>> Welcome to the Intel AI Lounge. Today, we're very excited to share with you the Precision Medicine panel discussion. I'll be moderating the session. My name is Kay Erin. I'm the general manager of Health and Life Sciences at Intel. And I'm excited to share with you these three panelists that we have here. First is John Madison. He is a chief information medical officer and he is part of Kaiser Permanente. We're very excited to have you here. Thank you, John. >> Thank you. >> We also have Naveen Rao. He is the VP and general manager for the Artificial Intelligence Solutions at Intel. He's also the former CEO of Nervana, which was acquired by Intel. And we also have Bob Rogers, who's the chief data scientist at our AI solutions group. So, why don't we get started with our questions. I'm going to ask each of the panelists to talk, introduce themselves, as well as talk about how they got started with AI. So why don't we start with John? >> Sure, so can you hear me okay in the back? Can you hear? Okay, cool. So, I am a recovering evolutionary biologist and a recovering physician and a recovering geek. And I implemented the health record system for the first and largest region of Kaiser Permanente. And it's pretty obvious that most of the useful data in a health record, in lies in free text. So I started up a natural language processing team to be able to mine free text about a dozen years ago. So we can do things with that that you can't otherwise get out of health information. I'll give you an example. I read an article online from the New England Journal of Medicine about four years ago that said over half of all people who have had their spleen taken out were not properly vaccinated for a common form of pneumonia, and when your spleen's missing, you must have that vaccine or you die a very sudden death with sepsis. In fact, our medical director in Northern California's father died of that exact same scenario. So, when I read the article, I went to my structured data analytics team and to my natural language processing team and said please show me everybody who has had their spleen taken out and hasn't been appropriately vaccinated and we ran through about 20 million records in about three hours with the NLP team, and it took about three weeks with a structured data analytics team. That sounds counterintuitive but it actually happened that way. And it's not a competition for time only. It's a competition for quality and sensitivity and specificity. So we were able to indentify all of our members who had their spleen taken out, who should've had a pneumococcal vaccine. We vaccinated them and there are a number of people alive today who otherwise would've died absent that capability. So people don't really commonly associate natural language processing with machine learning, but in fact, natural language processing relies heavily and is the first really, highly successful example of machine learning. So we've done dozens of similar projects, mining free text data in millions of records very efficiently, very effectively. But it really helped advance the quality of care and reduce the cost of care. It's a natural step forward to go into the world of personalized medicine with the arrival of a 100-dollar genome, which is actually what it costs today to do a full genome sequence. Microbiomics, that is the ecosystem of bacteria that are in every organ of the body actually. And we know now that there is a profound influence of what's in our gut and how we metabolize drugs, what diseases we get. You can tell in a five year old, whether or not they were born by a vaginal delivery or a C-section delivery by virtue of the bacteria in the gut five years later. So if you look at the complexity of the data that exists in the genome, in the microbiome, in the health record with free text and you look at all the other sources of data like this streaming data from my wearable monitor that I'm part of a research study on Precision Medicine out of Stanford, there is a vast amount of disparate data, not to mention all the imaging, that really can collectively produce much more useful information to advance our understanding of science, and to advance our understanding of every individual. And then we can do the mash up of a much broader range of science in health care with a much deeper sense of data from an individual and to do that with structured questions and structured data is very yesterday. The only way we're going to be able to disambiguate those data and be able to operate on those data in concert and generate real useful answers from the broad array of data types and the massive quantity of data, is to let loose machine learning on all of those data substrates. So my team is moving down that pathway and we're very excited about the future prospects for doing that. >> Yeah, great. I think that's actually some of the things I'm very excited about in the future with some of the technologies we're developing. My background, I started actually being fascinated with computation in biological forms when I was nine. Reading and watching sci-fi, I was kind of a big dork which I pretty much still am. I haven't really changed a whole lot. Just basically seeing that machines really aren't all that different from biological entities, right? We are biological machines and kind of understanding how a computer works and how we engineer those things and trying to pull together concepts that learn from biology into that has always been a fascination of mine. As an undergrad, I was in the EE, CS world. Even then, I did some research projects around that. I worked in the industry for about 10 years designing chips, microprocessors, various kinds of ASICs, and then actually went back to school, quit my job, got a Ph.D. in neuroscience, computational neuroscience, to specifically understand what's the state of the art. What do we really understand about the brain? And are there concepts that we can take and bring back? Inspiration's always been we want to... We watch birds fly around. We want to figure out how to make something that flies. We extract those principles, and then build a plane. Don't necessarily want to build a bird. And so Nervana's really was the combination of all those experiences, bringing it together. Trying to push computation in a new a direction. Now, as part of Intel, we can really add a lot of fuel to that fire. I'm super excited to be part of Intel in that the technologies that we were developing can really proliferate and be applied to health care, can be applied to Internet, can be applied to every facet of our lives. And some of the examples that John mentioned are extremely exciting right now and these are things we can do today. And the generality of these solutions are just really going to hit every part of health care. I mean from a personal viewpoint, my whole family are MDs. I'm sort of the black sheep of the family. I don't have an MD. And it's always been kind of funny to me that knowledge is concentrated in a few individuals. Like you have a rare tumor or something like that, you need the guy who knows how to read this MRI. Why? Why is it like that? Can't we encapsulate that knowledge into a computer or into an algorithm, and democratize it. And the reason we couldn't do it is we just didn't know how. And now we're really getting to a point where we know how to do that. And so I want that capability to go to everybody. It'll bring the cost of healthcare down. It'll make all of us healthier. That affects everything about our society. So that's really what's exciting about it to me. >> That's great. So, as you heard, I'm Bob Rogers. I'm chief data scientist for analytics and artificial intelligence solutions at Intel. My mission is to put powerful analytics in the hands of every decision maker and when I think about Precision Medicine, decision makers are not just doctors and surgeons and nurses, but they're also case managers and care coordinators and probably most of all, patients. So the mission is really to put powerful analytics and AI capabilities in the hands of everyone in health care. It's a very complex world and we need tools to help us navigate it. So my background, I started with a Ph.D. in physics and I was computer modeling stuff, falling into super massive black holes. And there's a lot of applications for that in the real world. No, I'm kidding. (laughter) >> John: There will be, I'm sure. Yeah, one of these days. Soon as we have time travel. Okay so, I actually, about 1991, I was working on my post doctoral research, and I heard about neural networks, these things that could compute the way the brain computes. And so, I started doing some research on that. I wrote some papers and actually, it was an interesting story. The problem that we solved that got me really excited about neural networks, which have become deep learning, my office mate would come in. He was this young guy who was about to go off to grad school. He'd come in every morning. "I hate my project." Finally, after two weeks, what's your project? What's the problem? It turns out he had to circle these little fuzzy spots on these images from a telescope. So they were looking for the interesting things in a sky survey, and he had to circle them and write down their coordinates all summer. Anyone want to volunteer to do that? No? Yeah, he was very unhappy. So we took the first two weeks of data that he created doing his work by hand, and we trained an artificial neural network to do his summer project and finished it in about eight hours of computing. (crowd laughs) And so he was like yeah, this is amazing. I'm so happy. And we wrote a paper. I was the first author of course, because I was the senior guy at age 24. And he was second author. His first paper ever. He was very, very excited. So we have to fast forward about 20 years. His name popped up on the Internet. And so it caught my attention. He had just won the Nobel Prize in physics. (laughter) So that's where artificial intelligence will get you. (laughter) So thanks Naveen. Fast forwarding, I also developed some time series forecasting capabilities that allowed me to create a hedge fund that I ran for 12 years. After that, I got into health care, which really is the center of my passion. Applying health care to figuring out how to get all the data from all those siloed sources, put it into the cloud in a secure way, and analyze it so you can actually understand those cases that John was just talking about. How do you know that that person had had a splenectomy and that they needed to get that pneumovax? You need to be able to search all the data, so we used AI, natural language processing, machine learning, to do that and then two years ago, I was lucky enough to join Intel and, in the intervening time, people like Naveen actually thawed the AI winter and we're really in a spring of amazing opportunities with AI, not just in health care but everywhere, but of course, the health care applications are incredibly life saving and empowering so, excited to be here on this stage with you guys. >> I just want to cue off of your comment about the role of physics in AI and health care. So the field of microbiomics that I referred to earlier, bacteria in our gut. There's more bacteria in our gut than there are cells in our body. There's 100 times more DNA in that bacteria than there is in the human genome. And we're now discovering a couple hundred species of bacteria a year that have never been identified under a microscope just by their DNA. So it turns out the person who really catapulted the study and the science of microbiomics forward was an astrophysicist who did his Ph.D. in Steven Hawking's lab on the collision of black holes and then subsequently, put the other team in a virtual reality, and he developed the first super computing center and so how did he get an interest in microbiomics? He has the capacity to do high performance computing and the kind of advanced analytics that are required to look at a 100 times the volume of 3.2 billion base pairs of the human genome that are represented in the bacteria in our gut, and that has unleashed the whole science of microbiomics, which is going to really turn a lot of our assumptions of health and health care upside down. >> That's great, I mean, that's really transformational. So a lot of data. So I just wanted to let the audience know that we want to make this an interactive session, so I'll be asking for questions in a little bit, but I will start off with one question so that you can think about it. So I wanted to ask you, it looks like you've been thinking a lot about AI over the years. And I wanted to understand, even though AI's just really starting in health care, what are some of the new trends or the changes that you've seen in the last few years that'll impact how AI's being used going forward? >> So I'll start off. There was a paper published by a guy by the name of Tegmark at Harvard last summer that, for the first time, explained why neural networks are efficient beyond any mathematical model we predict. And the title of the paper's fun. It's called Deep Learning Versus Cheap Learning. So there were two sort of punchlines of the paper. One is is that the reason that mathematics doesn't explain the efficiency of neural networks is because there's a higher order of mathematics called physics. And the physics of the underlying data structures determined how efficient you could mine those data using machine learning tools. Much more so than any mathematical modeling. And so the second thing that was a reel from that paper is that the substrate of the data that you're operating on and the natural physics of those data have inherent levels of complexity that determine whether or not a 12th layer of neural net will get you where you want to go really fast, because when you do the modeling, for those math geeks in the audience, a factorial. So if there's 12 layers, there's 12 factorial permutations of different ways you could sequence the learning through those data. When you have 140 layers of a neural net, it's a much, much, much bigger number of permutations and so you end up being hardware-bound. And so, what Max Tegmark basically said is you can determine whether to do deep learning or cheap learning based upon the underlying physics of the data substrates you're operating on and have a good insight into how to optimize your hardware and software approach to that problem. >> So another way to put that is that neural networks represent the world in the way the world is sort of built. >> Exactly. >> It's kind of hierarchical. It's funny because, sort of in retrospect, like oh yeah, that kind of makes sense. But when you're thinking about it mathematically, we're like well, anything... The way a neural can represent any mathematical function, therfore, it's fully general. And that's the way we used to look at it, right? So now we're saying, well actually decomposing the world into different types of features that are layered upon each other is actually a much more efficient, compact representation of the world, right? I think this is actually, precisely the point of kind of what you're getting at. What's really exciting now is that what we were doing before was sort of building these bespoke solutions for different kinds of data. NLP, natural language processing. There's a whole field, 25 plus years of people devoted to figuring out features, figuring out what structures make sense in this particular context. Those didn't carry over at all to computer vision. Didn't carry over at all to time series analysis. Now, with neural networks, we've seen it at Nervana, and now part of Intel, solving customers' problems. We apply a very similar set of techniques across all these different types of data domains and solve them. All data in the real world seems to be hierarchical. You can decompose it into this hierarchy. And it works really well. Our brains are actually general structures. As a neuroscientist, you can look at different parts of your brain and there are differences. Something that takes in visual information, versus auditory information is slightly different but they're much more similar than they are different. So there is something invariant, something very common between all of these different modalities and we're starting to learn that. And this is extremely exciting to me trying to understand the biological machine that is a computer, right? We're figurig it out, right? >> One of the really fun things that Ray Chrisfall likes to talk about is, and it falls in the genre of biomimmicry, and how we actually replicate biologic evolution in our technical solutions so if you look at, and we're beginning to understand more and more how real neural nets work in our cerebral cortex. And it's sort of a pyramid structure so that the first pass of a broad base of analytics, it gets constrained to the next pass, gets constrained to the next pass, which is how information is processed in the brain. So we're discovering increasingly that what we've been evolving towards, in term of architectures of neural nets, is approximating the architecture of the human cortex and the more we understand the human cortex, the more insight we get to how to optimize neural nets, so when you think about it, with millions of years of evolution of how the cortex is structured, it shouldn't be a surprise that the optimization protocols, if you will, in our genetic code are profoundly efficient in how they operate. So there's a real role for looking at biologic evolutionary solutions, vis a vis technical solutions, and there's a friend of mine who worked with who worked with George Church at Harvard and actually published a book on biomimmicry and they wrote the book completely in DNA so if all of you have your home DNA decoder, you can actually read the book on your DNA reader, just kidding. >> There's actually a start up I just saw in the-- >> Read-Write DNA, yeah. >> Actually it's a... He writes something. What was it? (response from crowd member) Yeah, they're basically encoding information in DNA as a storage medium. (laughter) The company, right? >> Yeah, that same friend of mine who coauthored that biomimmicry book in DNA also did the estimate of the density of information storage. So a cubic centimeter of DNA can store an hexabyte of data. I mean that's mind blowing. >> Naveen: Highly done soon. >> Yeah that's amazing. Also you hit upon a really important point there, that one of the things that's changed is... Well, there are two major things that have changed in my perception from let's say five to 10 years ago, when we were using machine learning. You could use data to train models and make predictions to understand complex phenomena. But they had limited utility and the challenge was that if I'm trying to build on these things, I had to do a lot of work up front. It was called feature engineering. I had to do a lot of work to figure out what are the key attributes of that data? What are the 10 or 20 or 100 pieces of information that I should pull out of the data to feed to the model, and then the model can turn it into a predictive machine. And so, what's really exciting about the new generation of machine learning technology, and particularly deep learning, is that it can actually learn from example data those features without you having to do any preprogramming. That's why Naveen is saying you can take the same sort of overall approach and apply it to a bunch of different problems. Because you're not having to fine tune those features. So at the end of the day, the two things that have changed to really enable this evolution is access to more data, and I'd be curious to hear from you where you're seeing data come from, what are the strategies around that. So access to data, and I'm talking millions of examples. So 10,000 examples most times isn't going to cut it. But millions of examples will do it. And then, the other piece is the computing capability to actually take millions of examples and optimize this algorithm in a single lifetime. I mean, back in '91, when I started, we literally would have thousands of examples and it would take overnight to run the thing. So now in the world of millions, and you're putting together all of these combinations, the computing has changed a lot. I know you've made some revolutionary advances in that. But I'm curious about the data. Where are you seeing interesting sources of data for analytics? >> So I do some work in the genomics space and there are more viable permutations of the human genome than there are people who have ever walked the face of the earth. And the polygenic determination of a phenotypic expression translation, what are genome does to us in our physical experience in health and disease is determined by many, many genes and the interaction of many, many genes and how they are up and down regulated. And the complexity of disambiguating which 27 genes are affecting your diabetes and how are they up and down regulated by different interventions is going to be different than his. It's going to be different than his. And we already know that there's four or five distinct genetic subtypes of type II diabetes. So physicians still think there's one disease called type II diabetes. There's actually at least four or five genetic variants that have been identified. And so, when you start thinking about disambiguating, particularly when we don't know what 95 percent of DNA does still, what actually is the underlining cause, it will require this massive capability of developing these feature vectors, sometimes intuiting it, if you will, from the data itself. And other times, taking what's known knowledge to develop some of those feature vectors, and be able to really understand the interaction of the genome and the microbiome and the phenotypic data. So the complexity is high and because the variation complexity is high, you do need these massive members. Now I'm going to make a very personal pitch here. So forgive me, but if any of you have any role in policy at all, let me tell you what's happening right now. The Genomic Information Nondiscrimination Act, so called GINA, written by a friend of mine, passed a number of years ago, says that no one can be discriminated against for health insurance based upon their genomic information. That's cool. That should allow all of you to feel comfortable donating your DNA to science right? Wrong. You are 100% unprotected from discrimination for life insurance, long term care and disability. And it's being practiced legally today and there's legislation in the House, in mark up right now to completely undermine the existing GINA legislation and say that whenever there's another applicable statute like HIPAA, that the GINA is irrelevant, that none of the fines and penalties are applicable at all. So we need a ton of data to be able to operate on. We will not be getting a ton of data to operate on until we have the kind of protection we need to tell people, you can trust us. You can give us your data, you will not be subject to discrimination. And that is not the case today. And it's being further undermined. So I want to make a plea to any of you that have any policy influence to go after that because we need this data to help the understanding of human health and disease and we're not going to get it when people look behind the curtain and see that discrimination is occurring today based upon genetic information. >> Well, I don't like the idea of being discriminated against based on my DNA. Especially given how little we actually know. There's so much complexity in how these things unfold in our own bodies, that I think anything that's being done is probably childishly immature and oversimplifying. So it's pretty rough. >> I guess the translation here is that we're all unique. It's not just a Disney movie. (laughter) We really are. And I think one of the strengths that I'm seeing, kind of going back to the original point, of these new techniques is it's going across different data types. It will actually allow us to learn more about the uniqueness of the individual. It's not going to be just from one data source. They were collecting data from many different modalities. We're collecting behavioral data from wearables. We're collecting things from scans, from blood tests, from genome, from many different sources. The ability to integrate those into a unified picture, that's the important thing that we're getting toward now. That's what I think is going to be super exciting here. Think about it, right. I can tell you to visual a coin, right? You can visualize a coin. Not only do you visualize it. You also know what it feels like. You know how heavy it is. You have a mental model of that from many different perspectives. And if I take away one of those senses, you can still identify the coin, right? If I tell you to put your hand in your pocket, and pick out a coin, you probably can do that with 100% reliability. And that's because we have this generalized capability to build a model of something in the world. And that's what we need to do for individuals is actually take all these different data sources and come up with a model for an individual and you can actually then say what drug works best on this. What treatment works best on this? It's going to get better with time. It's not going to be perfect, because this is what a doctor does, right? A doctor who's very experienced, you're a practicing physician right? Back me up here. That's what you're doing. You basically have some categories. You're taking information from the patient when you talk with them, and you're building a mental model. And you apply what you know can work on that patient, right? >> I don't have clinic hours anymore, but I do take care of many friends and family. (laughter) >> You used to, you used to. >> I practiced for many years before I became a full-time geek. >> I thought you were a recovering geek. >> I am. (laughter) I do more policy now. >> He's off the wagon. >> I just want to take a moment and see if there's anyone from the audience who would like to ask, oh. Go ahead. >> We've got a mic here, hang on one second. >> I have tons and tons of questions. (crosstalk) Yes, so first of all, the microbiome and the genome are really complex. You already hit about that. Yet most of the studies we do are small scale and we have difficulty repeating them from study to study. How are we going to reconcile all that and what are some of the technical hurdles to get to the vision that you want? >> So primarily, it's been the cost of sequencing. Up until a year ago, it's $1000, true cost. Now it's $100, true cost. And so that barrier is going to enable fairly pervasive testing. It's not a real competitive market becaue there's one sequencer that is way ahead of everybody else. So the price is not $100 yet. The cost is below $100. So as soon as there's competition to drive the cost down, and hopefully, as soon as we all have the protection we need against discrimination, as I mentioned earlier, then we will have large enough sample sizes. And so, it is our expectation that we will be able to pool data from local sources. I chair the e-health work group at the Global Alliance for Genomics and Health which is working on this very issue. And rather than pooling all the data into a single, common repository, the strategy, and we're developing our five-year plan in a month in London, but the goal is to have a federation of essentially credentialed data enclaves. That's a formal method. HHS already does that so you can get credentialed to search all the data that Medicare has on people that's been deidentified according to HIPPA. So we want to provide the same kind of service with appropriate consent, at an international scale. And there's a lot of nations that are talking very much about data nationality so that you can't export data. So this approach of a federated model to get at data from all the countries is important. The other thing is a block-chain technology is going to be very profoundly useful in this context. So David Haussler of UC Santa Cruz is right now working on a protocol using an open block-chain, public ledger, where you can put out. So for any typical cancer, you may have a half dozen, what are called sematic variance. Cancer is a genetic disease so what has mutated to cause it to behave like a cancer? And if we look at those biologically active sematic variants, publish them on a block chain that's public, so there's not enough data there to reidentify the patient. But if I'm a physician treating a woman with breast cancer, rather than say what's the protocol for treating a 50-year-old woman with this cell type of cancer, I can say show me all the people in the world who have had this cancer at the age of 50, wit these exact six sematic variants. Find the 200 people worldwide with that. Ask them for consent through a secondary mechanism to donate everything about their medical record, pool that information of the core of 200 that exactly resembles the one sitting in front of me, and find out, of the 200 ways they were treated, what got the best results. And so, that's the kind of future where a distributed, federated architecture will allow us to query and obtain a very, very relevant cohort, so we can basically be treating patients like mine, sitting right in front of me. Same thing applies for establishing research cohorts. There's some very exciting stuff at the convergence of big data analytics, machine learning, and block chaining. >> And this is an area that I'm really excited about and I think we're excited about generally at Intel. They actually have something called the Collaborative Cancer Cloud, which is this kind of federated model. We have three different academic research centers. Each of them has a very sizable and valuable collection of genomic data with phenotypic annotations. So you know, pancreatic cancer, colon cancer, et cetera, and we've actually built a secure computing architecture that can allow a person who's given the right permissions by those organizations to ask a specific question of specific data without ever sharing the data. So the idea is my data's really important to me. It's valuable. I want us to be able to do a study that gets the number from the 20 pancreatic cancer patients in my cohort, up to the 80 that we have in the whole group. But I can't do that if I'm going to just spill my data all over the world. And there are HIPAA and compliance reasons for that. There are business reasons for that. So what we've built at Intel is this platform that allows you to do different kinds of queries on this genetic data. And reach out to these different sources without sharing it. And then, the work that I'm really involved in right now and that I'm extremely excited about... This also touches on something that both of you said is it's not sufficient to just get the genome sequences. You also have to have the phenotypic data. You have to know what cancer they've had. You have to know that they've been treated with this drug and they've survived for three months or that they had this side effect. That clinical data also needs to be put together. It's owned by other organizations, right? Other hospitals. So the broader generalization of the Collaborative Cancer Cloud is something we call the data exchange. And it's a misnomer in a sense that we're not actually exchanging data. We're doing analytics on aggregated data sets without sharing it. But it really opens up a world where we can have huge populations and big enough amounts of data to actually train these models and draw the thread in. Of course, that really then hits home for the techniques that Nervana is bringing to the table, and of course-- >> Stanford's one of your academic medical centers? >> Not for that Collaborative Cancer Cloud. >> The reason I mentioned Standford is because the reason I'm wearing this FitBit is because I'm a research subject at Mike Snyder's, the chair of genetics at Stanford, IPOP, intrapersonal omics profile. So I was fully sequenced five years ago and I get four full microbiomes. My gut, my mouth, my nose, my ears. Every three months and I've done that for four years now. And about a pint of blood. And so, to your question of the density of data, so a lot of the problem with applying these techniques to health care data is that it's basically a sparse matrix and there's a lot of discontinuities in what you can find and operate on. So what Mike is doing with the IPOP study is much the same as you described. Creating a highly dense longitudinal set of data that will help us mitigate the sparse matrix problem. (low volume response from audience member) Pardon me. >> What's that? (low volume response) (laughter) >> Right, okay. >> John: Lost the school sample. That's got to be a new one I've heard now. >> Okay, well, thank you so much. That was a great question. So I'm going to repeat this and ask if there's another question. You want to go ahead? >> Hi, thanks. So I'm a journalist and I report a lot on these neural networks, a system that's beter at reading mammograms than your human radiologists. Or a system that's better at predicting which patients in the ICU will get sepsis. These sort of fascinating academic studies that I don't really see being translated very quickly into actual hospitals or clinical practice. Seems like a lot of the problems are regulatory, or liability, or human factors, but how do you get past that and really make this stuff practical? >> I think there's a few things that we can do there and I think the proof points of the technology are really important to start with in this specific space. In other places, sometimes, you can start with other things. But here, there's a real confidence problem when it comes to health care, and for good reason. We have doctors trained for many, many years. School and then residencies and other kinds of training. Because we are really, really conservative with health care. So we need to make sure that technology's well beyond just the paper, right? These papers are proof points. They get people interested. They even fuel entire grant cycles sometimes. And that's what we need to happen. It's just an inherent problem, its' going to take a while. To get those things to a point where it's like well, I really do trust what this is saying. And I really think it's okay to now start integrating that into our standard of care. I think that's where you're seeing it. It's frustrating for all of us, believe me. I mean, like I said, I think personally one of the biggest things, I want to have an impact. Like when I go to my grave, is that we used machine learning to improve health care. We really do feel that way. But it's just not something we can do very quickly and as a business person, I don't actually look at those use cases right away because I know the cycle is just going to be longer. >> So to your point, the FDA, for about four years now, has understood that the process that has been given to them by their board of directors, otherwise known as Congress, is broken. And so they've been very actively seeking new models of regulation and what's really forcing their hand is regulation of devices and software because, in many cases, there are black box aspects of that and there's a black box aspect to machine learning. Historically, Intel and others are making inroads into providing some sort of traceability and transparency into what happens in that black box rather than say, overall we get better results but once in a while we kill somebody. Right? So there is progress being made on that front. And there's a concept that I like to use. Everyone knows Ray Kurzweil's book The Singularity Is Near? Well, I like to think that diadarity is near. And the diadarity is where you have human transparency into what goes on in the black box and so maybe Bob, you want to speak a little bit about... You mentioned that, in a prior discussion, that there's some work going on at Intel there. >> Yeah, absolutely. So we're working with a number of groups to really build tools that allow us... In fact Naveen probably can talk in even more detail than I can, but there are tools that allow us to actually interrogate machine learning and deep learning systems to understand, not only how they respond to a wide variety of situations but also where are there biases? I mean, one of the things that's shocking is that if you look at the clinical studies that our drug safety rules are based on, 50 year old white guys are the peak of that distribution, which I don't see any problem with that, but some of you out there might not like that if you're taking a drug. So yeah, we want to understand what are the biases in the data, right? And so, there's some new technologies. There's actually some very interesting data-generative technologies. And this is something I'm also curious what Naveen has to say about, that you can generate from small sets of observed data, much broader sets of varied data that help probe and fill in your training for some of these systems that are very data dependent. So that takes us to a place where we're going to start to see deep learning systems generating data to train other deep learning systems. And they start to sort of go back and forth and you start to have some very nice ways to, at least, expose the weakness of these underlying technologies. >> And that feeds back to your question about regulatory oversight of this. And there's the fascinating, but little known origin of why very few women are in clinical studies. Thalidomide causes birth defects. So rather than say pregnant women can't be enrolled in drug trials, they said any woman who is at risk of getting pregnant cannot be enrolled. So there was actually a scientific meritorious argument back in the day when they really didn't know what was going to happen post-thalidomide. So it turns out that the adverse, unintended consequence of that decision was we don't have data on women and we know in certain drugs, like Xanax, that the metabolism is so much slower, that the typical dosing of Xanax is women should be less than half of that for men. And a lot of women have had very serious adverse effects by virtue of the fact that they weren't studied. So the point I want to illustrate with that is that regulatory cycles... So people have known for a long time that was like a bad way of doing regulations. It should be changed. It's only recently getting changed in any meaningful way. So regulatory cycles and legislative cycles are incredibly slow. The rate of exponential growth in technology is exponential. And so there's impedance mismatch between the cycle time for regulation cycle time for innovation. And what we need to do... I'm working with the FDA. I've done four workshops with them on this very issue. Is that they recognize that they need to completely revitalize their process. They're very interested in doing it. They're not resisting it. People think, oh, they're bad, the FDA, they're resisting. Trust me, there's nobody on the planet who wants to revise these review processes more than the FDA itself. And so they're looking at models and what I recommended is global cloud sourcing and the FDA could shift from a regulatory role to one of doing two things, assuring the people who do their reviews are competent, and assuring that their conflicts of interest are managed, because if you don't have a conflict of interest in this very interconnected space, you probably don't know enough to be a reviewer. So there has to be a way to manage the conflict of interest and I think those are some of the keypoints that the FDA is wrestling with because there's type one and type two errors. If you underregulate, you end up with another thalidomide and people born without fingers. If you overregulate, you prevent life saving drugs from coming to market. So striking that balance across all these different technologies is extraordinarily difficult. If it were easy, the FDA would've done it four years ago. It's very complicated. >> Jumping on that question, so all three of you are in some ways entrepreneurs, right? Within your organization or started companies. And I think it would be good to talk a little bit about the business opportunity here, where there's a huge ecosystem in health care, different segments, biotech, pharma, insurance payers, etc. Where do you see is the ripe opportunity or industry, ready to really take this on and to make AI the competitive advantage. >> Well, the last question also included why aren't you using the result of the sepsis detection? We do. There were six or seven published ways of doing it. We did our own data, looked at it, we found a way that was superior to all the published methods and we apply that today, so we are actually using that technology to change clinical outcomes. As far as where the opportunities are... So it's interesting. Because if you look at what's going to be here in three years, we're not going to be using those big data analytics models for sepsis that we are deploying today, because we're just going to be getting a tiny aliquot of blood, looking for the DNA or RNA of any potential infection and we won't have to infer that there's a bacterial infection from all these other ancillary, secondary phenomenon. We'll see if the DNA's in the blood. So things are changing so fast that the opportunities that people need to look for are what are generalizable and sustainable kind of wins that are going to lead to a revenue cycle that are justified, a venture capital world investing. So there's a lot of interesting opportunities in the space. But I think some of the biggest opportunities relate to what Bob has talked about in bringing many different disparate data sources together and really looking for things that are not comprehensible in the human brain or in traditional analytic models. >> I think we also got to look a little bit beyond direct care. We're talking about policy and how we set up standards, these kinds of things. That's one area. That's going to drive innovation forward. I completely agree with that. Direct care is one piece. How do we scale out many of the knowledge kinds of things that are embedded into one person's head and get them out to the world, democratize that. Then there's also development. The underlying technology's of medicine, right? Pharmaceuticals. The traditional way that pharmaceuticals is developed is actually kind of funny, right? A lot of it was started just by chance. Penicillin, a very famous story right? It's not that different today unfortunately, right? It's conceptually very similar. Now we've got more science behind it. We talk about domains and interactions, these kinds of things but fundamentally, the problem is what we in computer science called NP hard, it's too difficult to model. You can't solve it analytically. And this is true for all these kinds of natural sorts of problems by the way. And so there's a whole field around this, molecular dynamics and modeling these sorts of things, that are actually being driven forward by these AI techniques. Because it turns out, our brain doesn't do magic. It actually doesn't solve these problems. It approximates them very well. And experience allows you to approximate them better and better. Actually, it goes a little bit to what you were saying before. It's like simulations and forming your own networks and training off each other. There are these emerging dynamics. You can simulate steps of physics. And you come up with a system that's much too complicated to ever solve. Three pool balls on a table is one such system. It seems pretty simple. You know how to model that, but it actual turns out you can't predict where a balls going to be once you inject some energy into that table. So something that simple is already too complex. So neural network techniques actually allow us to start making those tractable. These NP hard problems. And things like molecular dynamics and actually understanding how different medications and genetics will interact with each other is something we're seeing today. And so I think there's a huge opportunity there. We've actually worked with customers in this space. And I'm seeing it. Like Rosch is acquiring a few different companies in space. They really want to drive it forward, using big data to drive drug development. It's kind of counterintuitive. I never would've thought it had I not seen it myself. >> And there's a big related challenge. Because in personalized medicine, there's smaller and smaller cohorts of people who will benefit from a drug that still takes two billion dollars on average to develop. That is unsustainable. So there's an economic imperative of overcoming the cost and the cycle time for drug development. >> I want to take a go at this question a little bit differently, thinking about not so much where are the industry segments that can benefit from AI, but what are the kinds of applications that I think are most impactful. So if this is what a skilled surgeon needs to know at a particular time to care properly for a patient, this is where most, this area here, is where most surgeons are. They are close to the maximum knowledge and ability to assimilate as they can be. So it's possible to build complex AI that can pick up on that one little thing and move them up to here. But it's not a gigantic accelerator, amplifier of their capability. But think about other actors in health care. I mentioned a couple of them earlier. Who do you think the least trained actor in health care is? >> John: Patients. >> Yes, the patients. The patients are really very poorly trained, including me. I'm abysmal at figuring out who to call and where to go. >> Naveen: You know as much the doctor right? (laughing) >> Yeah, that's right. >> My doctor friends always hate that. Know your diagnosis, right? >> Yeah, Dr. Google knows. So the opportunities that I see that are really, really exciting are when you take an AI agent, like sometimes I like to call it contextually intelligent agent, or a CIA, and apply it to a problem where a patient has a complex future ahead of them that they need help navigating. And you use the AI to help them work through. Post operative. You've got PT. You've got drugs. You've got to be looking for side effects. An agent can actually help you navigate. It's like your own personal GPS for health care. So it's giving you the inforamation that you need about you for your care. That's my definition of Precision Medicine. And it can include genomics, of course. But it's much bigger. It's that broader picture and I think that a sort of agent way of thinking about things and filling in the gaps where there's less training and more opportunity, is very exciting. >> Great start up idea right there by the way. >> Oh yes, right. We'll meet you all out back for the next start up. >> I had a conversation with the head of the American Association of Medical Specialties just a couple of days ago. And what she was saying, and I'm aware of this phenomenon, but all of the medical specialists are saying, you're killing us with these stupid board recertification trivia tests that you're giving us. So if you're a cardiologist, you have to remember something that happens in one in 10 million people, right? And they're saying that irrelevant anymore, because we've got advanced decision support coming. We have these kinds of analytics coming. Precisely what you're saying. So it's human augmentation of decision support that is coming at blazing speed towards health care. So in that context, it's much more important that you have a basic foundation, you know how to think, you know how to learn, and you know where to look. So we're going to be human-augmented learning systems much more so than in the past. And so the whole recertification process is being revised right now. (inaudible audience member speaking) Speak up, yeah. (person speaking) >> What makes it fathomable is that you can-- (audience member interjects inaudibly) >> Sure. She was saying that our brain is really complex and large and even our brains don't know how our brains work, so... are there ways to-- >> What hope do we have kind of thing? (laughter) >> It's a metaphysical question. >> It circles all the way down, exactly. It's a great quote. I mean basically, you can decompose every system. Every complicated system can be decomposed into simpler, emergent properties. You lose something perhaps with each of those, but you get enough to actually understand most of the behavior. And that's really how we understand the world. And that's what we've learned in the last few years what neural network techniques can allow us to do. And that's why our brain can understand our brain. (laughing) >> Yeah, I'd recommend reading Chris Farley's last book because he addresses that issue in there very elegantly. >> Yeah we're seeing some really interesting technologies emerging right now where neural network systems are actually connecting other neural network systems in networks. You can see some very compelling behavior because one of the things I like to distinguish AI versus traditional analytics is we used to have question-answering systems. I used to query a database and create a report to find out how many widgets I sold. Then I started using regression or machine learning to classify complex situations from this is one of these and that's one of those. And then as we've moved more recently, we've got these AI-like capabilities like being able to recognize that there's a kitty in the photograph. But if you think about it, if I were to show you a photograph that happened to have a cat in it, and I said, what's the answer, you'd look at me like, what are you talking about? I have to know the question. So where we're cresting with these connected sets of neural systems, and with AI in general, is that the systems are starting to be able to, from the context, understand what the question is. Why would I be asking about this picture? I'm a marketing guy, and I'm curious about what Legos are in the thing or what kind of cat it is. So it's being able to ask a question, and then take these question-answering systems, and actually apply them so that's this ability to understand context and ask questions that we're starting to see emerge from these more complex hierarchical neural systems. >> There's a person dying to ask a question. >> Sorry. You have hit on several different topics that all coalesce together. You mentioned personalized models. You mentioned AI agents that could help you as you're going through a transitionary period. You mentioned data sources, especially across long time periods. Who today has access to enough data to make meaningful progress on that, not just when you're dealing with an issue, but day-to-day improvement of your life and your health? >> Go ahead, great question. >> That was a great question. And I don't think we have a good answer to it. (laughter) I'm sure John does. Well, I think every large healthcare organization and various healthcare consortiums are working very hard to achieve that goal. The problem remains in creating semantic interoperatability. So I spent a lot of my career working on semantic interoperatability. And the problem is that if you don't have well-defined, or self-defined data, and if you don't have well-defined and documented metadata, and you start operating on it, it's real easy to reach false conclusions and I can give you a classic example. It's well known, with hundreds of studies looking at when you give an antibiotic before surgery and how effective it is in preventing a post-op infection. Simple question, right? So most of the literature done prosectively was done in institutions where they had small sample sizes. So if you pool that, you get a little bit more noise, but you get a more confirming answer. What was done at a very large, not my own, but a very large institution... I won't name them for obvious reasons, but they pooled lots of data from lots of different hospitals, where the data definitions and the metadata were different. Two examples. When did they indicate the antibiotic was given? Was it when it was ordered, dispensed from the pharmacy, delivered to the floor, brought to the bedside, put in the IV, or the IV starts flowing? Different hospitals used a different metric of when it started. When did surgery occur? When they were wheeled into the OR, when they were prepped and drapped, when the first incision occurred? All different. And they concluded quite dramatically that it didn't matter when you gave the pre-op antibiotic and whether or not you get a post-op infection. And everybody who was intimate with the prior studies just completely ignored and discounted that study. It was wrong. And it was wrong because of the lack of commonality and the normalization of data definitions and metadata definitions. So because of that, this problem is much more challenging than you would think. If it were so easy as to put all these data together and operate on it, normalize and operate on it, we would've done that a long time ago. It's... Semantic interoperatability remains a big problem and we have a lot of heavy lifting ahead of us. I'm working with the Global Alliance, for example, of Genomics and Health. There's like 30 different major ontologies for how you represent genetic information. And different institutions are using different ones in different ways in different versions over different periods of time. That's a mess. >> Our all those issues applicable when you're talking about a personalized data set versus a population? >> Well, so N of 1 studies and single-subject research is an emerging field of statistics. So there's some really interesting new models like step wedge analytics for doing that on small sample sizes, recruiting people asynchronously. There's single-subject research statistics. You compare yourself with yourself at a different point in time, in a different context. So there are emerging statistics to do that and as long as you use the same sensor, you won't have a problem. But people are changing their remote sensors and you're getting different data. It's measured in different ways with different sensors at different normalization and different calibration. So yes. It even persists in the N of 1 environment. >> Yeah, you have to get started with a large N that you can apply to the N of 1. I'm actually going to attack your question from a different perspective. So who has the data? The millions of examples to train a deep learning system from scratch. It's a very limited set right now. Technology such as the Collaborative Cancer Cloud and The Data Exchange are definitely impacting that and creating larger and larger sets of critical mass. And again, not withstanding the very challenging semantic interoperability questions. But there's another opportunity Kay asked about what's changed recently. One of the things that's changed in deep learning is that we now have modules that have been trained on massive data sets that are actually very smart as certain kinds of problems. So, for instance, you can go online and find deep learning systems that actually can recognize, better than humans, whether there's a cat, dog, motorcycle, house, in a photograph. >> From Intel, open source. >> Yes, from Intel, open source. So here's what happens next. Because most of that deep learning system is very expressive. That combinatorial mixture of features that Naveen was talking about, when you have all these layers, there's a lot of features there. They're actually very general to images, not just finding cats, dogs, trees. So what happens is you can do something called transfer learning, where you take a small or modest data set and actually reoptimize it for your specific problem very, very quickly. And so we're starting to see a place where you can... On one end of the spectrum, we're getting access to the computing capabilities and the data to build these incredibly expressive deep learning systems. And over here on the right, we're able to start using those deep learning systems to solve custom versions of problems. Just last weekend or two weekends ago, in 20 minutes, I was able to take one of those general systems and create one that could recognize all different kinds of flowers. Very subtle distinctions, that I would never be able to know on my own. But I happen to be able to get the data set and literally, it took 20 minutes and I have this vision system that I could now use for a specific problem. I think that's incredibly profound and I think we're going to see this spectrum of wherever you are in your ability to get data and to define problems and to put hardware in place to see really neat customizations and a proliferation of applications of this kind of technology. >> So one other trend I think, I'm very hopeful about it... So this is a hard problem clearly, right? I mean, getting data together, formatting it from many different sources, it's one of these things that's probably never going to happen perfectly. But one trend I think that is extremely hopeful to me is the fact that the cost of gathering data has precipitously dropped. Building that thing is almost free these days. I can write software and put it on 100 million cell phones in an instance. You couldn't do that five years ago even right? And so, the amount of information we can gain from a cell phone today has gone up. We have more sensors. We're bringing online more sensors. People have Apple Watches and they're sending blood data back to the phone, so once we can actually start gathering more data and do it cheaper and cheaper, it actually doesn't matter where the data is. I can write my own app. I can gather that data and I can start driving the correct inferences or useful inferences back to you. So that is a positive trend I think here and personally, I think that's how we're going to solve it, is by gathering from that many different sources cheaply. >> Hi, my name is Pete. I've very much enjoyed the conversation so far but I was hoping perhaps to bring a little bit more focus into Precision Medicine and ask two questions. Number one, how have you applied the AI technologies as you're emerging so rapidly to your natural language processing? I'm particularly interested in, if you look at things like Amazon Echo or Siri, or the other voice recognition systems that are based on AI, they've just become incredibly accurate and I'm interested in specifics about how I might use technology like that in medicine. So where would I find a medical nomenclature and perhaps some reference to a back end that works that way? And the second thing is, what specifically is Intel doing, or making available? You mentioned some open source stuff on cats and dogs and stuff but I'm the doc, so I'm looking at the medical side of that. What are you guys providing that would allow us who are kind of geeks on the software side, as well as being docs, to experiment a little bit more thoroughly with AI technology? Google has a free AI toolkit. Several other people have come out with free AI toolkits in order to accelerate that. There's special hardware now with graphics, and different processors, hitting amazing speeds. And so I was wondering, where do I go in Intel to find some of those tools and perhaps learn a bit about the fantastic work that you guys are already doing at Kaiser? >> Let me take that first part and then we'll be able to talk about the MD part. So in terms of technology, this is what's extremely exciting now about what Intel is focusing on. We're providing those pieces. So you can actually assemble and build the application. How you build that application specific for MDs and the use cases is up to you or the one who's filling out the application. But we're going to power that technology for multiple perspectives. So Intel is already the main force behind The Data Center, right? Cloud computing, all this is already Intel. We're making that extremely amenable to AI and setting the standard for AI in the future, so we can do that from a number of different mechanisms. For somebody who wants to develop an application quickly, we have hosted solutions. Intel Nervana is kind of the brand for these kinds of things. Hosted solutions will get you going very quickly. Once you get to a certain level of scale, where costs start making more sense, things can be bought on premise. We're supplying that. We're also supplying software that makes that transition essentially free. Then taking those solutions that you develop in the cloud, or develop in The Data Center, and actually deploying them on device. You want to write something on your smartphone or PC or whatever. We're actually providing those hooks as well, so we want to make it very easy for developers to take these pieces and actually build solutions out of them quickly so you probably don't even care what hardware it's running on. You're like here's my data set, this is what I want to do. Train it, make it work. Go fast. Make my developers efficient. That's all you care about, right? And that's what we're doing. We're taking it from that point at how do we best do that? We're going to provide those technologies. In the next couple of years, there's going to be a lot of new stuff coming from Intel. >> Do you want to talk about AI Academy as well? >> Yeah, that's a great segway there. In addition to this, we have an entire set of tutorials and other online resources and things we're going to be bringing into the academic world for people to get going quickly. So that's not just enabling them on our tools, but also just general concepts. What is a neural network? How does it work? How does it train? All of these things are available now and we've made a nice, digestible class format that you can actually go and play with. >> Let me give a couple of quick answers in addition to the great answers already. So you're asking why can't we use medical terminology and do what Alexa does? Well, no, you may not be aware of this, but Andrew Ian, who was the AI guy at Google, who was recruited by Google, they have a medical chat bot in China today. I don't speak Chinese. I haven't been able to use it yet. There are two similar initiatives in this country that I know of. There's probably a dozen more in stealth mode. But Lumiata and Health Cap are doing chat bots for health care today, using medical terminology. You have the compound problem of semantic normalization within language, compounded by a cross language. I've done a lot of work with an international organization called Snowmed, which translates medical terminology. So you're aware of that. We can talk offline if you want, because I'm pretty deep into the semantic space. >> Go google Intel Nervana and you'll see all the websites there. It's intel.com/ai or nervanasys.com. >> Okay, great. Well this has been fantastic. I want to, first of all, thank all the people here for coming and asking great questions. I also want to thank our fantastic panelists today. (applause) >> Thanks, everyone. >> Thank you. >> And lastly, I just want to share one bit of information. We will have more discussions on AI next Tuesday at 9:30 AM. Diane Bryant, who is our general manager of Data Centers Group will be here to do a keynote. So I hope you all get to join that. Thanks for coming. (applause) (light electronic music)

>> Hey welcome back everybody. Jeff Frick here with theCUBE, we are on the ground in downtown San Francisco at the Google Next 17 Conference. It's this crazy conference week, and arguably this is the center of all the action. Cloud is big, Google Cloud Platform is really coming out with a major enterprise shift and focus, which they've always had, but now they're really getting behind it. And I think this conference is over 14,000 people, has grown quite a bit from a few years back, and we're really excited to have one of the powerhouse partners with Google, who's driving to the enterprise, and that's Intel, and I'm really excited to be joined by Raejeanne Skillern, she's the VP and GM of the Cloud Platform Group, Raejeanne, great to see you. >> Thank you, thanks for having me. >> Yeah absolutely. So when we got this scheduled, I was thinking, wow, last time I saw you was at the Open Compute Project 2015, and we were just down there yesterday. >> Yesterday. And we missed each other yesterday, but here we are today. >> So it's interesting, there's kind of the guts of the cloud, because cloud is somebody else's computer that they're running, but there is actually a computer back there. Here, it's really kind of the front end and the business delivery to people to have the elastic capability of the cloud, the dynamic flexibility of cloud, and you guys are a big part of this. So first off, give us a quick update, I'm sure you had some good announcements here at the show, what's going on with Intel and Google Cloud Platform? >> We did, and we love it all, from the silicon ingredients up to the services and solutions, this is where we invest, so it's great to be a part of yesterday and today. I was on stage earlier today with Urs Holzle talking about the Google and Intel Strategic Alliance, we actually announced this alliance last November, between Diane Green and Diane Bryant of Intel. And we had a history, a decade plus long of collaborating on CPU level optimization and technology optimization for Google's infrastructure. We've actually expanded that collaboration to cover hybrid cloud orchestration, security, IOT edge to cloud, and of course, artificial intelligence, machine learning, and deep learning. So we still do a lot of custom work with Google, making sure our technologies run their infrastructure the best, and we're working beyond the infrastructure to the software and solutions with them to make sure that those software and solutions run best on our architecture. >> Right cause it's a very interesting play, with Google and Facebook and a lot of the big cloud providers, they custom built their solutions based on their application needs and so I would presume that the microprocessor needs are very specific versus say, a typical PC microprocessor, which has a more kind of generic across the board type of demand. So what are some of the special demands that cloud demands from the microprocessor specifically? >> So what we've seen, right now, about half the volume we ship in the public cloud segment is customized in some way. And really the driving force is always performance per dollar TCO improvement. How to get the best performance and the lowest cost to pay for that performance. And what we've found is that by working with the top, not just the Super Seven, we call them, but the Top 100, closely, understanding their infrastructure at scale, is that they benefit from more powerful servers, with performance efficiency, more capability, more richly configured platforms. So a lot of what we've done, these cloud service providers have actually in some cases pushed us off of our roadmap in terms of what we can provide in terms of performance and scalability and agility in their infrastructure. So we do a lot of tweaks around that. And then of course, as I mentioned, it's not just the CPU ingredients, we have to optimize in the software level, so we do a lot of co-engineering work to make sure that every ounce of performance and efficiency is seen in their infrastructure. And that's how they, their data center is their cost to sales, they can't afford to have anything inefficient. So we really try to partner to make sure that it is completely tailor-optimized for that environment. >> Right, and the hyperscale, like you said, the infrastructure there is so different than kind of classic enterprise infrastructure, and then you have other things like energy consumption, which, again, at scale, itty bitty little improvements >> It's expensive. >> Make a huge impact. And then application far beyond the cloud service providers, so many of the applications that we interact with now today on a day to day basis are cloud-based applications, whether it is the G Suite for documents or this or that, or whether it's Salesforce, or whether we just put in Asana for task tracking, and Slack, and so many of these things are now cloud-based applications, which is really the way we work more and more and more on our desktops. >> Absolutely. And one of the things we look at is, applications really have kind of a gravity. Some applications are going to have a high affinity to public cloud. You see Tustin Dove, you see email and office collaboration already moving into the public cloud. There are some legacy applications, complex, some of the heavier modeling and simulation type apps, or big huge super computers that might stay on premise, and then you have this middle ground of applications, that, for various reasons, performance, security, data governance, data gravity, business need or IP, could go between the public cloud or stay on premise. And that's why we think it's so important that the world recognizes that this really is about a hybrid cloud. And it's really nice to partner with Google because they see that hybrid cloud as the end state, or they call it the Multi Cloud. And their Kubernetes Orchestration Platform is really designed to help that, to seamlessly move those apps from on a customer's premise into the Google environment and have that flow. So it's a very dynamic environment, we expect to see a lot of workloads kind of continue to be invested and move into the public cloud, and people really optimizing end-to-end. >> So you've been in the data center space, we talked a little bit before we went live, you've been in the data center space for a long, long time. >> Long time. >> We won't tell you how long. (laughing) >> Both: Long time. >> So it must be really exciting for you to see this shift in computing. There's still a lot of computing power at the edge, and there's still a lot of computing power now in our mobile devices and our PCs, but so much more of the heavy lift in the application infrastructure itself is now contained in the data center, so much more than just your typical old-school corporate data centers that we used to see. Really fun evolution of the industry, for you. >> Absolutely, and the public cloud is now one of the fastest growing segments in the enterprise space, in the data center space, I should say. We still have a very strong enterprise business. But what I love is it's not just about the fact that the public cloud is growing, this hybrid really connects our two segments, so I'm really learning a lot. It's also, I've been at Intel 23 years, most of it in the data center, and last year, we reorganized our company, we completely restructured Intel to be a cloud and IoT company. And from a company that for multiple decades was a PC or consumer-based client device company, it is just amazing to have data center be so front and center and so core to the type of infrastructure and capability expansion that we're going to see across the industry. We were talking about, there isn't going to be an industry left untouched by technology. Whether it's agriculture, or industrial, or healthcare, or retail, or logistics. Technology is going to transform them, and it all comes back to a data center and a cloud-based infrastructure that can handle the data and the scale and the processing. >> So one of the new themes that's really coming on board, next week will it be a Big Data SV, which has grown out of Hadoop and the old big data conversation. But it's really now morphing into the next stage of that, which is machine learning, deep learning, artificial intelligence, augmented reality, virtual reality, so this whole 'nother round that's going to eat up a whole bunch of CPU capacity. But those are really good cloud-based applications that are now delivering a completely new level of value and application sophistication that's driven by power back at the data center. >> Right. We see, artificial intelligence has been a topic since the 50s. But the reality is, the technology is there today to both capture and create the data, and compute on the data. And that's really unlocking this capabilities. And from us as a company, we see it as really something that is going to not just transform us as a business but transform the many use cases and industries we talked about. Today, you or I generate about a gig and a half of data, through our devices and our PC and tablet. A smart factory or smart plane or smart car, autonomous car, is going to generate terabytes of data. Right, and that is going to need to be stored. Today it's estimated only about 5% of the data captured is used for business insight. The rest just sits. We need to capture the data, store the data efficiently, use the data for insights, and then drive that back into the continuous learning. And that's why these technologies are so amazing, what they're going to be able to do, because we have the technology and the opportunity in the business space, whether it's AI for play or for good or for business, AI is going to transform the industry. >> It's interesting, Moore's Law comes up all the time. People, is Moore's Law done, is Moore's Law done? And you know, Moore's Law is so much more than the physics of what he was describing when he first said that in the first place, about number of transistors on a chip. It's really about an attitude, about this unbelievable drive to continue to innovate and iterate and get these order of magnitude of increase. We talked to David Floyer at OCP yesterday, and he's talking about it's not only the microprocessors and the compute power, but it's the IO, it's the networking, it's storage, it's flash storage, it's the interconnect, it's the cabling, it's all these things. And he was really excited that we're getting to this massive tipping point, of course in five years we'll look back and think it's archaic, of these things really coming together to deliver low latency almost magical capabilities because of this combination of factors across all those different, kind of the three horseman of computing, if you will, to deliver these really magical, new applications, like autonomous vehicles. >> Absolutely. And we, you'll hear Intel talk about Jevons Paradox, which is really about, if you take something and make it cheaper and easier to consume, people will consume more of it. We saw that with virtualization. People predicted oh everything's going to slow down cause you're going to get higher utilization rates. Actually it just unlocked new capabilities and the market grew because of it. We see the same thing with data. Our CEO will talk about, data is the new oil. It is going to transform, it's going to unlock business opportunity, revenue growth, cost savings in environment, and that will cause people to create more services, build new businesses, reach more people in the industry, transform traditional brick and mortar businesses to the digital economy. So we think we're just on the cusp of this transformation, and the next five to 10 years is going to be amazing. >> So before we let you go, again, you've been doing this for 20 plus years, I wasn't going to say anything, she said it, I didn't say it, and I worked at Intel the same time, so that's good. As you look forward, what are some of your priorities for 2017, what are some of the things that you're working on, that if we get together, hopefully not in a couple years at OCP, but next year, that you'll be able to report back that this is what we worked on and these are some of the new accomplishments that are important to me? >> So I'm really, there's a number of things we're doing. You heard me mention artificial intelligence many, many times. In 2016, Intel made a number of significant acquisitions and investments to really ensure we have the right technology road map for artificial intelligence. Machine learning, deep learning, training and inference. And we've really shored up that product portfolio, and you're going to see these products come to market and you're going to see user adoption, not just in my segment, but transforming multiple segments. So I'm really excited about those capabilities. And a lot of what we'll do, too, will be very vertical-based. So you're going to see the power of the technology, solving the health care problem, solving the retail problem, solving manufacturing, logistics, industrial problems. So I like that, I like to see tangible results from our technology. The other thing is the cloud is just growing. Everybody predicted, can it continue to grow? It does. Companies like Google and our other partners, they keep growing and we grow with them, and I love to help figure out where they're going to be two or three years from now, and get our products ready for that challenge. >> Alright, well I look forward to our next visit. Raejeanne, thanks for taking a few minutes out of your time and speaking to us. >> It was nice to see you again. >> You too. Alright, she's Raejeanne Skillern and I'm Jeff Frick, you're watching theCUBE, we're at the Google Cloud Next Show 2017, thanks for watching. (electronic sounds)

>> [Narrator] Live from Boston Massachusetts. This is the Cube, covering Sparks Summit East 2017. Brought to you by Databricks. Now here are your hosts, Dave Alante and George Gilbert. >> Back to you Boston everybody. This is the Cube and we're here live at Spark Summit East, #SparkSummit. Ziya Ma is here. She's the Vice President of Big Data at Intel. Ziya, thanks for coming to the Cube. >> Thanks for having me. >> You're welcome. So software is our topic. Software at Intel. You know people don't necessarily associate Intel with always with software but what's the story there? >> So actually there are many things that we do for software. Since I manage the Big Data engineering organization so I'll just say a little bit more about what we do for Big Data. >> [Dave] Great. >> So you know Intel do all the processors, all the hardware. But when our customers are using the hardware, they like to get the best performance out of Intel hardware. So this is for the Big Data space. We optimize the Big Data solution stack, including Spark and Hadoop on top of Intel hardware. And make sure that we leverage the latest instructions set so that the customers get the most performance out of the newest released Intel hardware. And also we collaborated very extensively with the open source community for Big Data ecosystem advancement. For example we're a leading contributor to Apache Spark ecosystem. We're also a top contributor to Apache Hadoop ecosystem. And lately we're getting into the machine learning and deep learning and the AI space, especially integrating those capabilities into the Big Data eTcosystem. >> So I have to ask you a question to just sort of strategically, if we go back several years, you look at during the Unix days, you had a number of players developing hardware, microprocessors, there were risk-based systems, remember MIPS and of course IBM had one and Sun, et cetera, et cetera. Some of those live on but very, very small portion of the market. So Intel has dominated the general purpose market. So as Big Data became more mainstream, was there a discussion okay, we have to develop specialized processors, which I know Intel can do as well, or did you say, okay, we can actually optimize through software. Was that how you got here? Or am I understanding that? >> We believe definitely software optimization, optimizing through software is one thing that we do. That's why Intel actually have, you may not know this, Intel has one of the largest software divisions that focus on enabling and optimizing the solutions in Intel hardware. And of course we also have very aggressive product roadmap for advancing continuously our hardware products. And actually, you mentioned a general purpose computing. CPU today, in the Big Data market, still has more than 95% of the market. So that's still the biggest portion of the Big Data market. And will continue our advancement in that area. And obviously as the Ai and machine learning, deep learning use cases getting added into the Big Data domain and we are expanding our product portfolio into some other Silicon products. >> And of course that was kind of the big bet of, we want to bet on Intel. And I guess, I guess-- >> You should still do. >> And still do. And I guess, at the time, Seagate or other disk mounts. Now flash comes in. And of course now Spark with memory, it's really changing the game, isn't it? What does that mean for you and the software group? >> Right, so what do we... Actually, still we focus on the optimi-- Obviously at the hardware level, like Intel now, is not just offering the computing capability. We also offer very powerful network capability. We offer very good memory solutions, memory hardware. Like we keep talking about this non-volatile memory technologies. So for Big Data, we're trying to leverage all those newest hardware. And we're already working with many of our customers to help them, to improve their Big Data memory solution, the e-memory, analytics type of capability on Intel hardware, give them the most optimum performance and most secure result using Intel hardware. So that's definitely one thing that we continue to do. That's going to be our still our top priority. But we don't just limit our work to optimization. Because giving user the best experience, giving user the complete experience on Intel platform is our ultimate goal. So we work with our customers from financial services company. We work with folks from manufacturing. From transportation. And from other IOT internet of things segment. And to make sure that we give them the easiest Big Data analytics experience on Intel hardware. So when they are running those solutions they don't have to worry too much about how to make their application work with Intel hardware, and how to make it more performant with Intel hardware. Because that's the Intel software solution that's going to bridge the gap. We do that part of the job. And so that it will make our customers experience easier and more complete. >> You serve as the accelerant to the marketplace. Go ahead George. >> [Ziya] That's right. >> So Intel's big ML as the news product, as of the last month of so, open source solution. Tell us how there are other deep learning frameworks that aren't as fully integrated with Spark yet and where BigML fits in since we're at a Spark conference. How it backfills some functionality and how it really takes advantage of Intel hardware. >> George, just like you said, BigDL, we just open sourced a month ago. It's a deep learning framework that we organically built onto of Apache Spark. And it has quite some differences from the other mainstream deep learning frameworks like Caffe, Tensorflow, Torch and Tianu are you name it. The reason that we decide to work on this project was again, through our experience, working with our analytics, especially Big Data analytic customers, as they build their AI solutions or AI modules within their analytics application, it's funny, it's getting more and more difficult to build and integrate AI capability into their existing Big Data analytics ecosystem. They had to set up a different cluster and build a different set of AI capabilities using, let's say, one of the deep learning frameworks. And later they have to overcome a lot of challenges, for example, moving the model and data between the two different clusters and then make sure that AI result is getting integrated into the existing analytics platform or analytics application. So that was the primary driver. How do we make our customers experience easier? Do they have to leave their existing infrastructure and build a separate AI module? And can we do something organic on top of the existing Big Data platform, let's say Apache Spark? Can we just do something like that? So that the user can just leverage the existing infrastructure and make it a naturally integral part of the overall analytics ecosystem that they already have. So this was the primary driver. And also the other benefit that we see by integrating this BigDL framework naturally was the Big Data platform, is that it enables efficient scale-out and fault tolerance and elasticity and dynamic resource management. And those are the benefits that's on naturally brought by Big Data platform. And today, actually, just with this short period of time, we have already tested that BigDL can scale easily to tens or hundreds of nodes. So the scalability is also quite good. And another benefit with solution like BigDL, especially because it eliminates the need of setting a separate cluster and moving the model between different hardware clusters, you save your total cost of ownership. You can just leverage your existing infrastructure. There is no need to buy additional set of hardware and build another environment just for training the model. So that's another benefit that we see. And performance-wise, again we also tested BigDL with Caffe, Torch and TensorFlow. So the performance of BigDL on single node Xeon is orders of magnitude faster than out of box at open source Caffe, TensorFlow or Torch. So it definitely it's going to be very promising. >> Without the heavy lifting. >> And useful solution, yeah. >> Okay, can you talk about some of the use cases that you expect to see from your partners and your customers. >> Actually very good question. You know we already started a few engagement with some of the interested customers. The first customer is from Stuart Industry. Where improving the accuracy for steel-surface defect recognition is very important to it's quality control. So we worked with this customer in the last few months and built end-to-end image recognition pipeline using BigDL and Spark. And the customer just through phase one work, already improved it's defect recognition accuracy to 90%. And they're seeing a very yield improvement with steel production. >> And it used to by human? >> It used to be done by human, yes. >> And you said, what was the degree of improvement? >> 90, nine, zero. So now the accuracy is up to 90%. And another use case and financial services actually, is another use case, especially for fraud detection. So this customer, again I'm not at the customer's request, they're very sensitive the financial industry, they're very sensitive with releasing their name. So the customer, we're seeing is fraud risks were increasing tremendously. With it's wide range of products, services and customer interaction channels. So the implemented end-to-end deep learning solution using BigDL and Spark. And again, through phase one work, they are seeing the fraud detection rate improved 40 times, four, zero times. Through phase one work. We think there were more improvement that we can do because this is just a collaboration in the last few month. And we'll continue this collaboration with this customer. And we expect more use cases from other business segments. But that are the two that's already have BigDL running in production today. >> Well so the first, that's amazing. Essentially replacing the human, have to interact and be much more accurate. The fraud detection, is interesting because fraud detection has come a long way in the last 10 years as you know. Used to take six months, if they found fraud. And now it's minutes, seconds but there's a lot of false positives still. So do you see this technology helping address that problem? >> Yeah, we actually that's continuously improving the prediction accuracy is one of the goals. This is another reason why we need to bring AI and Big Data together. Because you need to train your model. You need to train your AI capabilities with more and more training data. So that you get much more improved training accuracy. Actually this is the biggest way of improving your training accuracy. So you need a huge infrastructure, a big data platform so that you can host and well manage your training data sets. And so that it can feed into your deep learning solution or module for continuously improving your training accuracy. So yes. >> This is a really key point it seems like. I would like to unpack that a little bit. So when we talk to customers and application vendors, it's that training feedback loop that gets the models smarter and smarter. So if you had one cluster for training that was with another framework, and then Spark was your... Rest of your analytics. How would training with feedback data work when you had two separate environments? >> You know that's one of the drivers why we're creating BigDL. Because, we tried to port, we did not come to BigDL at the very beginning. We tried to port the existing deep learning frameworks like Caffe and Tensorflow onto Spark. And you also probably saw some research papers folks. There's other teams that out there that's also trying to port Caffe, Tensorflow and other deep learning framework that's out there onto Spark. Because you have that need. You need to bring the two capabilities together. But the problem is that those systems were developed in a very traditional way. With Big Data, not yet in consideration, when those frameworks were created, were innovated. But now the need for converging the two becomes more and more clear, and more necessary. And that's we way, when we port it over, we said gosh, this is so difficult. First it's very challenging to integrate the two. And secondly the experience, after you've moved it over, is awkward. You're literally using Spark as a dispatcher. The integration is not coherent. It's like they're superficially integrated. So this is where we said, we got to do something different. We can not just superficially integrate two systems together. Can we do something organic on top of the Big Data platform, on top of Apache Spark? So that the integration between the training system, between the feature engineering, between data management can &be more consistent, can be more integrated. So that's exactly the driver for this work. >> That's huge. Seamless integration is one of the most overused phrases in the technology business. Superficial integration is maybe a better description for a lot of those so-called seamless integrations. You're claiming here that it's seamless integration. We're out of time but last word Intel and Spark Summit. What do you guys got going here? What's the vibe like? >> So actually tomorrow I have a keynote. I'm going to talk a little bit more about what we're doing with BigDL. Actually this is one of the big things that we're doing. And of course, in order for BigDL, system like BigDL or even other deep learning frameworks, to get optimum performance on Intel hardware, there's another item that we're highlighting at MKL, Intel optimized Math Kernel Library. It has a lot of common math routines. That's optimized for Intel processor using the latest instruction set. And that's already, today, integrated into the BigDL ecosystem.z6 So that's another thing that we're highlighting. And another thing is that those are just software. And at hardware level, during November, Intel's AI day, our executives from BK, Diane Bryant and Doug Fisher. They also highlighted the Nirvana product portfolio that's coming out. That will give you different hardware choices for AI. You can look at FPGA, Xeon Fi, Xeon and our new Nirvana based Silicon like Crestlake. And those are some good silicon products that you can expect in the future. Intel, taking us to Nirvana, touching every part of the ecosystem. Like you said, 95% share and in all parts of the business. Yeah, thanks very much for coming the Cube. >> Thank you, thank you for having me. >> You're welcome. Alright keep it right there. George and I will be back with our next guest. This is Spark Summit, #SparkSummit. We're the Cube. We'll be right back.

>> Narrator: Live from San Francisco, it's the Cube. Covering Oracle OpenWorld 2016. Brought to you by Oracle. Now here's your hosts, John Furrier and Peter Burris. >> Hey welcome back everyone, we are here live at Oracle OpenWorld in San Francisco on the show floor. This is the Cube, SiliconANGLE's flagship program. We go out to the events and extract the entrepreneurs I'm John Furrier. Co-seated with me is going to be Peter Burris Head of Research for SiliconANGLE. Also the GM of Wikibon.com our research arm. Our next guest is Reggie Bradford who is a Senior Vice President of Product Development for Oracle Cloud. Reporting for Thomas Kurian who could not make the keynote last night but he did send in the tape during the Diane Bryant thing so that was really good. So hope everything is going well with his family. Welcome to the Cube. >> Thank you, glad to be here. >> Okay so you're a product guy which is great 'cause you're now on the product road map. You get to look at the holistic picture, not so much the go to market which Oracle has that separation. >> Yeah. >> The Cloud is really a conversion of Ironman when you think about it. The stack has to be set up in a way that enables innovation, at the same time preserves the value of what's moving to the Cloud or what get's started >> Yeah. >> in the Cloud. Cloud-Native. Take a minute to describe what Oracle's doing in this regard because you do have a huge install base. >> Yeah. >> And Larry pointed out in the earnings call, they're not yet moving all over yet so Microsoft started their progression. >> Yeah. >> You guys got a huge tsunami coming. >> Yeah. Well I think it's an evolution. It's not a revolution, first of all. So we have 420,000 customers worldwide as you mentioned of substantials installed base. As Larry has mentioned before with Thomas, we've been working on the Cloud applications for over 10 years. We started with a SaaS layer. Now the PaaS layer and the infrastructure layer. I think that if I was to use a sports analogy, we're in the first inning of a nine inning game so we're just getting started. >> First of all we love sports metaphors. I had it at top of the second but okay we'll give early innings. But Fusion ten years ago was kind of pre-cloud although Larry had the famous Churchill Club video I think about ten years ago with the Sun guy saying the Cloud is just a data center with an address that nobody knows. It's essentially that kind of concept. So I can see the progression of the ten year run but something happened four years ago. We could feel it when we were covering it, the show here. You saw Larry on stage almost knowing what's coming. They had not yet released the Fusion base. What's changed in your mind internally? And share with the folks, what's the internal pulse? 'Cause some say you're late to the game. Which you guys refute. You're in top of the second, how late can you be? But how much more work needs to get done? Can you share >> Yeah. >> the internal mojo, mindset, vibe and what work needs to get done? >> Well, I think let's start with it's a 135,000 person company. Not that there's corporate inertia but it's a very large company with a lot of customers that have an existing installed base. I think that we're I definitely don't think that we're late to the Cloud. I think if you look at the work loads that have been done to date. Something like six percent of workloads had been done in the Cloud. But I think that I can't speak for the past. I've been here for four years. My company was acquired. I agree with you. The energy and momentum, the acceleration, the sense of urgency is palpable. And it continues to accelerate. I think there's just this recognition that we feel like we've got a very strong position and a strong hand and we're going to play that. >> One of the things Larry mentioned on his keynote yesterday and which came up today is that Amazon is an environment where you can go to and you've got to do all this work. Oracle, you can just move stuff to Oracle, and it moves to the Cloud instantly. But it still brings up the integration game because it's still a lot more kind of point solutions out there. >> Yeah. >> You can call a startup doing something. >> Yeah. >> An ecosystem as a feature, not a company that would might want to plug into that so how do you bring friction-less integration with a suite mindset? Because essentially Oracle has that gravity. >> Yeah. >> But at the same time you don't want to get stuck in that as an Oracle only solution. >> Absolutely. >> How do you integrate well with others? >> Well first of all I'd say it starts with a mentality that when I was running my startup in 2011, if you think about just the marketing cloud alone, Scott Brinker has the landscape of startups in the Cloud. There was 100 I think back then and I think there's almost 4,000 now. Just in Marketing Cloud alone so the Cloud has opened up a huge era of new startups and innovation and credit card swiping and companies can get into that. That challenge for that is, as Larry said last night, nobody wants to integrate 50 different applications from 50 different companies. I think that we come in with this, we've got all the layers of the stack but we've also got to have a mindset and a mentality being able to be open to best and be -- to bring in end solutions from startups via APIs and making it easy for them to work with us and want to partner with us. I think that's the future for Oracle. >> Do you see Oracle inside or do you see Oracle facilitating other brands? So is it more, going back to what John's point was, is it customary to sit down with an Oracle screen and access stuff? Or is a customer going to sit down with an Oracle framework and know what they're accessing through the partners? >> Well I think it depends on the product and the customer. I'd say we're going to be both. I mean I think we've got the breadth and depth and capability to be it an inside platform type approach or infrastructure but also if you look at an HCM or ERP or Marketing Cloud, we're going to be on the front end. >> Do you anticipate that you're going to go more horizontal as opposed to vertical? So you're going to go from the one infrastructure to the horizontal and then let other folks verticalize? Is that kind of how the thinking is? >> I don't know that we see necessarily a distinction at this point. We obviously have a big industry. You guys have probably talked to them before. Go to Market Group, our entire business unit, but I think we're going to continue to take the market, what the market gives us, and continue to push out our solutions. >> So Reggie, I've talked to a lot of your customers all the time on the Cube and channel partners at Oracle and one of the things that keeps on coming up is that the swim lanes of the database is a big part of the business and you start to see now with the Cloud being more of an integrated IaaS, PaaS and SaaS, the role of database is certainly changing. But it still makes up a big bulk of the business, however you kind of consolidate the numbers. However they fall, the database is 70% roughly the business. My opinion. My analysis. Okay so I wrote a tweet last night, kind of being snarky, but kind of on the whole Tom Brady thing, bringing up sports a analogy. "Free Tom Brady," I said, "Free Database." (laughing) Meaning that one of the thesis that the Wikibon research have come up with is that with free data, you have more data exposed for potential use and by having locked in data, you can still manage the systems of record and then start getting at a whole new set of engagement data that could then spawn another set of innovation on top of it. This is clearly where the market's going in memory and whatnot. So question: What's going on with the database? How do you talk the customers into saying okay the database is going to be fine, use some other databases, they'll still work with Oracle. How do you have that conversation? Are you breaking down that swim lane, broadening the swim lane for the database team? >> Yeah I think from a business model standpoint, to use your Tom Brady analogy tweet. >> The database is suspended for four games? (laughing) >> I don't want to make any declarations on the future business models but I think the fundamental underpinnings of a lot of these new capabilities that are enabled with data, whether it's AI, machine learning, internet of things, I think that those are going to be best leveraged when you've got the broadest and deepest swath of internal and external third party data. And we feel like we've been in that business from day one forty years ago and now we have the broadest suite or capability and data and integrating that data and making it easy to consume and use. >> It just seems that the old move that Oracle made, which I thought was brilliant, the database business, and web logic was a nice sticky component of that, allowed the extensibility. So we're seeing that same dynamic in the Cloud where we want some reliability, we want some high quality. As Larry said yesterday, it's hard to do what you do, I get that. But at the same time there's a growth opportunity, an innovation strategy for Oracle and your partners. Developing an ecosystem or potentially a channel partner. >> Yeah. >> We need to see that thing so do you guys talk about that in the product group and what specifically does that translate to in terms of new features, new focus? >> Yeah we've got a very robust data cloud strategy which is leading in the internal and external third party. Marrying those data sets together and integrating those into our product suite on the applications side. Specifically around the database, opening that up to third parties and how that evolves, I think there's going to be more to come on that. >> What's the biggest thing that people are missing when they try to understand and squint through the Oracle breadth of massive size? When you look at the Cloud strategy, the numbers aren't really killing it. It's four billionth for Oracle. I mean it's still small relative to the big piece of the pie with a lot more growth to go. If that should be comforting to Wall Street at least but from a market perspective, what does the Cloud mean? I mean how do you have that conversation? >> I think that what's missing is that if this was a stand alone company it would be an incredibly viable IPO-able company with a growth rate faster than anybody would scale on the Cloud. Growing double the growth rate. I think another unknown is that we sell a lot of product to companies under 500 million in revenue. 75% of the user base, of the customer base, is under 500 million in revenue so we call that small to mid-size business so everybody thinks Oracle is only an enterprise class company. We are enterprise class but we also scale down and I think that's part of the announcement you've seen today. >> And the new net, new customers are higher than they were. >> Exactly. >> And the growth thing I'm like, okay you know 77% from where you were, I doubled my market share from one to two percent. I mean, percentage is a good benchmark, I get that, but at the end of the day the numbers are the numbers so Cloud-Native is attractive But it's hard. We were talking about it in our intro. It's hard for companies to get there. >> Yeah. >> They have their own inertia. So we're really trying to understand, what's the path for the customer? When you talk to a customer, and think about the customer from a product that you just roll out products, they want that bridge. Is that the past layer? How do you guys -- >> Well we try not to define it. I think it can be challenging because we do have so much product. >> Yeah. >> And we cover so much ground but that's what makes us so successful and that's why companies rely on us. I think the good new is, we have the most flexible platform. If you want to move some of your workloads, you want to move some of your applications to the Cloud, you want to do a hybrid, you want to transition over time, we've said a 100% of our customers are going to move to the Cloud and a 100% of our applications are going to run in the Cloud. But we haven't set a time table on that. >> I want to spend the rest of the time of the segment, to talk about your entrepreneurial background. You sold the company to Oracle four years ago, so you've been in the system for four years but prior to that, you had to be nimble and you also did some acquisitions. I don't know if they were hires but ultimately you're putting together a lot of stuff. So dealing with different product road maps. So two questions: One is how does that go on today? Is there any agile to that in terms of doing that? Is it hard? Is it easier today than it was before? And two: For startups out there, there are a lot of startups that aren't going to make it. They're not going to be the unicorn. They're not going to be that big company but they might be a nice 10 million dollar business. But it's looking for an ecosystem. >> Yeah. So the second part, which I'm going to take first is I'm a startup, I'm not going to make it to the IPO. Maybe it's a lifestyle business, cash small business whatever the word, I need a home. I need an ecosystem. It would appear that you guys would be a good fit for those kinds of companies. Can you share your thoughts on what those entrepreneurs should be thinking? Actions they can take? >> Yeah I mean I think first of all this is Reggie speaking and not Oracle but as a startup, find a big addressable market opportunity that nobody has. I know it's easier said than done. Surround yourself with great people and focus. I think the biggest challenge that startups face is they try to do too many things or be all things to all people. If you can find that niche, yes you know I've been part of -- We've acquired, personally speaking we've acquired many, many, many companies that fill a particular niche. It's easier for us to acquire and to integrate than it is for us to go build it ourselves. I think that's the mantra. >> So the first part of the question, now that you have that entrepreneurs, how's that translate inside Oracle? Because if you think about it, Oracle also does a lot of M&A. A lot of organic growth as well with R&D but you have to kind of pull those together. Does the data cloud, is there new fabric that gets developed so it's not like -- You know some startups go, you be by yourself for a little while and then some get integrated in quickly. Is there a way for an environment to be agile in the sense that you can just plug these new opportunities in. >> Well I would say again having gone through three acquisitions, and this is the God's honest truth, Oracle knows how to acquire companies better than -- certainly it's been the best experience that I've had over the other two. We can always improve. You know a lot of times you read about or follow the big tape deals. You know the NetSuites of the world and that but there's a lot of smaller companies that get acquired and I think that there's a very solid methodology and approach that we take that enables us to capture the value of these startups and make them feel like they're part of a broader company. More than half of the employees at Oracle have come through acquisitions. >> Yeah. Reggie, final question for the folks watching. What's the one thing they may not know about the Oracle Cloud that they should know about? >> Again, what I think that they probably don't know is we are working with companies that have as few as one or two employees that are using our cloud right now. So we're not just a company that's only available to enterprise. We are contemporizing our offering for companies of all sizes that want to deliver better quality and to lower cost. >> Cloud for all. >> Cloud for all. Exactly. (laughing) Democratizing the Cloud. (laughing) >> I wish we had more time. I'd love to dig into the developer conversation. How you guys were with developers. Any quick comment on the developer angle? >> We've always >> You own Java so it's like-- >> Yeah, we've always sought developers and I think if anything, you're going to see us push further towards that community. It's so vital and important for us to develop new products to integrate and create more capability. >> Looking forward to following up on that. Thanks for coming in and sharing your insight inside the Cube. Really appreciate it. >> Thanks for having me. >> Reggie Bradford here inside the Cube at Oracle OpenWorld live in San Francisco. More coverage. Three days of wall to wall live coverage. 35 segments. I just saw CNBC packing it up. They only had a few interviews and they go. So it looks like we won first round. Day one of the bake off between Bloomberg and CNBC. You're watching the Cube. Be right back with more after this short break. (upbeat electronic music) >> I remember

>> Voiceover: Live from Las Vegas, it's theCUBE, covering Discover 2016, Las Vegas. Brought to you by Hewlett Packard Enterprise. Now, here are your hosts, John Furrier and Dave Vellante. >> Okay, welcome back everyone. We are here live in Las Vegas for SiliconANGLE Media's theCUBE. It's our flagship program, we go out to the events to extract the signal from the noise, we're your exclusive coverage of HP Enterprise, Discover 2016, I'm John Furrier with my co-host, Dave Vellante, extracting the signals from the noise with two great guests, Dr. Tom Bradicich, VP and General Manager of the servers and IoT systems, and Eric Starkloff, the EVP of Global Sales and Marketing at National Instruments, welcome back to theCUBE. >> Thank you. >> John: Welcome for the first time Cube alumni, welcome to theCUBE. >> Thank you. >> So we are seeing a real interesting historic announcement from HP, because not only is there an IoT announcement this morning that you are the architect of, but the twist that you're taking with IoT, is very cutting edge, kind of like I just had Google IO, and at these big conferences they always have some sort of sexy demo, that's to kind of show the customers the future, like AI, or you know, Oculus Rift goggles as the future of their application, but you actually don't have something that's futuristic, it's reality, you have a new product, around IoT, at the Edge, Edgeline, the announcements are all online. Tom, but you guys did something different. And Eric's here for a reason, we'll get to that in a second, but the announcement represents a significant bet. That you're making, and HP's making, on the future of IoT. Please share the vision, and the importance of this event. >> Well thank you, and it's great to be back here with you guys. We've looked around and we could not find anything that existed today, if you will, to satisfy the needs of this industry and our customers. So we had to create not only a new product, but a new product category. A category of products that didn't exist before, and the new Edgeline1000, and the Edgeline4000 are the first entrance into this new product category. Now, what's a new product category? Well, whoever invented the first automobile, there was not a category of automobiles. When the first automobile was invented, it created a new product category called automobiles, and today everybody has a new entry into that as well. So we're creating a new product category, called converged IoT systems. Converged IoT systems are needed to deliver the real-time insights, real-time response, and advance the business outcomes, or the engineering outcomes, or the scientific outcomes, depending on the situation of our customers. They're needed to do that. Now when you have a name, converged, that means somewhat, a synonym is integration, what did we integrate? Now, I want to tell you the three major things we integrated, one of which comes from Eric, and the fine National Instruments company, that makes this technology that we actually put in, to the single box. And I can't wait to tell you more about it, but that's what we did, a new product category, not just two new products. >> So, you guys are bringing two industries together, again, that's not only just point technologies or platforms, in tooling, you're bringing disparate kind of players together. >> Yes. >> But it's not just a partnership, it's not like shaking hands and doing a strategic partnership, so there's real meat on the bone here. Eric, talk about one, the importance of this integration of two industries, basically, coming together, converged category if you will, or industry, and what specifically is in the box or in the technology. >> Yeah, I think you hit it exactly right. I mean, everyone talks about the convergence of OT, or operational technology, and IT. And we're actually doing it together. I represent the OT side, National Instruments is a global leader. >> John: OT, it means, just for the audience? >> Operational Technology, it's basically industrial equipment, measurement equipment, the thing that is connected to the real world. Taking data and controlling the thing that is in the internet of things, or the industrial internet of things as we play. And we've been doing internet of... >> And IT is Information Technologies, we know what that is, OT is... >> I figured that one you knew, OT is Operational Technology. We've been doing IoT before it was a buzzword. Doing measurement and control systems on industrial equipment. So when we say we're making it real, this Edgeline system actually incorporates in National Instruments technology, on an industry standard called PXI. And it is a measurement and control standard that's ubiquitous in the industry, and it's used to connect to the real world, to connect to sensors, actuators, to take in image data, and temperature data and all of those things, to instrument the world, and take in huge amounts of analog data, and then apply the compute power of an Edgeline system onto that application. >> We don't talk a lot about analog data in the IT world. >> Yeah. >> Why is analog data so important, I mean it's prevalent obviously in your world. Talk a little bit more about that. >> It's the largest source of data in the world, as Tom says it's the oldest as well. Analog, of course if you think about it, the analog world is literally infinite. And it's only limited by how many things we want to measure, and how fast we measure them. And the trend in technology is more measurement points and faster. Let me give you a couple of examples of the world we live in. Our customers have acquired over the years, approximately 22 exabytes of data. We don't deal with exabytes that often, I'll give an analogy. It's streaming high definition video, continuously, for a million years, produces 22 exabytes of data. Customers like CERN, that do the Large Hadron Collider, they're a customer of ours, they take huge amounts of analog data. Every time they do an experiment, it's the equivalent of 14 million images, photographs, that they take per second. They create 25 petabytes of data each year. The importance of this and the importance of Edgeline, and we'll get into this some, is that when you have that quantity of data, you need to push processing, and compute technology, towards the edge. For two main reasons. One, is the quantity of data, doesn't lend itself, or takes up too much bandwidth, to be streaming all of it back to central, to cloud, or centralized storage locations. The other one that's very, very important is latency. In the applications that we serve, you often need to make a decision in microseconds. And that means that the processing needs to be done, literally the speed of light is a limiting factor, the processing must be done on the edge, at the thing itself. >> So basically you need a data center at the edge. >> A great way to say it. >> A great way to say it. And this data, or big analog data as we love to call it, is things like particulates, motion, acceleration, voltage, light, sound, location, such as GPS, as well as many other things like vibration and moisture. That is the data that is pent up in things. In the internet of things. And Eric's company National Instruments, can extract that data, digitize it, make it ones and zeroes, and put it into the IT world where we can compute it and gain these insights and actions. So we really have a seminal moment here. We really have the OT industry represented by Eric, connecting with the IT industry, in the same box, literally in the same product in the box, not just a partnership as you pointed out. In fact it's quite a moment, I think we should have a photo op here, shaking hands, two industries coming together. >> So you talk about this new product category. What are the parameters of a new product category? You gave an example of an automobile, okay, but nobody had ever seen one before, but now you're bringing together sort of two worlds. What defines the parameters of a product category, such that it warrants a new category? >> Well, in general, never been done before, and accomplishes something that's not been done before, so that would be more general. But very specifically, this new product, EL1000 and EL4000, creates a new product category because this is an industry first. Never before have we taken data acquisition and capture technology from National Instruments, and data control technology from National Instruments, put that in the same box as deep compute. Deep x86 compute. What do I mean by deep? 64 xeon cores. As you said, a piece of the data center. But that's not all we converged. We took Enterprise Class systems management, something that HP has done very well for many, many years. We've taken the Hewlett Packard Enterprise iLo lights-out technology, converged that as well. In addition we put storage in there. 10s of terabytes of storage can be at the edge. So by this combination of things, that did exist before, the elements of course, by that combination of things, we've created this new product category. >> And is there a data store out there as well? A database? >> Oh yes, now since we have, this is the profundity of what I said, lies in the fact that because we have so many cores, so close to the acquisition of the data, from National Instruments, we can run virtually any application that runs on an x86 server. So, and I'm not exaggerating, thousands. Thousands of databases. Machine learning. Manageability, insight, visualization of data. Data capture tools, that all run on servers and workstations, now run at the edge. Again, that's never been done before, in the sense that at the edge today, are very weak processing. Very weak, and you can't just run an unmodified app, at that level. >> And in terms of the value chain, National Instruments is a supplier to this new product category? Is that the right way to think about it? >> An ingredient, a solution ingredient but just like we are, number one, but we are both reselling the product together. >> Dave: Okay. >> So we've jointly, collaboratively, developed this together. >> So it's engineers and engineers getting together, building the product. >> Exactly. His engineers, mine, we worked extremely close, and produced this beauty. >> We had a conversation yesterday, argument about the iPhone, I was saying hey, this was a game-changing category, if you will, because it was a computer that had software that could make phone calls. Versus the other guys, who had a phone, that could do text messages and do email. With a browser. >> Tom: With that converged product. >> So this would be similar, if I may, and you can correct me if I'm wrong, I want you to correct me and clarify, what you're saying is, you guys essentially looked at the edge differently, saying let's build the data center, at the edge, in theory or in concept here, in a little concept, but in theory, the power of a data center, that happens to do edge stuff. >> Tom: That's right. >> Is that accurate? >> I think it's very accurate. Let me make a point and let you respond. >> Okay. >> Neapolitan ice cream has three flavors. Chocolate, vanilla, strawberry, all in one box. That's what we did with this Edgeline. What's the value of that? Well, you can carry it, you can store it, you can serve it more conveniently, with everything together. You could have separate boxes, of chocolate, vanilla, and strawberry, that existed, right, but coming together, that convergence is key. We did that with deep compute, with data capture and control, and then systems management and Enterprise class device and systems management. And I'd like to explain why this is a product. Why would you use this product, you know, as well. Before I continue though, I want to get to the seven reasons why you would use this. And we'll go fast. But seven reasons why. But would you like to add anything about the definition of the conversion? >> Yeah, I was going to just give a little perspective, from an OT and an industrial OT kind of perspective. This world has generally lived in a silo away from IT. >> Mm-hmm. >> It's been proprietary networking standards, not been connected to the rest of the enterprise. That's the huge opportunity when we talk about the IoT, or the industrial IT, is connecting that to the rest of the enterprise. Let me give you an example. One of our customers is Duke Energy. They've implemented an online monitoring system for all of their power generation plants. They have 2,000 of our devices called CompactRIO, that connect to 30,000 sensors across all of their generation plants, getting real-time monitoring, predictive analytics, predictive failure, and it needs to have processing close to the edge, that latency issue I mentioned? They need to basically be able to do deep processing and potentially shut down a machine. Immediately if it's an a condition that warrants so. The importance here is that as those things are brought online, into IT infrastructure, the importance of deep compute, and the importance of the security and the capability that HPE has, becomes critical to our customers in the industrial internet of things. >> Well, I want to push back and just kind of play devil's advocate, and kind of poke holes in your thesis, if I can. >> Eric: Sure thing. >> So you got the probes and all the sensors and all the analog stuff that's been going on for you know, years and years, powering and instrumentation. You've got the box. So okay, I'm a customer. I have other stuff I might put in there, so I don't want to just rely on just your two stuff. Your technologies. So how do you deal with the corner case of I might have my own different devices, it's connected through IT, is that just a requirement on your end, or is that... How do you deal with the multi-vendor thing? >> It has to be an open standard. And there's two elements of open standard in this product, I'll let Tom come in on one, but one of them is, the actual IO standard, that connects to the physical world, we said it's something called PXI. National Instruments is a major vendor within this PXI market, but it is an open standard, there are 70 different vendors, thousands of products, so that part of it in connecting to the physical world, is built on an open standard, and the rest of the platform is as well. >> Indeed. Can I go back to your metaphor of the smartphone that you held up? There are times even today, but it's getting less and less, that people still carry around a camera. Or a second phone. Or a music player. Or the Beats headphones, et cetera, right? There's still time for that. So to answer your question, it's not a replacement for everything. But very frankly, the vision is over time, just like the smartphone, and the app store, more and more will get converged into this platform. So it's an introduction of a platform, we've done the inaugural convergence of the aforementioned data capture, high compute, management, storage, and we'll continue to add more and more, again, just like the smartphone analogy. And there will still be peripheral solutions around, to address your point. >> But your multi-vendor strategy if I get this right, doesn't prevent you, doesn't foreclose the customer's benefits in any way, so they connect through IT, they're connected into the box and benefits. You changed, they're just not converged inside the box. >> At this point. But I'm getting calls regularly, and you may too, Eric, of other vendors saying, I want in. I would like to relate that conceptually to the app store. Third party apps are being produced all the time that go onto this platform. And it's pretty exciting. >> And before you get to your seven killer attributes, what's the business model? So you guys have jointly engineered this product, you're jointly selling it through your channels, >> Eric: Yes. >> If you have a large customer like GE for example, who just sort of made the public commitment to HPE infrastructure. How will you guys "split the booty," so to speak? (laughter) >> Well we are actually, as Tom said we are doing reselling, we'll be reselling this through our channel, but I think one of the key things is bringing together our mutual expertise. Because when we talk about convergence of OT and IT, it's also bringing together the engineering expertise of our two companies. We really understand acquiring data from the real world, controlling industrial systems. HPE is the world leader in IT technology. And so, we'll be working together and mutually with customers to bring those two perspectives together, and we see huge opportunity in that. >> Yeah, okay so it's engineering. You guys are primarily a channel company anyway, so. >> Actually, I can make it frankly real simple, knowing that if we go back to the Neapolitan ice cream, and we reference National Instruments as chocolate, they have all the contact with the chocolate vendor, the chocolate customers if you will. We have all the vanilla. So we can go in and then pull each other that way, and then go in and pull this way, right? So that's one way as this market develops. And that's going to very powerful because indeed, the more we talk about when it used to be separated, before today, the more we're expressing that also separate customers. That the other guy does not know. And that's the key here in this relationship. >> So talk about the trend we're hearing here at the show, I mean it's been around in IT for a long time. But more now with the agility, the DevOps and cloud and everything. End to end management. Because that seems to be the table stakes. Do you address any of that in the announcement, is it part, does it fit right in? >> Absolutely, because, when we take, and we shift left, this is one of our monikers, we shift left. The data center and the cloud is on the right, and we're shifting left the data center class capabilities, out to the edge. That's why we call it shift left. And we meet, our partner National Instruments is already there, and an expert and a leader. As we shift left, we're also shifting with it, the manageability capabilities and the software that runs the management. Whether it be infrastructure, I mean I can do virtualization at the edge now, with a very popular virtualization package, I can do remote desktops like the Citrix company, the VMware company, these technologies and databases that come from our own Vertica database, that come from PTC, a great partner, with again, operations technology. Things that were running already in the data center now, get to run there. >> So you bring the benefit to the IT guy, out to the edge, to management, and Eric, you get the benefit of connecting into IT, to bring that data benefits into the business processes. >> Exactly. And as the industrial internet of things scales to billions of machines that have monitoring, and online monitoring capability, that's critical. Right, it has to be manageable. You have to be able to have these IT capabilities in order to manage such a diverse set of assets. >> Well, the big data group can basically validate that, and the whole big data thesis is, moving data where it needs to be, and having data about physical analog stuff, assets, can come in and surface more insight. >> Exactly. The biggest data of all. >> And vice versa. >> Yup. >> All right, we've got to get to the significant seven, we only have a few minutes left. >> All right. Oh yeah. >> Hit us. >> Yeah, yeah. And we're cliffhanging here on that one. But let me go through them real quick. So the question is, why wouldn't I just, you know, rudimentary collect the data, do some rudimentary analytics, send it all up to the cloud. In fact you hear that today a lot, pop-up. Censored cloud, censored cloud. Who doesn't have a cloud today? Every time you turn around, somebody's got a cloud, please send me all your data. We do that, and we do that well. We have Helion, we have the Microsoft Azure IoT cloud, we do that well. But my point is, there's a world out there. And it can be as high as 40 to 50 percent of the market, IDC is quoted as suggesting 40 percent of the data collected at the edge, by for example National Instruments, will be processed at the edge. Not sent, necessarily back to the data center or cloud, okay. With that background, there are seven reasons to not send all the data, back to the cloud. That doesn't mean you can't or you shouldn't, it just means you don't have to. There are seven reasons to compute at the edge. With an Edgeline system. Ready? >> Dave: Ready. >> We're going to go fast. And there'll be a test on this, so. >> I'm writing it down. >> Number one is latency, Eric already talked about that. How fast do you want your turnaround time? How fast would you like to know your asset's going to catch on fire? How fast would you like to know when the future autonomous car, that there's a little girl playing in the road, as opposed to a plastic bag being blown against the road, and are you going to rely on the latency of going all the way to the cloud and back, which by the way may be dropped, it's not only slow, but you ever try to make a phone call recently, and it not work, right? So you get that point. So that's latency one. You need to time to incite, time to response. Number one of seven, I'll go real quick. Number two of seven is bandwidth. If you're going to send all this big analog data, the oldest, the fastest, and the biggest of all big data, all back, you need tremendous bandwidth. And sometimes it doesn't exist, or, as some of our mutual customers tell us, it exists but I don't want to use it all for edge data coming back. That's two of seven. Three of seven is cost. If you're going to use the bandwidth, you've got to pay for it. Even if you have money to pay for it, you might not want to, so again that's three, let's go to four. (coughs) Excuse me. Number four of seven is threats. If you're going to send all the data across sites, you have threats. It doesn't mean we can't handle the threats, in fact we have the best security in the industry, with our Aruba security, ClearPass, we have ArcSight, we have Volt. We have several things. But the point is, again, it just exposes it to more threats. I've had customers say, we don't want it exposed. Anyway, that's four. Let's move on to five, is duplication. If you're going to collect all the data, and then send it all back, you're going to duplicate at the edge, you're going to duplicate not all things, but some things, both. All right, so duplication. And here we're coming up to number six. Number six is corruption. Not hostile corruption, but just package dropped. Data gets corrupt. The longer you have it in motion, e.g. back to the cloud, right, the longer it is as well. So you have corruption, you can avoid. And number three, I'm sorry, number seven, here we go with number seven. Not to send all the data back, is what we call policies and compliance, geo-fencing, I've had a customer say, I am not allowed to send all the data to these data centers or to my data scientists, because I can't leave country borders. I can't go over the ocean, as well. Now again, all these seven, create a market for us, so we can solve these seven, or at least significantly ameliorate the issues by computing at the edge with the Edgeline systems. >> Great. Eric, I want to get your final thoughts here, and as we wind down the segment. You're from the ops side, ops technologies, this is your world, it's not new to you, this edge stuff, it's been there, been there, done that, it is IoT for you, right? So you've seen the evolution of your industry. For the folks that are in IT, that HP is going to be approaching with this new category, and this new shift left, what does it mean? Share your color behind, and reasoning and reality check, on the viability. >> Sure. >> And relevance. >> Yeah, I think that there are some significant things that are driving this change. The rise of software capability, connecting these previously siloed, unconnected assets to the rest of the world, is a fundamental shift. And the cost point of acquisition technology has come down the point where we literally have a better, more compelling economic case to be made, for the online monitoring of more and more machine-type data. That example I gave of Duke Energy? Ten years ago they evaluated online monitoring, and it wasn't economical, to implement that type of a system. Today it is, and it's actually very, very compelling to their business, in terms of scheduled downtime, maintenance cost, it's a compelling value proposition. And the final one is as we deliver more analytics capability to the edge, I believe that's going to create opportunity that we don't even really, completely envision yet. And this deep computing, that the Edgeline systems have, is going to enable us to do an analysis at the edge, that we've previously never done. And I think that's going to create whole new opportunities. >> So based on your expert opinion, talk to the IT guys watching, viability, and ability to do this, what's the... Because some people are a little nervous, will the parachute open? I mean, it's a huge endeavor for an IT company to instrument the edge of their business, it's the cutting, bleeding edge, literally. What's the viability, the outcome, is it possible? >> It's here now. It is here now, I mean this announcement kind of codifies it in a new product category, but it's here now, and it's inevitable. >> Final word, your thoughts. >> Tom: I agree. >> Proud papa, you're like a proud papa now, you got your baby out there. >> It's great. But the more I tell you how wonderful the EL1000, EL4000 is, it's like my mother calling me handsome. Therefore I want to point the audience to Flowserve. F-L-O-W, S-E-R-V-E. They're one of our customers using Edgeline, and National Instruments equipment, so you can find that video online as well. They'll tell us about really the value here, and it's really powerful to hear from a customer. >> John: And availability is... >> Right now we have EL1000s and EL4000s in the hands of our customers, doing evaluations, at the end of the summer... >> John: Pre-announcement, not general availability. >> Right, general availability is not yet, but we'll have that at the end of the summer, and we can do limited availability as we call it, depending on the demand, and how we roll it out, so. >> How big the customer base is, in relevance to the... Now, is this the old boon shot box, just a quick final question. >> Tom: It is not, no. >> Really? >> We are leveraging some high-performance, low-power technology, that Intel has just announced, I'd like to shout out to that partner. They just announced and launched... Diane Bryant did her keynote to launch the new xeon, E3, low-power high-performance xeon, and it was streamed, her keynote, on the Edgeline compute engine. That's actually going into the Edgeline, that compute blade is going into the Edgeline. She streamed with it, we're pretty excited about that as well. >> Tom and Eric, thanks so much for sharing the big news, and of course congratulations, new category. >> Thank you. >> Let's see how this plays out, we'll be watching, got to get the draft picks in for this new sports league, we're calling it, like IoT, the edge, of course we're theCUBE, we're living at the edge, all the time, we're at the edge of HPE Discovery. Have one more day tomorrow, but again, three days of coverage. You're watching theCUBE, I'm John Furrier with Dave Vellante, we'll be right back. (electronic music)