil Quade. Phil, welcome back to theCUBE >> Thanks of having me today. >> Great to have you here. So you had this interesting keynote this morning talking about cyber security fundamentals in the age of digital transformation. So we'll kind of peel apart that. But, something that I'm really curious about is, as a CISO, you are probably looked at as a trusted advisor to your peers, at Fortinet customers, at perspective customers. Tell us about, as we're in this evolution of security that Kenzie talked about, what are some of the things that you're hearing? What are they looking to you to help them understand and help from strategic perspective to enable in their environments? >> I often hear people say, "I recognize that my security's inadequate, what can I do about it?" Or, "I think my security's good enough, but I'm not evolving commensurably with the risk." And they say, "What do I do about that? How do I get to a better spot?" And I typically talk about them modernizing their strategy, and then based on their modernized strategy, that leads to specific technical solutions. And I'll have to talk to you more about what some of those might be. >> Yeah, on the strategy side of things, I find that very interesting. Peter and I were talking with Kenzie earlier, and with the 20 to 30 different security solutions that an organization has in place today that are disparate, not connected, where does the strategy discussion start? >> Well it starts to me with, I say, the adversary's comin at you at speed and scale, so how do you address the problems of speed and scale? It's through automation and integration. And fortunately, I believe in that strategy, but it plays directly into Fortinet's strengths, right? We have speed baked into our solution set. We have speed at the edge for our custom ASICs. And we're fundamentally are an integrated company where our products are designed to work together as a team because what you want to do strategy wise, is you want to, I think, you want to defend at your place of strength. And at a time and place of strength as opposed if your adversaries, where he's probing at your weak point. So, that's this integration thing's not only strategic, but it's essential to address the problems with speed and scale. >> So, Phil, technology's being applied to a lot of IT and other business disciplines. So, for example, when I was seeing machine learning, and related types of technologies actually being applied to improve programmer productivity through what we call augmented programming. And that may open the aperture on the number of people that actually can participate in the process of creating digital value. But it still requires a developer mindset. You still have to approach your problem from a developer perspective. What is the security mindset? That as security technology becomes more automated, that more people can participate, more people can be cognizant of the challenges. What is that constant security mindset that has to be sustained in an enterprise to continue to drive better and superior security. >> Got it. I think that some companies get too hyped about artificial intelligence, and I think it's important to remember that you need to use computer science to get to science fiction. So, a very disciplined way you need to say, well in order to achieve high degrees of automation, or perhaps machine learning, or artificial intelligence, what are the building blocks of that? Well, the building blocks are speed, because if you have a decision that's too late, who cares. Integration. If you have a decision that can't be communicated effectively, who cares. And then, of course, access to all the right types of data. In order to get smart to do machine learning, you need access to lots of different data sources, so you need to have lots of disparate centers sending in data for you to analyze. Back in my old job, we used to do some centralized processing, say back in the data center. We would precompute a result, we'd push that precomputed result back to the edge, and then you would do that last bit of analysis right at the point of need. And I think, again, the Fortinet architecture supports that in that we have a back end called Fortiguard Labs, if you know what that is. It does deep analysis and research, pushes their results forward, then we use speed at the edge inside customer premises to sort of compute, I'm mixing metaphors, but do the last mile of computing. So I think it's, back to your question, what's the mentality? It's about leveraging technology to our advantage, rather than people being the slaves of machines, we need to have machines serving more man. And we need computer science to do that, rather than, like I say, creating busy work for humans. >> Peter: Got it. >> You talked about speed and scale a minute ago. And as we look at, I'm curious of your perspective as the CISO, how do you get that balance between enabling digital business transformation, which is essential for growth, profitability, competition, and managing, or really balancing that with security risk management. So, if a business can't evolve digitally at speed and scale, and apply security protocols at every point they need to, is digital transformation meaningless? How do they get that-- >> Great question. Cause you don't want to feel like it's going to be a haves and have nots. The good news is that, for example, for those who seek to move to the cloud for whatever reason, convenience or agility or business efficiencies, you don't have to go all cloud or no cloud, right. And the security solutions of Fortinet allows you to do each. You can have some cloud, some non-cloud, and get them both to work together simultaneously under what we call a single pane of glass. So, as a user, you don't care if your firewall is a physical appliance or a virtual one, you want to establish a security policy and have that pushed out no matter what your firewall looks like. So to answer your question, I think that hybrid solutions are the way to go, and we need to let people know that it's not an all or nothing solution. >> That visibility that you kind of mentioned seems to have been kind of a bane of security folk's existence before. How do we get that broad visibility? >> Yeah, I think right, it's visibility and complexity I'd say are the bane of cyber security, right? Visibility, what you can't see, you can't defend against, and complexity is the enemy of security, right? So we need to address the problems. You asked me what CISOs say. We have to reduce complexity, and we have to improve visibility. And again, I think Fortinet's well postured to offer those types of solutions. >> So as you increase, we talk about the edge, you mentioned the edge. As more processing power goes to the edge, and more data's being collected, and more data's being acted upon at the edge, often independent of any essential resource, the threat of exposure goes up. Cause you're putting more processing power, or more data out there. How is securing the edge going to be different than securing other resources within the enterprise? >> Well encryptions will remain a part, right. Encryption to create confidentiality between the two computing entities is always a part. And then of course encryption can be used to authenticate local processes at the edge. So even though encryption might not be perceived as the silver bullet that it used to be, in the age of pending quantum computing, I can talk more about that in a second. In fact encryption is a fantastic tool for creating trust among entities and within an entity. So I think the applications of smart, strong encryption among and within the entities can create that web of trust we're talking to. If I could just briefly go back to quantum computing, right. So most commercial entities today, or most think tanks think that a quantum computer, a usable one, will be invented within 15ish or so years or so. Fortinet is actually already implementing quantum resistant cryptography in our products. >> Peter: Quantum what? >> It's called quantum resistant cryptography. And a quantum computer-- >> I understand. >> Will be able to break asymmetric encryption, so we're making sure we're implementing the algorithms today to future-proof our products against a future quantum computer. >> That's a major statement. Cause as you said, we're probably not looking at a more broad base utilization of quantum computing for many many many many years. And we'll know when they're being used by bad guys. We'll know who has one. How fast is that going to become a real issue. I mean as people think about it. >> The problem is that private sector doesn't know what the bad guy countries, when they will indeed have a computer, so Fortinet is being forward leaning, making sure we're starting to get familiar with the technology now. And also encryption's the type of thing that sometimes it requires special hardware requirements, special power-- >> Peter: Quantum computing does. >> No. Any encryption technology. The more computation you have to do, sometimes it might require more memory, or a faster processor. Well that takes months, if not years, if you're putting that into a custom chip. So we're planning and doing these things now, so we can make sure that we're ready, and aren't surprised by the actual compute power that's required of quantum resistant cryptography, or, and of course, aren't surprised when an adversary does in fact have one. >> Peter: Interesting. >> Good stuff. >> One of the things that you're doing later today is a panel, right? Between IT and OT folks. And I wanted to explore with you some of the evolution in the risks on the operational technology side. Tell us a little bit about what that panel today is going to discuss and maybe and example of, Triton for example, and how these types of attacks are now very prevalent from a physical stand point. >> Favorite topic of mine. Thanks for bringing it up. So one of the first things I'll do is I'll make the distinction between OT, operational technology, and IOT. So what I'll say is operational technology's designed primarily to work to protect the safety and reliability of physical processes and things. Things that move electricity, move oil and gas inside industrial automation plants. So operational technology. And then I'll talk a little bit more about IOT, the internet of things, which are primarily, and I'm cartooning a little bit, more about enabling consumer friendly things to happen. To increase the friendliness, the convenience, of our everyday lives. And so, once I make that distinction, I'll talk about the security solutions that are different between those. So, the OT community has done just fine for years, thank you very much, without the IT folks coming in saying I'll save your day. But that's because they've had the luxury of relying on the air gap. But unfortunately-- Meaning to attack an OT system you had to physically touch it. But unfortunately the air gap is dead or dying in the OT space as well. So we need to bring in new strategies and technologies to help secure OT. The IT side, that's a different story, because IOT is fundamentally lightweight, inexpensive devices without security built in. So we're not as a community going to automatically be able to secure IOT. What we're going to need to do is implement a strategy we call earned trust. So a two part strategy. Number one, rather than pretend we're going to be able to secure the IOT devices at the device level, that are currently unsecurable, we're going to move security to a different part of the architecture. Cause remember I talked about that's what you can do with security fabric, if you do defense as a team, you want to defend at the time and place you're choosing. So with IOT, we'll move the defense to a different part of the architecture. And what we'll implement is a strategy we call earned trust. We'll assign a level of trust to the IOT appliances, and then evaluate how they actually behave. And if they do in fact behave over time according to their advertised type of trust, we'll allow more, or in some cases, less access. So that's our IOT solution. And both of them are really important to the community, but they're very different IOT and OT. But unfortunately they share two letters and people are mixing them up to much. >> But at the same time, as you said, the air gap's going away, but also we're seeing an increasing number of the protocols and the technologies and other types of things start to populate into the OT world. So is there going to be a-- There's likely to be some type of convergence, some type of flattening of some of those devices, but it would be nice to see some of those as you said, hardened, disciplined, deep understanding of what it means to do OT security also start to influence the way IT thinks about security as well. >> Love it. Great point. Not only can the OT folks perhaps borrow some strategies and technologies from the IT folks, but the opposite's true as well. Because on the OT side, I know you're making this point, they've been securing their industrial internet of things for decades, and doing just fine. And so there's plenty that each community can learn from each other. You brought up a recent type of malware effecting OT systems Triton or Trisis. And the memory brings me back to about nine years ago, you might be familiar there was just a catastrophic incident in Russia at their-- It was a failure of operational technology. Specifically it was the largest electricity generation, hydroelectric plant, ninth biggest in the whole world, they took it offline to do some maintenance, loaded some parameters that were out of range, cause vibration in the machinery, and next thing you know, a major cover flew off, a 900 ton motor came off its bearings, water flooded the engine compartment, and it caused a catastrophic explosion. With I think, I'll just say, well over 50 people dying and billions of dollars of economic loss. So, what I'm trying to say is not, you know, get excited over a catastrophe, but to say that the intersection between physical and cyber is happening. There's not just the stuff of spy novels anymore. Countries have demonstrated the will and the ability to attack physical infrastructures with cyber capabilities. But back to Triton and Trisis. This is just a couple months ago. That sort of rocked the operational community because it was a very sophisticated piece of malware. And not only could it affect what are called control systems, but the safety systems themselves. And that is considered the untouchable part of operational technologies. You never want to affect the safety system. So the time is here. The opportunity and need is here for us to do a better job as a community to protecting the OT systems. >> So the speed, the scale, all the other things that you mentioned, suggests that we're moving beyond, and Kenzie has talked about this as well, the third generation of security. That we're moving beyond just securing a perimeter and securing a piece of hardware. We're now thinking about a boundary that has to be porous, where sharing is fundamentally the good that is being provided. How is a CISO thinking differently about the arrangement of hardware, virtuals, services, virtual capabilities, and, in fact, intellectual property services, to help businesses sustain their profile? >> I think you're spot on. The boundary as we know it is dead. You know, dying, if not dead. Right so, the new strategy is doing agile segmentation, both at the macro level and the micro level. And because you might want to form a coalition today that might break apart tomorrow, and that's why you need this agile segmentation. Back you your point about having some stuff in the cloud and some stuff perhaps in your own data center. Again, we don't want to make people choose between those two things. We need to create a virtual security perimeter around the data, whether part of it's existing in the data center or part of it exists in the cloud. And that again gets back to that strategy of agile segmentation at both macro and micro levels. And of course we need to do that with great simplicity so we don't overwhelm the managers of these systems with complexity that causes the human brain to fail on us. I'll often times say it's not the hardware or the software that fails us, it's the wetware. It's the brain that we have that we get overwhelmed by complexity and it causes us to do silly or sloppy things. >> So let me build on that thought one second, and come back to the role that you play within Fortinet, but also the CISO is starting to evolve into. As a guy who used to run not a big business, but a publicly traded company, I learned that when you wanted to go into a partnership with another firm, you got a whole bunch of lawyers involved, you spent a long time negotiating it, you set the parameters in place, and then you had a set of operating models with people that made sure that the partnership worked together. When we're talking about digital, we're talking about that partnership happening at much faster speeds, potentially much greater scale, and the issue of securing that partnership is not just making sure that the people are doing the right things, but the actual systems are doing the right things. Talk about the evolving role of the CISO as a manager of digital partnerships. >> I think you're right, it used to be the case where if you're entering a partnership, you're partner might say tell me a little bit more about how you secure your systems. And that company might say that's none of your business, thank you very much. But today, for the reasons you so well said, your risk is my risk. As soon as we start operating collaboratively, that risk becomes a shared situation. So, in fact, it becomes a responsibility of the CISOs to make sure the risks are appropriately understood and co-managed. Don't get me wrong, each company still needs to manage their own risk. But once you start richly collaborating, you have to make sure that your interfacing doesn't create new risks. So it used to be the day that only a couple of people in a company could say no. Of course the CEO, maybe the general council, maybe the CFO. But increasingly the CISO can say no too, because the exposure to a company is just too broad to take risks that you can't understand. >> And it's not a financial problem. It's not a legal problem. It's an operational problem >> That's right. That's right. And so the good news that CISOs I think are stepping up to the plate for that. The CISOs of today are not the CISOs of five, seven years ago. They're not insecure folks fighting for their posture C suite. They are valued members to the C suite. >> I wish we had more time guys, cause I would love to dig into that shared responsibility conversation. We've got to wrap up. Phil, thank you so much for stopping by theCUBE again, and sharing your insights on the strategic side, not only the evolution of Fortinet and security, but also the evolution that you guys are leading in at 2018 with your partners. We wish you a great time at the event, and we think you're having us back. >> Thanks for having me very much. I enjoyed talking to you both. >> And for my cohost Peter Burris, I'm Lisa Martin. We are live on theCUBE at Fortinet Accelerate 2018. Stick around and we'll be right back. (computerized music)