>> Narrator: Live from Washington D.C. it's the CUBE covering the AWS Public Sector Summit 2017. Brought to you by the Amazon web services and its partner Ecosystem. >> Melvin Greer is with us now he's the director of Data Science and Analytics at Intel. Now Melvin, thank you for being here with us on the CUBE. Good to see you here this morning. >> Thank you John and John I appreciate getting a chance to talk with you it's great to be here at the AWS Public Sector Summit. >> Yeah we make it easy for you. >> I never forget the names. >> John and John. Let's talk just about data science in general and analytics I mean tell us about, give us the broad definition of that. You know the elevator speech about what's being done and then we'll drill down a little bit deeper about Intel and what you're doing with in terms of government work and healthcare work. >> Sure well data science and analytics covers a number of key areas and it's really important to consider the granularity of each of these key areas. Primarily because there's so much confusion about what people think of as artificial intelligence. It's certainly got a number of facets associated with it. So we have core analytics like descriptive, diagnostic, predictive and prescriptive. This describes what happened, what's going to happen next, why is it happening and what should I do about it. So those are core analytics. >> And (mumbles) oh go ahead. >> And a different tech we have machine learning cognitive computing. These things are different than core analytics in that they are recognizing patterns and relying on the concepts of training algorithms and then inference. The use of these trained algorithms to infer new knowledge. And then we have things like deep learning and convolutional neuro networks which use convolutional layers to drive better and better granularity and understanding of data. They often typically don't rely on training and have a large focus area around deep learning and deep cognitive skills. And then all of those actually line up in this discussion around narrow artificial intelligence and you've seen a lot of that already haven't you john? You've seen where we teach a machine how to play poker or we teach a machine how to play Jeopardy or Go. These are narrow AI applications. When we think about general AI however, this is much different. This is when we're actually outsourcing human cognition to a thinking machine at internet speed. >> This is amazing I love this conversation cause couple things, in that thread you just brought up is poker which is great cause it's not just Jeopardy it's poker is unknown conditions. You don't know the personality of the other guy. You don't know their cards their dealing with so it's a lot like unstructured data and you have to think about that so but it really highlights the (mumbles) between super computing paradigm and data and that really kind of changes the game on data science cause the old data warehouse model storing information, pulling it back, latency, and so we're seeing machine learning in these new aps really disrupting old data analytics models. So, I want to get your thoughts on this because and what is Intel doing because you guys have restructured things a bit differently. The AI messages out there as this new revolution takes place with data, how are you guys handling that? >> So Intel formed in late 2016 its artificial intelligence product group and the formation of this group is extremely consistent with our pivot to becoming a data company. So we're certainly not going to be abandoning any of that great performance and strong capabilities that we have in silicon architectures but as a data company it means that now we're going to be using all of these assets in artificial intelligence, machine learning cognitive computing and Intel in fact by using this is really in a unique position to focus on what we have termed and what you'll hear our CEO talk about as the virtuous cycle of growth. This cycle of growth includes cloud computing, data center, and IOT. And our ability to harness the power of artificial intelligence in data science and analytics means that Intel is really capable of driving this discussion around cloud computing and powering the cloud and also driving the work that's required to make a smart and a connected world a reality. Our artificial intelligence product group expands our portfolio and it means that we're bringing all these capabilities that I talked to you that make up data science and analytics. Cognitive, machine learning, artificial intelligence, deep learning, convolutional neuro networks, to bare to solve some of the nation's most significant and important problems and it means that Intel with its partners are really focused on the utilization of our core capabilities to drive government missions. >> Well give us an example then in terms of federal government NAI. How you're applying that to the operation of what's going on in this giant bureaucracy of a town that we have. >> So one of the things that I'm most excited about it that there's really no agency almost every federal agency in the U.S. is doing an investigation of artificial intelligence. It started off with this discussion around business intelligence and as you said data warehousing and other things but clearly the government has come to realize that turning data into a strategic asset is important, very very important. And so there are a number of key domain spaces in the federal government where Intel has made a significant impact. One is in health and life sciences so when you think about health and life sciences and biometrics, genomics, using advanced analytics for phenotype and genotype analysis this is where Intel's strengths are in performance in the ability to deliver. We created a collaborative cancer cloud that allows researches to use Intel hardware and software to accelerate the learnings from all of these health and life sciences advances that they want. Sharing data without compromising that data. We're focused significantly on cyber intelligence where we're applying threat and vulnerability analytics to understanding how to identify real cyber problems and big cyber vulnerabilities. We are now able to use Intel products to encrypt from the bios all the way up through the application stack and what it means is, is that our government clients who typically are hyper sensitive around security, get a chance to have data follow their respective process and meet their mission in a safe and secure way. >> If I can drill down on that for a second cause this is kind of a really sweet area for innovation. Data is now the new development environment the new development >> You said Bacon is the Oil is the new bacon (laughing) >> Versus the gold nuggets so I was talking with >> You hear what he said? >> No. >> It's the new bacon. >> The new bacon (laughs) love that. >> Data's the new bacon. >> Everyone loves bacon, everyone loves data. There's a thirst for the data and this also applies is that I ask you the role of the CDO, the chief data officer is emerging in companies and so we're seeing that also at the federal level. I want to get your thoughts on that but to quote the professor from Carnegie Mellon who I interviewed last week said the problem with a lot of data problems its like looking for a needle in the haystack with there's so much data now you have a haystack of needles so his premise is you can't find everything you got to use machine learning and AI to help with that so this is also going to be an issue for this chief data officer a new role. So is there a chief data officer role is there a need for that is there a CCO? Who handles the data? (laughing) >> Yeah so this is >> it's a tough one cause there's a lot a tech involved but also there's policies. >> Yeah so the federal government has actually mandated that each agency assign a federal chief data officer at the agency level and this person is working very closely with the chief information officer and the agency leaders to insure that they have the ability to take advantage of this large set of data that they collect. Intel's been working with most of the folks in the federal data cabinet who are the CDO's who are working to solve this problem around data and analysis of data. We're excited about the fact that we have chief data officers as an entry point to help discuss this hyper convergence that you described in technology. Where we have large data sets, we have faster hardware, of course Intel's helping to provide much of that and then better mathematics and algorithms. When we converge these three things together it's the soup that makes it possible for us to continue to drive artificial intelligence but that not withstanding federal data officers have a really hard job and we've been engaging them at many levels. We just had our artificial intelligence day in government where we had folks from many federal agencies that are on that cabinet and they shared with us directly how important it is to get Intel's on both hardware, hardware performance but also on software. When we think about artificial intelligence and the chief data officer or the data scientist this is likely a different individual than the person that is buying our silicon architectures. This is a person who is focused primarily on an agency mission and is looking for Intel to provide hardware and software capabilities that drive that mission. >> I got to ask you from an Intel perspective you guys are doing a lot of innovative things you have a great R and D group but also silicon you mentioned is important and you know software is eating the world but data's eating software so what's next what's eating data? We believe it's memory and silica and so one of the trends in big data is real time analytics is moving closer and closer to memory and then and now silicon who have some of those security paradigms with data involved seeing silicon implementations, root security, malware, firmware, kind of innovations. This is an interesting trend cause if software gets on to the silicon to the level that is better security you have fingerprinting all kinds of technologies. How is that going to impact the analytics world? So if you believe that they want faster lower latency data it's going to end up in the silicon. >> John you described exactly why Intel is focused on the virtuous cycle of growth. Because as more cloud enabled data moves itself from the cloud through our 5g networks and out to the edge in IOT devices whether they be autonomous vehicles or drones this is exactly why we have this continuum that allows data to move seamlessly between these three areas and operationalizes the core missions of government as well as provides a unique experience that most people can't even imagine. You likely saw the NBA finals you talked about Kevin Durant and you saw there the Intel 360 demonstration >> Love that! >> Where you're able to see how through different camera angles the entire play is unfolding. That is a prime example of how we use back end cloud hyper connected hardware with networks and edge devices where we're pushing analytics closer and closer to the edge >> by the way that's a real life media example of an IOT situation where it's at the edge of the network AKA stadium. I mean we geek out on that as well as Amazon has the MLB thing Andy (mumbles) knows I love that because it's like we're both baseball fans. >> We're excited about it too we think that along with autonomous vehicles, we think that this whole concept of experiences rather than capabilities and technologies >> but most people don't know that that example of basketball takes massive amounts of compute I mean to make that work at that level. >> In real time. >> This is the CG environment we're seeing with gaming culture the people are expecting an interface that looks more like Call of Duty (laughing) or Minecraft than they are Windows desktop machines what we're used to. We think that's great. >> That's why we say we're building the future John. (men laughing) >> You touched on something you said a little bit ago. A data officer of the federal government has got a tough job, a big job. >> Yes. >> What's the difference between private and public sector somebody who is handling the same kinds of responsibilities but has different compliance pressures different enforcement pressures and those kinds of things so somebody in the public space, what are they facing that somebody on the other side of the fence is not? >> All data officers have a tough job whether it's about cleansing data, being able to ingest it. What we talk about, and you described this, a haystack of needles is the need and ability to create a hyper relevancy to data because hyper relevancy is what makes it possible for personalized medicine and precision medicine. That's what makes it possible for us to do hyper scale personalized retail. This is what makes it possible to drive new innovation is this hyper relevancy and so whether you're working in a highly regulated environment like energy or financial services or whether you're working in the federal government with the department of defense and intelligence agencies or deep space exploration like at NASA you're still solving many data problems that are in common. Of course there are some differences right when you work for the federal government you're a steward of citizen's data that adds a different level of responsibility. There's a legal framework that guides how that data's handled as opposed to just a regulatory and legal one but when it comes to artificial intelligence all of us as practitioners are really focusing on the legal, ethical, and societal implications associate with the implementation of these advanced technologies. >> Quick question end this segment I know we're a little running over time but I wanted to get this last point in and this is something that we've talked on the CUBE a lot me and Dave have been debating because data is very organic innovation. You don't know what your going to do until you get into it, alchemy if you will, but trust and security and policy is a top down slow down mentality so often in the past it's been restricting growth so the balance here that you're getting at is how do you provide the speed and agility of real time experiences while maintaining all the trust and secure requirements that have slowed things down. >> You mention a topic there John and in my last book, 21st Century Leadership I actually described this concept as ambidextrous leadership. This concept of being able to do operational excellence extremely well and focus on delivery of core mission and at the same time be in a position to drive innovation and look forward enough to think about how, not where you are today but where you will be going in the future. This ambidexterity is really a critical factor when we talk about all leadership today, not just leaders in government or people who just work mostly on artificial intelligence. >> It's multidimensional, multi disciplined too right I mean. >> That's right, that's right. >> That's the dev opps ethos, that's the cloud. Move fast, I mean Mark Zuckerberg had the best quote with Facebook, "move fast and break stuff" up until that time he had about a billion users and then changed to move fast and be secure and reliable. (laughing) >> Yeah and don't break anything >> Well he understood you can't just break stuff at some point you got to move fast and be reliable. >> One of five books I want to mention by the way. >> That's right I'm working on my sixth and seventh now but yeah. >> And also the managing of the Greer Institute of Leadership and Management so you've written now almost seven books, you're running this leadership, you're working with Intel what do you do in your spare time Melvin? >> My wife is the chef and >> He eats a lot. (laughing) >> And so I get a chance to chance to enjoy all of the great food she cooks and I have two young sons and they keep me very very busy believe me. >> I think you're busy enough (laughing). Thanks for being on the CUBE. >> I very much appreciate it. >> It's good to have you >> Thank you. >> With us here at the AWS Public Sector Summit back with more coverage live with here on the Cube, Washington D.C. right after this.