>> Announcer: SiliconANGLE Media presents theCUBE! covering Alibaba Cloud's annual conference, brought to you by Intel. Now here's John Furrier.... >> Hi I'm John Furrier, with SiliconANGLE, Wikibon and theCUBE. I'm the co-founder based in Silicon Valley, California, Palo Alto, California, and I am here in Hangzhou, China for the Alibaba Cloud conference in Cloud City, it's the biggest cloud computing conference here in China. I'm excited to be here with Dr. Min Wanli, who's the Chief Data Scientist and General Manager of Big Data division at Alibaba Cloud. Dr. Wanli, thank you for spending time. >> Thank you for having me. >> We have seen a lot of data in the conversation here at the show, data technology's a big part of this new revolution, it's an industrial revolution that we've never seen before, a whole 'nother generation of technology. What does data technology mean to Alibaba? >> Okay, it means everything. So first off, our internal technical speaking, it's technology handling massive real-time data and streaming data, and that's of different variety. For instance the app for the mobile app, for system knock, the customer behavior, they click, and they click browsing of the digital image of each merchant and asking for the price and compare against another similar product. All these behaviors are translated as data, and this data will be further merged with the archived data and try to update the profile of this customer's interests, and then try to detect whether there's a good match of they current merchant with the customer intent. If the match is good, then will flash this to the top priority, the top spot. So that try to increase the conversion rate. So if the conversion rate is high, then our sales is high. So DT, data technology means everything to Alibaba. >> It's interesting, I find my observation here, it's so fascinating because in the old days, applications produced data, that was stored on drives. They'd go to data warehouses, and they'd analyze them. You guys, in Alibaba Cloud are doing something fundamentally different, that's exciting in the sense that you have data, people call it data exhaust or data in general, but you're reusing the data in the development in real-time. So it's not just data exhaust, or data from an application. You're using the data to make a better user experience and make the systems smarter and more intelligent. Did I get that right? >> Exactly, exactly. This is a positive feedback loop, in a way, so in the old-fashioned way, you archived the data for offline analysis and for post-event analysis, and trying to identify whether there's any room for improvement. But that's fine. But now people cannot wait, and we cannot wait. Offline is not enough. So we have to do this in real time, online, in a feedback version, in search of a way that we could capture exactly at the right moment, understand the intent of the customer, and then try to deliver the right content to the customer on the fly. >> Jackie Ma, or Jack Ma, your boss, and also Dr. Wong who I spoke with yesterday, talk about two things. Jack Ma talks about a new revolution, a new kind of industrial revolution, a smarter world, a better society. Dr. Wong talks about data flowing like a river, and you hear Hangzhou as an example, but it highlights something that's happening across the world. We're moving from a batch, slow world with data to one that's in motion and always real time. They're not necessarily mutually exclusive, but they're different. A data lake or a data river, whatever word you want, I don't really like the word data lake personally, I think it means, it's batch to me. But batch has been around for a while. Real time mixed streaming. This is something that's happening, and it's impacting the architecture and the value proposition of applications, and it's highlighted in Internet of Things, it's highlighted in examples that we're seeing that's exciting like the ET Brains. Can you share your view in your project around ET Brains, because that is not just one one vertical. It's healthcare, it's industrial, it's transportation, it's consumer, it's everything. >> Yeah, good question, so first of all I concur with you that data lake already exists, will continue to exist, because it's got its own value because our ET Brain for example, actually emerged from data lake, because it has to learn all the benchmark, the baseline model, the basic knowledge from the existing archive data, which is a data lake. However, that's not enough. Once you have the knowledge, you have the capability but you need to put that in action. So we are talking about data in motion, data in action. How do we do that? So once you have the training example, all the training data from data lake, and you train the brain, the brain is mature enough and in the next step you want to push the brain coupled with real-time streaming data, and then to generate real-time action in real-time manner in preemptive way, rather than posting in a reactive way. So for example, in transportation and travel, T and T, travel and transportation, and traffic management. So currently, all the authorities, they have access to real-time information, and then they do a post-event analysis if there's a traffic jam, and then they want to do some mitigation. However, the best scenario is, if you can prevent the traffic jam from happening in the first place, right, how can you foresee there will be, there would be, there could be traffic jam happen in 10 minutes from now, and then you take a preemptive strike, and then try to prevent that from happening. That's the goal ET Brain, in traffic management want to achieve. Like for example, you see the ambulance case, and once the ET Brain receives the message say the ambulance is going to go to Point A, pick up a patient, and carry that patient, rush them to Hospital B, and then it immediately calculates the right routing, the driving direction, and the calculate the ETA to every intermediate intersection and then try to coordinate with the traffic lights, traffic signal. All this systematic integration will create on demand a green wave for ambulance, but in the past ambulance is just by the siren, right. >> Yeah, this is fascinating, and also I'd like to get your thoughts, because you bring us something that's important, and that is, and I'd like to connect the dots for the audience, and that is, real time matters. If you're crossing the street, you can't be near real time, because you could get hit by a car. But also latency's important, also the quality of the data is good. I was talking to an executive who's laying out an architecture for a smart city, and he said, "I want the data in real time," and the IT department said, "Here it is, "it's in real time", and he says, "No, that's last year's data." And so the data has to be real time and the latency has to be low. >> Exactly. I completely agree. The latency has to be low. Unfortunately, in the current IT infrastructure, very often the latency exist. You cannot eliminate that, right? And then you have to live with that, so the ET Brain acknowledge the fact, in fact we have our own algorithm designed in a way that it can make a shortened prediction. So based on five minutes ago data, the data collected five minutes ago, and then it can project the next five minutes, next 10 minutes, what would be the data, and then use that to mitigate, or to conquer, to offset the latency. So we find that to be a good strategy, because it's relatively easy to implement, and it is fast and efficient. >> Dr. Wanli, fascinating conversation. I'd like to get your thoughts on connecting that big data conversation or data conversation to this event. This is a cloud computing event. We at theCUBE and SiliconANGLE and our Wikibon research team we go to all the events. But sometimes the big data events are about big data, Hadoop, whatever, and then you have cloud, talking about DevOPs, and virtual machines. This conference is not just a siloed topic. You have cloud computing, which is the compute, it's the energy, it's the unlimited compute potential, but it's also got a lot of data. You guys are blending it in. >> Exactly. >> Is that by design, and why is that important? >> It's by design. Actually, you cannot separate cloud from data, big data. Or you cannot talk big data without referring to cloud, because once the data is big, you need a huge computation power. Where does that come from? Cloud computing. So that means that data intelligence, all the value has to require a good technological tool to unleash the value. What's the tool? Cloud computing. For example, the first time IBM come up with a smart plan, a smart city, that's 2005 or 2006, around that time, there's no cloud computing yet, at the earliest emerging stage. And then we see what happens. And the smarter city and then gradually become IT infrastructure construction. But it's not DT, data technology. So they invested billions of dollars in the infrastructure level, and they collect so much data, but all the data become a burden to the government, to save, to archive the data or protect the data from hacking, right. Now, these days, if you have the cloud computing available, you can do real-time analytics to unleash the value in the first place, at the first moment you receive the data and then later on you know which data is more valuable, which data is of less value, and then you know how much you want to archive. >> Our Wikibon research team put out research this past year that said IT is no longer a department, it's everywhere, >> It's everywhere >> it supports your DT, data technology, it's a fabric. But one thing that's interesting going back to 2005 to now is not only the possibility for unlimited compute, is that now you're seeing wireless technologies significantly exploding in a good way, it's really happening. That's also going to be a catalyst for change. >> Definitely. >> What's your thoughts on how wireless connectivity, 'cause you have all these networks, you have to move data around, it has to be addressable, you have to manage security. That's a heavy load.\ what do you do, how are you guys doing that? >> Okay, very good question. We faced this challenge a couple of years ago, we realized that, because in Chinese domestic market, the users they are migrating from PC to mobile, and this create the mobile phone has wi-fi, right, so interacts with another AP, Access Point, right. So then how do we recognize our tracking, and recognizes ID identification, all this stuff, create huge headache to us, and this time, in this conference, we announce our solution for mobile, for mobile cloud. So what does that mean? So essentially, we have a cloud infrastructure product designed in order to do a real-time integration and do a data cleansing of the mobile data. I mean by mobile, and wireless as well. Wireless means even bluetooth, or even IoT, IoT solution also supported there. So this is a evolving process in the way. The first solution probably is less than perfect, but gradually, as we are expanding into more and more application scenario, and then we will amalgamate the solution and try to make it more robust. >> You guys have a good opportunity, and Alibaba Cloud certainly met with Karen Liu about the opportunity in North America and United States where I'm from. But Alibaba Cloud, and Alibaba Group, in the Alibaba Cloud has had a great opportunity, almost a green field, almost a clean sheet of paper, but you have a very demanding consumer base here in China. They're heavily on mobile as you pointed out, but they love applications. So the question I want to ask you is, and I'd love your thoughts on this. How do you bring that consumerization, its velocity, the acceleration of the changing landscape of the consumer expectation and their experience to small businesses and to enterprises? >> Ok, very good question. So user not just customer base, and the demanding customers in China trying to help us to harden our product, harden our solution, and to reduce the cost, the overall cost, and the economy of mass scale, economy of scale, and then once we reach that critical point, and then our service is inexpensive enough, and then the small and medium, SMB, small and medium business they could afford that. And in old days, SMB, they want to have access to high performance computing, but they do not have enough budget to afford the supercomputer. But these days now, because our product, our computation product, cloud product, big data product is efficient enough, so the total cost is affordable. And then you see that 80% of our customers of Alibaba, at least 80%, are actually SMB. So we believe the same practice can be applied to overseas market as well. >> You bring the best practices of the consumer and the scale of Alibaba Cloud to the small and medium-sized enterprises, and they buy as they grow. >> Exactly. >> They don't buy a lot upfront. >> Yeah, yeah, they buy on demand, as they need. >> That's the cloud, the benefit of the cloud. >> Exactly. >> Okay, the compute is great, you've got greatness with the compute power, it's going to create a renaissance of big data applications where you see that. What is your relationship with Intel and the ecosystem, because we see, you guys have the same playbook as a lot of successful companies in this open source era, you need horsepower and you need open source, what is Alibaba's strategy around the ecosystem, relationship with Intel, and how are you guys going to deal with partners? >> Yeah, first of all, so we really happy that we have Intel as our partner. In our most recent big data hackathon for the medical AI competition, and we just closed that competition, that data hackathon. Okay, very fascinating event, okay. Intel provided a lot of support. All the participants of this data hackathon, they do their computing leveraging on the Intel's products, because they do their image process. And then we provided the overall computing platform. Okay, this is a perfect example of how we collaborated with our technology partners. Beyond Intel, in terms of the ecosystem, first of all, we are open. We are building our ecosystem. We need partners. We need partners from pure technology perspective, and we also need partners from the traditional vertical sectors as well, because they provide us domain knowhow. Once we couple our cloud computing and big data technology with the domain knowhow, the subject matter expertise, well together the marriage will generate a huge value. >> That's fantastic, and believe me, open source is going to grow exponentially, and by 2025 we predict that it's going to look like a hockey stick. From the Linux foundation that's doing amazing work, you're seeing the Cloud Native Foundation. I want to get your thoughts on the future generation. >> Yeah, you mean open source? >> The future generation that's using open source, they're younger, you guys have tracked, you know the demographics in your employee base, you have a cloud native developer now emerging. They want to program the infrastructure as they go. They don't want to provision servers, they want the street lights to just work, whatever the project, the brains have to be in the infrastructure, but they want to be creative. You're bringing two cultures together. And you've got AI, it's a wonderful trend, machine learning is doing very well. How do you guys train the younger generation, what's your advice to people looking at Alibaba Cloud, that want to play with all the good toys? You got machine learning, you got AI, they don't want to necessarily baby, they don't want to program either. They don't want to configure switches. >> Yeah, very good question. Actually this is related to our product strategy. So in a way, like today we announce our ET Brain, so we are going to release this and share this as a platform to nurture all the creative mind, creative brains, okay, people, trying to leverage on this brain and then do the creative job, rather than worry about the underlying infrastructure, the basic stuff. So this is that part which we want to share with the young generation, tell them that unleash your creativity, unleash your imagination, don't worry about the hard coding part, and we already build the infrastructure, the backbone for you. And then image anything you think possible and then try to use ET Brain, try to explore that. And we provide the necessary tool and building blocks. >> And the APIs. >> And the APIs as well, yes. >> Okay, so I want to get your thoughts on something important to our audience, and that is machine learning, the gateway to AI. AI, what is AI? AI software, using cloud. Some will argue that AI hasn't really yet come on the scene but it's coming. We love AI, but machine learning is really where the action is right now, and they want to learn about how to get involved in machine learning. So what's your view on the role of machine learning, because now you have the opportunity for a new kind of software development, a lot of math involved, that's something that you know a lot about. So is there going to be more libraries? What's your vision on how machine learning moves from a bounded use case to more unbounded opportunities, because, I'm a developer, I want the horizontally scalable resource of the cloud, but I'm going to have domain expertise in a vertical application. So I need to have a little bit of specialism, and I want the scalability. So data's got to move this way and it's got to be up this way. >> Yes, yeah, okay, let me put it this way. So first off, for people who are really interested in AI, or they want to work on AI, my recommendation first of all, you got to learn some mathematics. Why, because all the AIs and machine learnings is talking about algorithms, and those algorithms are actually all about math, mathematics, the formula, and also the optimization, how to speed up the convergence of the algorithms, right. So all this maths is important, okay. And then if you have that math background, and then you have the capability to judge or to see next, which algorithm, or which machine software is suitable to solve the vertical problems. Very often the most popular algorithm may not be the right one to solve the specific vertical problems. So you're going to the way, capability to differentiate and to see that and make the right choice. That's the first recommendation. The second recommendation, try to do as many type of examples as possible, try to get your hands on, don't stop at looking at the function specification and oh, this is a function and input, output, da da da, but you need to get your hands dirty, get your hands on the real problem, the real data. So that you can have a feeling about how powerful it is or how bad or how good it is. Once you have this kind of experience, and then you do have capability, you gradually build up a cumulative capability to make a right choice. >> This is fascinating, Dr. Wanli, this is fantastic. I want to follow up on that because you're bringing up, in my mind I can almost see all these tools. There's an artisan culture coming on. You're seeing that. Dr. Wong discussed that with me yesterday. Artisans meeting technologists, scientists and creatives. UI, we're seeing evolutions in user experience that's more art. And so culture's important. But the machine learners of the algorithms, sometimes you have to have a lot of tools. If you have one tool, you shouldn't try to use tools for other jobs. So bring this up. How should a company who's architecting their business or their application look at tooling, because on one hand, there's the right tool for the right job, but you don't want to use a tool for a job that it's not designed for. To your point. Tools, what's your advice and philosophy on the kinds of toolings and when to engage platforms, relationship between platforms and tools. >> Okay, then put it this way. So, this is a decision based on a mixture of different criteria together. So first of all, from technology perspective, and secondly from the business perspective. From technology perspective I would say if your company's critical competence is technical stuff, and then you've got to have your own tool, your own version. If you only rely on some existing tool from other companies, your whole business actually is dependent on that, and this is the weakest link, the most dangerous link. But however, very often to develop your own version of the tool takes forever, and market wouldn't give you so much time. And then you need too strike a balance, how much I want to get involved for self development and how much for in-house development, and it's how much I want to buy in. >> And time. >> And time as well, yes. And another one is that you've got to look at the competitive landscape. If this tool actually has already existed for many years and many similar product in the market, and the problem is not a good idea to reproduce or reinvent, and then you're going to why not buy it, you take that for granted. And it think that's a fact, and then you build a new fact, right. So this is another in terms of the maturity of the tool, and then you need to strike a balance. And in the end, in the extreme case, if your business, your company is doing a extremely new, innovative, first of a kind study or service, you probably need some differentiate, and that differentiator probably is a new tool. >> Final question for you. For the audience in America, in Silicon Valley, what would you like to share from your personal perspective about Alibaba Cloud that they should know about? Or they might not know about and should know about. >> Okay, 'cause I worked in the US for 16 years. To be frank, I knew nothing about Alibaba until I came back. So as a Chinese overseas, I'm so ignorance about Alibaba until I came back. So I can predict, I can guess, more or less, in the overseas market, in US customers, they probably know not that much about Alibaba or Alibaba Cloud. So my advice and from my personal experience, I say, first of all, Alibaba is a global company, and Alibaba Cloud is a global company. We are going to go global. It's not only a Chinese company, for example. We are going to serve customers overseas market in Europe and North America and Southeast Asia. So we want to go global first. And second, we are not only doing the cloud. We are doing blending of cloud and big data and vertical solutions. I call this VIP. V for vertical, I for innovation. P for product. So VIP is our strategy. And the innovation is based upon our cloud product and big data product. >> And data's at the center of it. >> Data is the center of this, and we already got our data technique, our data practice from our own business, which is e-commerce. >> And you're solving some hard problems, the ET Brain's a great playground of AI opportunity. You must be super-excited. >> Yeah, yeah, right, right, okay. >> Are you having fun? >> Yes, a lot of fun. Very rewarding experience. A lot of dreams really come true. >> Well, certainly when you come to Silicon Valley, I know you have a San Mateo office, we're in Palo Alto, and this is theCUBE coverage of Alibaba Cloud. I'm John Furrier, co-founder of SiliconANGLE, Wikibon research and theCUBE, here in China covering the Alibaba Cloud, with Dr. Wanli, thanks for watching.

>> SiliconANGLE Media presents theCUBE! Covering the Alibaba Cloud annual conference. Brought to you by Intel. Now, here's John Furrier... >> Hello everyone, welcome to exclusive coverage with SiliconANGLE, Wikibon, and theCUBE here in Hangzhou, China for Alibaba Cloud's annual event here in Cloud City, the whole town is a Cloud. This is their event with developers, music festivals, and again, theCUBE coverage. Our next guest is Dhiraj Mallick, who is the Vice President of the Data Center Group, and the General Manager of Innovation, Pathfinding, and Architecture Group. That's a mouthful. Basically the CTO of the Data Center Group, trying to figure out the next big thing. >> That's right, John. >> Thanks for spending the time. >> It's my pleasure. >> We're here in China, it's-- You know in the U.S., we're looking at China, and we say okay, the fourth largest Cloud, Alibaba Cloud? >> Yes. >> Going outside of Mainland China, going global. You guys are strategic partners with them. >> Yes. >> They need a lot of compute, they need a lot of technology. Is this the path that you're finding for Intel? >> Yeah, so we've been collaborators with Alibaba for over 10 years, and we view them as a very strategic partner. They're one of the Super Seven, which is our top seven Cloud providers, and certainly in China, they're a very relevant customer for many years. We engage with them on a variety of fronts. On the technology side, we engage with them on what their key pinpoints are, what is the problems they want to be solving three to five years out, and then we co-develop, or co-architect solutions with them. >> So, I want to get your take on the event here in China, and how it relates to the global landscape, because I, it's my first time here, and I was taken back by the booth. I walked through Alibaba's booth, and obviously Jack Ma is inspirational. Steve Jobs like the culture, and artistry and science coming together, but I walked through the booth, it's almost too good to be true. They've got Quantum Computing, a Patent Wall, they've got Hybrid Cloud, they got security, they have IoT examples with The City Brain, a lot of great tech here at Alibaba Cloud. >> So I think the technologies that they're investing in are very, very impressive. Most cloud companies are probably not as far along as them, and looking at such a broad range of technologies, the Brain Project is really exciting, because it's going to be the Nexus of smart cities, both in China, as well as globally. The second thing that's very interesting is their research and investments in Quantum. While Quantum is not here today, it's certainly on the frontier, and Intel also has significant investments in sort of unpacking where Quantum will go, and what promises it offers to address. >> What I find interesting is that also hearing the positioning of, I kind of squint through the positioning, they're almost talking Cloud-native, DevOps, but they have all this goodness under the hood, and they're kind of talking IT-transitioning to Data Technology. Everything's about data to these guys, not just collecting data, using data with software. Now, that's really critical, because isn't that software-defined, data-driven is a hot trend? >> Yes, software-defined and data-driven is a very hot trend, in fact at Intel our CEO and us all believe that we've entered the data economy, and that the explosion in data is, and the thirst for analyzing that data to be able to drive smart business analytics is really the key to this digital revolution. I was reading an industry report by one of the analysts that said by 2019 there would have been over 100 billion dollars spent on business intelligence. And so, the real key is this data economy. >> The intersection of things, and even industrial internet, IIot, Industrial Iot, with artificial intelligence AI, intelligence Intel inside that word, interesting play on words-- >> Yes. >> Is coming together, and we've covered what you guys were doing on Mobile World Congress this year, where 5G was clearly an end-to-end architecture. You got FPGAs, all this goodness here going on. So that's 5G, and that's going to fuel a lot of IoT if you think of it like that way, but now AI. >> Yes. >> It's Software. How does that connect? Because that's the path we see forward on the Wikibon analyst side, we see software eating the world, but data eating software. And now you got 5G creating more data. >> Yeah, so the way we look at it at Intel is, we have data-center technologies that are fueled by the growth at the Edge by IoT devices, because they're creating demand for more processing capability to be able to unpack and analyze that information, and it's a self-fulfilling circle. We call it the virtual cycle of growth, because the data center feeds IoT demand and then IoT feeds the data center. And so it's the combination of those. What 5G does, is 5G forms the connectivity fabric between the data center and the Edge. It allows data to be pre-positioned at the correct places in the network, so that you minimize latencies through the network, and can process or do the analytics on it as quickly as you possibly can. >> So we were talking before we came on camera about Jack Ma, they call him Jackie Ma here, keynote being very inspirational, and talking moving to a new industrial era, a digital economy, all that good stuff, very, very inspirational. Let's translate that into the data center transformation, because we're seeing the data center and the Cloud with Hybrid Cloud become really critical to support what you were just talking about which is, how do you put it all together? It sounds so easy, but it really is difficult. >> It is, and so our vision is that in order to be able to fulfill this data economy, we will need to have five key innovations in the data center. The first innovation, in no particular order, is that the data center will be frictionless. And what I mean by frictionless, is that there will be zero to low latencies in order to provide that real-time experience at the Edge. So latency is extremely critical, and the way we believe that that can be achieved is by moving from copper to light. And Intel has significant investments in leadership products and silicon photonics that will enable switches to be based on photonics. It'll enable CPUs, and server hosts to be based on light. So we believe that light is a critical aspect to this success. The second aspect of frictionless is the need for liquid cooling and that was in the keynotes from Simon Hu this morning, that the liquid cooling is going to be essential to be able to enable a lot more horsepower in these data centers to be able to handle the volume of data that's coming. >> So you guys obviously with the photonics and the liquid cooling, you guys have been working on this in your labs for a long time, it's great R&D, but you need the connective tissue because with 5G you're now talking about a ubiquitous RF cloud, powering autonomous vehicles. We're seeing the Brain Project here, ET Brain, the City Brain-- >> Yes. >> Which is essentially IoT and big data being a big application that they're showcasing. What's the connective tissue? How does that work, from the data center, to the Edge? What's Intel's position? How do you see it? And what's going to unfold in front of our eyes? >> Yeah, so two things, so number one, I believe that the data center is boundary-less. It's not based on four physical walls. It's a connected link between the data center, and all the Edge devices that you called IoT. In order to fulfill this, you have to have 5G technology. We're invested in Silicon, in radio technologies, as well as in driving the 5G industry in consortia, to be able to bring 5G solutions to market. We think that 5G, as well as a tiered architecture between the Edge to the center, where you do some processing at the Edge, the radio stations, some in intermediate data centers, and then some in the back end Cloud data center, is what's going to be essential, and Intel has significant investments, both in developing this distributed hierarchical architecture, as well as in 5G. >> That's a great point. I want to just unpack that, and double-click on it a little bit, because you mentioned data at the Edge, and you also said earlier, low latency. Okay, a lot of people have been talking about, it costs you speed and time to move data around. So there's no real one general architecturing, where you have to kind of decide the architecture for the use case. >> Yes. >> So, the beauty is in the eye of the beholder, whoever has the workloads or the equipment. >> Yes. >> How do you look at that, because now you're thinking about, if I don't want to move data around, maybe you shouldn't, maybe you want to move data around. How does that fit with the Cloud of model, because we're seeing Cloud being a great use case for IoT in one instance, and maybe not in another. How do you think about that? How should practitioners think about the data architecture? >> Yeah, so our vision is that the Cloud changes from a centralized Cloud, to a distributed Cloud, and is amorphoused between the Edge where the IoT devices are, and the backend, and the way to think about it perhaps, is to say that storage as people have envisioned it, as being centralized, that paradigm has to change, and storage has to become distributed, such that data is available at different points in the network, and my vision is that you don't want to move data around, you want to minimize data movement for most use cases, and you want to have it pre-positioned on the 5G network, and you want to move the compute to the data, that's more energy-efficient. >> So I got to ask you, as someone who's doing the path-finding, which is the future path for Intel, and innovation and architecture. I was talking with some practitioners recently at another event, and trying to find someone, because I don't speak Chinese very well. But they asked me the same question. It matters what's in my Cloud. And what they mean by their Cloud, either on-premise private Cloud that they're putting together, operating model of their business, now going Cloud-like. But also as they pick their Cloud provider, they want to have multi-Cloud, and so what's in their Cloud, and their Cloud provider's matters. You guys are the inside of the Cloud across many spectrums, Intel. >> Yes. >> How should a customer think about that question? What's in my Cloud? Why should it matter, and it should matter. What's your take on that, and what should they look for? >> Yeah, so my take is that for years we've had the debate of whether it's public Cloud, or private Cloud, or on-prem Cloud. Our view is that the world is Hybrid, which is why we are big supporters of Alibaba, and the Hybrid Cloud movement, and as such, if it's Hybrid, it sort of suggests that the end state is that there'll be about an equal amount of applications that run on public versus private, and so I think the number of applications have an affinity to move into the public Cloud, like mail, and then there's other applications that you might care more about the compliance and security that you would say have an affinity to being on-prem. >> Also you mentioned that there's no walls, it's boundary-less in the data center. Okay, there's no door, there's no mote, you can't put a firewall on that door, unlimited access surface area for security. Obviously security hacks are big. We found out today that Israel had hacked, and notified the NSA. Hacking is a huge problem. Equifax is going to be another one. How should customers protect themselves? >> It's a very fair question John. This is one of the side-effects of saying that the data center will be boundary-less. We now have to have security technologies that can, we've effectively expanded the attacks of security in a significant way, but I don't think the answer is to say we need to move backwards and not adopt this boundary-less Cloud. I think we want to adopt it, and we want to develop technologies. So at Intel, we are developing multiple isolation technologies that allow different VM and container tenants to be isolated from other tenants. >> And this was your point earlier, making the device more intelligent, whether that's more on-board memory, and more chips. >> Yes. >> That's what you were kind of referring to, is that right? >> That's correct. >> Okay great, so I want to get one kind of off-the-wall question, since I have you on here. It's just a brain trust here from Intel, which it's great to have him here. Distributed computing has been around for awhile, we know all about that. Network effects, distributed computing, the computer industry. But now we're seeing a trend with decentralization. Blockchain is one shining example. Russia just banned cryptocurrency. This poses a architectural challenge. What's your thoughts on the decentralization, and distributed architectures that are emerging? Opportunity is scary. How should customers think about decentralization? >> Well certainly there's a security challenge, as we just spoke, related to this. But I think the computer industry has oscillated, depending on the era and the needs between centralized and decentralized a number of times now. And we're going through an era where decentralization makes sense, because we expect 30 to 50 billion devices at the Edge, and so you can't handle that with a centralized model, primarily due to three reasons, number one, just moving that volume of data would be very expensive to do over the network. Second there'll be a number of applications that are latency-sensitive. And third, you might care about data federation, and crossing country boundaries in a number of cases. So I think for the use case that we have with IoT, we have to adopt decentralized and distributed. >> So, if The Brain is processing and data, and you've got plenty of it at Intel with more compute power, what's the central nervous system, the metadata? >> Well, actually look at the central nervous system as the 5G distributed network that enables the end-points, or the nerve endings if you will, to be connected to the spinal cord. >> Okay so a final question for you, I really appreciate you spending the time. >> Sure, it's been a pleasure. >> Intel's been a wave company in its generation, and obviously Moore's law, it's not well documented. It seems that Moore's law is every year some journalist claims Moore's law is dead, and that it never goes away, so we expect more and more innovation coming from Intel. You guys have surfed many waves. In your opinion, what waves are coming? Because it feels like the waves are big now, but a lot of people think that there's bigger waves coming. That the big wave set is coming in. What's the technology wave that you're looking at from a path-finding, innovation standpoint, that customers should look for, maybe prepare for. It could be further out coming in. What's the big wave coming in, obviously AI was seeing these things. What's your focus on that? >> So, a number of them. I think, you know distributed computing is not a solved problem yet. But certainly it needs to be solved to be able to address these end-point challenges. Another great example I think, is around visual computing. So in the past, most of the type of data that people handled, was textual. But that's moving to visual very rapidly, and there's so many examples. You brought up the City Brain Project as an example. But video and analyzing images, requires a different kind of art. Different compression techniques. If a human doesn't need to see it, you perhaps don't have to have as high a resolution, and so there's a number of ships in the assumption space. And so I think for me, visual computing is a great opportunity, as well as a wave, that's coming at us. >> And the software too. So the final question, final, final question. Alibaba here, are connecting the dots. You can see where it's going. How do you see the Cloud service provider opportunity, because obviously they're a Cloud service provider on paper, but they're big, they're a Native Cloud now, like with the big guys like Amazon, Google, Microsoft. But we're seeing an emergence of new class of Cloud service provider. Certainly our research is showing that what was a very thin neck in the power laws, now expanding into a much bigger range, where VARs and value-edited software developers are going to start doing their own Cloud-like solutions with the Native Clouds, because they need horizontally scalable data infrastructure, connective tissue, and Edge devices from Intel, but they're going to provide software expertise that's vertically specialized, whether it's traffic, IoT, or oil and gas, or financial, Fintech. The specialism of application developers combined with horizontally scalable Cloud, it seems like a renaissance in the Cloud service provider market. Do you see that as well, and how should the industry think about this potential renaissance? >> So I think there's two possibilities. One is for the vast majority of functions that people run in the public Cloud, I think one possibility is that there's a consolidation amongst a few players. But I think your point's a very good one. That they are specialized services that companies are able to provide, where they're able to carve out a niche, and become a Cloud provider for that particular set of functions, as well as there's a second reason that motivates regional Cloud providers to succeed, again, because of data federation requirements, as well as local proximal, proximity to the end-points. I think these two phenomena are likely to drive the emergence of regional Clouds, as well as specialized Clouds, like you described to perform certain functions. >> And potentially a new kind of ecosystem development. >> Yes. >> And this is, then you guys are all about ecosystems, so is Alibaba. >> That's right. >> Dhiraj, thanks so much for coming on theCUBE, this is exclusive CUBE coverage with SiliconANGLE, and Wikibon here in China with Intel's booth here. Talking about AI, and the future of the data center and Cloud. I'm John Furrier, thanks for watching.