>>C G technology is focused on data where we have long believed that data is in our DNA. We help maximize humanity's potential by delivering world class, precision engineered data solutions developed through sustainable and profitable partnerships. Included in our offerings are hard disk drives. As I'm sure many of you know, ah, hard drive consists of a slider also known as a drive head or transducer attached to a head gimbal assembly. I had stack assembly made up of multiple head gimbal assemblies and a drive enclosure with one or more platters, or just that the head stacked assembles into. And while the concept hasn't changed, hard drive technology has progressed well beyond the initial five megabytes, 500 quarter inch drives that Seagate first produced. And, I think 1983. We have just announced in 18 terabytes 3.5 inch drive with nine flatters on a single head stack assembly with dual head stack assemblies this calendar year, the complexity of these drives further than need to incorporate Edge analytics at operation sites, so G Edward stemming established the concept of continual improvement and everything that we do, especially in product development and operations and at the end of World War Two, he embarked on a mission with support from the US government to help Japan recover from its four time losses. He established the concept of continual improvement and statistical process control to the leaders of prominent organizations within Japan. And because of this, he was honored by the Japanese emperor with the second order of the sacred treasure for his teachings, the only non Japanese to receive this honor in hundreds of years. Japan's quality control is now world famous, as many of you may know, and based on my own experience and product development, it is clear that they made a major impact on Japan's recovery after the war at Sea Gate. The work that we've been doing and adopting new technologies has been our mantra at continual improvement. As part of this effort, we embarked on the adoption of new technologies in our global operations, which includes establishing machine learning and artificial intelligence at the edge and in doing so, continue to adopt our technical capabilities within data science and data engineering. >>So I'm a principal engineer and member of the Operations and Technology Advanced Analytics Group. We are a service organization for those organizations who need to make sense of the data that they have and in doing so, perhaps introduce a different way to create an analyzed new data. Making sense of the data that organizations have is a key aspect of the work that data scientist and engineers do. So I'm a project manager for an initiative adopting artificial intelligence methodologies for C Gate manufacturing, which is the reason why I'm talking to you today. I thought I'd start by first talking about what we do at Sea Gate and follow that with a brief on artificial intelligence and its role in manufacturing. And I'd like them to discuss how AI and machine Learning is being used at Sea Gate in developing Edge analytics, where Dr Enterprise and Cooper Netease automates deployment, scaling and management of container raised applications. So finally, I like to discuss where we are headed with this initiative and where Mirant is has a major role in case some of you are not conversant in machine learning, artificial intelligence and difference outside some definitions. To cite one source, machine learning is the scientific study of algorithms and statistical bottles without computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference Instead, thus, being seen as a subset of narrow artificial intelligence were analytics and decision making take place. The intent of machine learning is to use basic algorithms to perform different functions, such as classify images to type classified emails into spam and not spam, and predict weather. The idea and this is where the concept of narrow artificial intelligence comes in, is to make decisions of a preset type basically let a machine learn from itself. These types of machine learning includes supervised learning, unsupervised learning and reinforcement learning and in supervised learning. The system learns from previous examples that are provided, such as images of dogs that are labeled by type in unsupervised learning. The algorithms are left to themselves to find answers. For example, a Siris of images of dogs can be used to group them into categories by association that's color, length of coat, length of snout and so on. So in the last slide, I mentioned narrow a I a few times, and to explain it is common to describe in terms of two categories general and narrow or weak. So Many of us were first exposed to General Ai in popular science fiction movies like 2000 and One, A Space Odyssey and Terminator General Ai is a I that can successfully perform any intellectual task that a human can. And if you ask you Lawn Musk or Stephen Hawking, this is how they view the future with General Ai. If we're not careful on how it is implemented, so most of us hope that is more like this is friendly and helpful. Um, like Wally. The reality is that machines today are not only capable of weak or narrow, a I AI that is focused on a narrow, specific task like understanding, speech or finding objects and images. Alexa and Google Home are becoming very popular, and they can be found in many homes. Their narrow task is to recognize human speech and answer limited questions or perform simple tasks like raising the temperature in your home or ordering a pizza as long as you have already defined the order. Narrow. AI is also very useful for recognizing objects in images and even counting people as they go in and out of stores. As you can see in this example, so artificial intelligence supplies, machine learning analytics inference and other techniques which can be used to solve actual problems. The two examples here particle detection, an image anomaly detection have the potential to adopt edge analytics during the manufacturing process. Ah, common problem in clean rooms is spikes in particle count from particle detectors. With this application, we can provide context to particle events by monitoring the area around the machine and detecting when foreign objects like gloves enter areas where they should not. Image Anomaly detection historically has been accomplished at sea gate by operators in clean rooms, viewing each image one at a time for anomalies, creating models of various anomalies through machine learning. Methodologies can be used to run comparative analyses in a production environment where outliers can be detected through influence in an automated real Time analytics scenario. So anomaly detection is also frequently used in machine learning to find patterns or unusual events in our data. How do you know what you don't know? It's really what you ask, and the first step in anomaly detection is to use an algorithm to find patterns or relationships in your data. In this case, we're looking at hundreds of variables and finding relationships between them. We can then look at a subset of variables and determine how they are behaving in relation to each other. We use this baseline to define normal behavior and generate a model of it. In this case, we're building a model with three variables. We can then run this model against new data. Observations that do not fit in the model are defined as anomalies, and anomalies can be good or bad. It takes a subject matter expert to determine how to classify the anomalies on classify classification could be scrapped or okay to use. For example, the subject matter expert is assisting the machine to learn the rules. We then update the model with the classifications anomalies and start running again, and we can see that there are few that generate these models. Now. Secret factories generate hundreds of thousands of images every day. Many of these require human toe, look at them and make a decision. This is dull and steak prone work that is ideal for artificial intelligence. The initiative that I am project managing is intended to offer a solution that matches the continual increased complexity of the products we manufacture and that minimizes the need for manual inspection. The Edge Rx Smart manufacturing reference architecture er, is the initiative both how meat and I are working on and sorry to say that Hamid isn't here today. But as I said, you may have guessed. Our goal is to introduce early defect detection in every stage of our manufacturing process through a machine learning and real time analytics through inference. And in doing so, we will improve overall product quality, enjoy higher yields with lesser defects and produce higher Ma Jin's. Because this was entirely new. We established partnerships with H B within video and with Docker and Amaranthus two years ago to develop the capability that we now have as we deploy edge Rx to our operation sites in four continents from a hardware. Since H P. E. And in video has been an able partner in helping us develop an architecture that we have standardized on and on the software stack side doctor has been instrumental in helping us manage a very complex project with a steep learning curve for all concerned. To further clarify efforts to enable more a i N M l in factories. Theobald active was to determine an economical edge Compute that would access the latest AI NML technology using a standardized platform across all factories. This objective included providing an upgrade path that scales while minimizing disruption to existing factory systems and burden on factory information systems. Resource is the two parts to the compute solution are shown in the diagram, and the gateway device connects to see gates, existing factory information systems, architecture ER and does inference calculations. The second part is a training device for creating and updating models. All factories will need the Gateway device and the Compute Cluster on site, and to this day it remains to be seen if the training devices needed in other locations. But we do know that one devices capable of supporting multiple factories simultaneously there are also options for training on cloud based Resource is the stream storing appliance consists of a kubernetes cluster with GPU and CPU worker notes, as well as master notes and docker trusted registries. The GPU nodes are hardware based using H B E l 4000 edge lines, the balance our virtual machines and for machine learning. We've standardized on both the H B E. Apollo 6500 and the NVIDIA G X one, each with eight in video V 100 GP use. And, incidentally, the same technology enables augmented and virtual reality. Hardware is only one part of the equation. Our software stack consists of Docker Enterprise and Cooper Netease. As I mentioned previously, we've deployed these clusters at all of our operations sites with specific use. Case is planned for each site. Moran Tous has had a major impact on our ability to develop this capability by offering a stable platform in universal control plane that provides us, with the necessary metrics to determine the health of the Kubernetes cluster and the use of Dr Trusted Registry to maintain a secure repository for containers. And they have been an exceptional partner in our efforts to deploy clusters at multiple sites. At this point in our deployment efforts, we are on prem, but we are exploring cloud service options that include Miranda's next generation Docker enterprise offering that includes stack light in conjunction with multi cluster management. And to me, the concept of federation of multi cluster management is a requirement in our case because of the global nature of our business where our operation sites are on four continents. So Stack Light provides the hook of each cluster that banks multi cluster management and effective solution. Open source has been a major part of Project Athena, and there has been a debate about using Dr CE versus Dr Enterprise. And that decision was actually easy, given the advantages that Dr Enterprise would offer, especially during a nearly phase of development. Cooper Netease was a natural addition to the software stack and has been widely accepted. But we have also been a work to adopt such open source as rabbit and to messaging tensorflow and tensor rt, to name three good lab for developments and a number of others. As you see here, is well, and most of our programming programming has been in python. The results of our efforts so far have been excellent. We are seeing a six month return on investment from just one of seven clusters where the hardware and software cost approached close to $1 million. The performance on this cluster is now over three million images processed per day for their adoption has been growing, but the biggest challenge we've seen has been handling a steep learning curve. Installing and maintaining complex Cooper needs clusters in data centers that are not used to managing the unique aspect of clusters like this. And because of this, we have been considering adopting a control plane in the cloud with Kubernetes as the service supported by Miranda's. Even without considering, Kubernetes is a service. The concept of federation or multi cluster management has to be on her road map, especially considering the global nature of our company. Thank you.

>>live from San Francisco. It's the Q covering Micron Insight 2019. Brought to you by Micron. >>Welcome back to Pier 27 sentences. You're beautiful day here. You're watching the Cube, the leader in live tech coverage recovering micron inside 2019 hashtag micron in sight. My co host, David Floy er and I are pleased to welcome Michael Wood, Acre Cube alum and a fellow at Hewlett Packard Enterprise. Michael, good to see you again. Thanks. Coming on. >>Thanks for having me. >>So you're welcome? So you're talking about HBC on a panel today? But of course, your role inside of HP is is a wider scope. Talk about that a little bit. >>She also I'm the lead technologists in our Compute Solutions business unit that pack out Enterprise. So I've come from the group that worked on in memory computing the Superdome flex platform around things like traditional enterprise computing s it, Hannah. But I'm now responsible not only for that mission critical solutions platform, but also looking at our blades and edge line businesses. Well said broader technology. >>Okay. And then, of course, today we're talking a lot about data, the growth of data and As you say, you're sitting on a panel talking about high performance computing and the impact on science. What are you seeing? One of the big trends in terms of the intersection between data in the collision with H. P. C and science. >>So what we're seeing is just this explosion of data and this really move from traditionally science of space around how you put equations into supercomputers. Run simulations. You test your theories out, look at results. >>Come back in a couple weeks, >>exactly a potential years. Now. We're seeing a lot of work around collecting data from instruments or whether it's genomic analysis, satellite observations of the planner or of the universe. These aerial generating data in vast quantities, very high rates. And so we need to rethink how we're doing our science to gain insights from this massive data increase with seeing, >>you know, when we first started covering the 10th year, the Cuban So in 2010 if you could look at the high performance computing market as sort of an indicator of some of the things that were gonna happen in so called big data, and some of those things have played out on I think it probably still is a harbinger. I wonder, how are you seeing machine intelligence applied to all this data? And what can we learn from that? In your opinion, in terms of its commercial applications. >>So a CZ we'll know this massive data explosion is how do we gain insights from this data? And so, as I mentioned, we serve equations of things like computational fluid dynamics. But now things are progressing, so we need to use other techniques to gain understanding. And so we're using artificial intelligence and particularly today, deep learning techniques to basically gain insights from the state of Wei. Don't have equations that we can use to mind this information. So we're using these aye aye techniques to effectively generate the algorithms that can. Then you bring patterns of interest to our you know, focused of them, really understand what is the scientific phenomenon that's driving the things particular pattern we're seeing within the data? So it's just beyond the ability of the number of HPC programmers, we have the sort of traditional equation based methodologies algorithms to gain insight. We're moving into this world where way just have outstripped knowledge and capabilities to gain insight. >>So So how does that? How is that being made possible? What are the differences in the architecture that you've had to put in, for example, to make this sort of thing possible? >>Yeah, it's it's really interesting time, actually, a few years ago seemed like computing was starting to get boring because wears. Now we've got this explosion of new hardware devices being built, basically moving into the more of a hetero genius. Well, because we have this expo exponential growth of data. But traditional computing techniques are slowing down, so people are looking at exaggerate er's to close that gap and all sorts of hatred genius devices. So we've really been thinking. How do we change that? The whole computing infrastructure to move from a compute centric world to a memory centric world? And how can we use memory driven computing techniques to close that gap to gain insight, so kind of rethinking the whole architectural direction basically merge, sort of collapsing down the traditional hierarchy you have, from storage to memory to the CPU to get rid of the legacy bottlenecks in converting protocols from process of memory storage down to just a simple basically memory driven architecture where you have access to the entire data set you're looking at, which could be many terabytes to pad of eyes to exabytes that you can do simple programming. Just directly load store to that huge data set to gain insights. So that's that's really changed. >>Fascinating, isn't it? So it's the Gen Z. The hope of Gen Z is actually taking place now. >>Yes, so Gen Z is an industry led consulting around a memory fabric and the, you know, Hewlett Packard Enterprise Onda whole host of industry partners, a part of the ecosystem looking at building a memory fabric where people can bring different innovations to operate, whether it's processing types, memory types, that having that common infrastructure. I mean, there's other work to in the industry the Compute Express Link Consortium. So there's a lot of interest now in getting memory semantics out of the process, er into a common fabric for people to innovate. >>Do you have some examples of where this is making a difference now, from from the work in the H B and your commercial work? >>Certainly. Yeah, we're working with customers in areas like precision medicine, genomex basically exaggerating the ability to gain insights into you know what medical pathway to go on for a particular disease were working in cybersecurity. Looking at how you know, we're worried about security of our data and things like network intrusion. So we're looking at How can you gain insights not only into known attacking patterns on a network that the unknown patents that just appearing? So we're actually a flying machine learning techniques on sort of graft data to understand those things. So there's there's really a very broad spectrum where you can apply these techniques to Data Analytics >>are all scientists now, data scientists. And what's the relationship between sort of a classic data scientist, where you think of somebody with stats and math and maybe a little bit of voting expertise and a scientist that has much more domain expertise you're seeing? You see, data scientists sort of traversed domains. How are those two worlds coming together? >>It's funny you mentioned I had that exact conversation with one of the members of the Cosmos Group in Cambridge is the Stephen Hawking's cosmology team, and he said, actually, he realized a couple of years ago, maybe he should call himself a day two scientists not cosmologist, because it seemed like what he was doing was exactly what you said. It's all about understanding their case. They're taking their theoretical ideas about the early universe, taking the day to measurements from from surveys of the sky, the background, the cosmic background radiation and trying to pair these together. So I think your data science is tremendously important. Right now. Thio exhilarate you as they are insights into data. But it's not without you can't really do in isolation because a day two scientists in isolation is just pointing out peaks or troughs trends. But how do you relate that to the underlying scientific phenomenon? So you you need experts in whatever the area you're looking at data to work with, data scientists to really reach that gap. >>Well, with all this data and all this performance, computing capacity and almost all its members will be fascinating to see what kind of insights come out in the next 10 years. Michael, thanks so much for coming on. The Cube is great to have you. >>Thank you very much. >>You're welcome. And thank you for watching. Everybody will be right back at Micron Insight 2019 from San Francisco. You're watching the Cube

>> Narrator: Live from Las Vegas, it's theCUBE. Covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back everyone to theCUBE's live coverage of ServiceNow Knowledge18. We're coming at you from Las Vegas, I'm your host, Rebecca Knight, along with my cohost Dave Vellante, we are theCUBE, we are the leader in live tech coverage. We are joined by Sebastiaan Laurijsse, he is the global senior director, IT, cyber security, digital transformations at NXP, thanks so much for coming on theCUBE Sebastiaan. >> Thank you for having me. >> Good to see you. >> Thank you. >> So I want to start out by asking you a little bit about NXP, what you do and then what your company does and then also what you do there. >> NXP is the leading semiconductors in providing products for automotive and our company vision is providing a sure connections and infrastructures for a smart world. And that's what we are trying to achieve by implementing new ways of working with making the world more autonomous, like autonomous driving et cetera, so that's really what we're trying to do. >> Dave: Cool company. >> We are really building the future of tomorrow. >> Yeah. >> Big, large company too right? >> Yeah. Roughly about 36 thousand employees currently. >> Wow, okay, yeah. >> So you said you're really building the future of tomorrow, unpack that a bit, tell our viewers exactly what you're doing there. >> So today what you have experienced also on this event is a lot about artificial intelligence and machine learning. NXP has been elected as the number three in the world as the provider of solutions for artificial intelligence. So if you really think what we are developing today, it's already started and will become available in five or three years from now. So it's, you only can imagine what the future brings us and what we will shape. >> When do you think owning your own car and driving your own car will become and exception? >> Driving your own car, you won't own a car anymore. It will be some kind of help that comes to your home on demand when you need it and it even predicts when you like to travel and then it comes by automatically. >> How far away is that, you think it's two decades? >> Nah I think here it's not about technology, I think we have the technology to even enable it today. >> Dave: It's policy. >> It's policy, regulation, compliancy that doesn't allow to lets go harvest all data to make the right decisions there. >> We had the insurance company on the other day and they were like, no we're going to figure this out. >> Out of necessity. >> We always figure this stuff out. >> Yeah it's really not about technology anymore, it's really about legal, what prevents us access the data to make the right decisions, right. >> It's amazing though just to watch the progression of automotive, I mean they're basically software defined vehicles now I mean how many semiconductors are in a car now? >> Yeah but also you can clearly see within that experience, we are transforming our business to more software because developing a product as hardware that needs to stay in for 15 years or longer if you look to a car. Then you would like to have the ability to be dynamic more on top of the product by using software so also our products are becoming software defined. >> So you're a very R and D centric culture. >> Sebastiaan: Yes. >> Maybe talk about that ethos and the cultural aspects, and maybe what the process looks like, share with our viewers. >> I think it's the most awesome part of the company. Of course we also manufacture our products but mainly R and D is so dynamic, we have so, tech savvy people and we have so much issues as IT and you think what are they consuming so much bandwidth on Netflix and then they tell me hey we are developing a product for 4K entertainment into the car. So I have an issue on my wider network, you're providing all kinds of services but you're building for entertainment into the car for the future. >> That car better be autonomous. >> Exactly. >> Yes. >> That's for the kids in the back seat I think. >> Yes. >> You once described ServiceNow as the platform of platforms can you talk a little bit about that from your R and D process? >> So what you clearly see and also I think that all companies will eventually become an IT company, yeah? Also the banking companies tell us now today they are an IT company with a banking license. What I truly believe in is that we need to close the gap between IT and the business so I think the future model is that IT will dissolve for a certain part into the business. But you don't want to have, of course you still have you shared services, you still have a hybrid model where you have the countries where you're providing support from, so you're not always as close to the business. You have 24 seven economy and you need to provide those services and what you don't want to build is human interfaces. So what you try to achieve by building the platform of platforms, the fabric is that you try to connect the business acumen, the business dynamics, the project management tools that requires management into the IT systems and since you can detect the phase where they are in if they are facing issues with their products the projects are slipping or delaying, you would like to increase automatically the severity of the incidents. So that they can automatically solve and you have a better understanding of the business priorities. >> NXP is really interesting because you're at the intersection of a lot of big trends. I'm mean you're a hardware-- >> Sebastiaan: IOT. >> You're hardware manufacturers, you're a software developer, security, AI, IOT and underlying all this is data. >> Yeah, the new money. >> Yeah, right so I'm just envisioning this pretty complicated matrix, I'm wondering if you could describe that in your terms. >> If you look from an IT infrastructure perspective the growth on data is enormous. To cope with that growth because the data allows us to make better products. Data could be a requirement but could be also the affect of the results. What we tried to prevent, the project in bringing to the real life that you feel your requirement of quality is increasing. We had consumer great, automotive great, and we had for the flying industry, also the same great. But however your norm is increasing, so what you clearly see by increasing the norm, we call that the total quality culture, you also would like to have a total quality product, you don't want to replace your phone one year from now and I think if you look four years back, a phone, one and a half years, two years and then you had a new one. But as products become more expensive, they become more part of your daily life, part of your personal brand even and it generates that data, we need, if you try to work on proper quality that will generate an enormous amount of data. But a data can use, you optimize your processes upfront in the future as such it becomes more cost efficient to develop new products. So it's really about the conditioning for more data is also conditionally need to optimize your processes. >> Where does ServiceNow fit in to all this? How do you use ServiceNow? >> So for me what you really see in ServiceNow today is the best work flow engine you can imagine. It really orchestrates all IT and connecting business processes. And I think the potential and I think if you look into the portfolio where they have HR, it's going beyond IT and now they often, as already said by John Donahue, they come in via the IT angle, ITSM but as the process become more and more part of your culture rather than inhabit a forced way of working then the platform starts supporting the culture of your organization because by machine learning a proper UI, visualization capabilities it becomes really part about metering, showing what you're doing and really helps you to orchestrate your daily work and that's also I think of the new company, it's a little too difficult to pronounce, have you ever, it's about orchestrating the future way of working. >> So we're hearing so much about this, making the world of work work better for people, you describe it as a work flow engine, really helping employees organize their work days, orchestrate their work days, improve them, can you describe the culture at NXP and sort of how ServiceNow is improving employees everyday lives. >> What we really try to do and it's also what we see it's easy to show the cost efficiency savings you have from a platform as ServiceNow. If you improve your onboarding by optimizing the process by three days, because that's your first point of engagement when you bring some people on board and if it goes fluently, work integration with ServiceNow providing the services, everything is ready at day one. Day one you're there, your laptop is ready, your provisions, your desk is ready, and you have orchestrated a process that's a flawless end user experience. And that's what we would like to provide with ServiceNow, orchestrate with ServiceNow, because that's what the uses is. If it's a need of any of the help of services, we would like them to go, shift left to ServiceNow and with help of knowledge help themselves. We are all doctor Google and we would like to have access to that information ourselves and not be dependent by the expert, we all become that expert. >> Are employees happier? I mean I think that's a question too. Because we know that from research that happier employees make more productive >> Are more productive. Workplaces. They're more likely to stay, recommend it to their friends and the network gets bigger, I mean what's your-- >> If you have a company that shapes the future, we have very happy employees. (laughing) >> Self fulfilling prophecy there. >> Yes. >> When did you go live with? >> So we are one of the first adopters in 2007 in Europe. So we really started then, I don't know the name because they talk about days, months and now they talk about locations. (laughing) But I think we did a big overhaul during some of our big integrations that we have done so we are really one of the first customers in Europe providing the product. >> And how far, where, what version you in now? >> We are ready to upgrade, we will skip one release if we go to-- >> It's coming to London. >> Yep, London. >> Oh okay. And you started with ITSM like most? >> ITSM, ITOM, so IT operation management and now we have the IT business management app like demand management, IT financial management, really orchestrating from demand to fulfilling. >> A lot of our guys have written that they feel like machine intelligence and ITOM go together very well. >> Yes. >> You agree with that and how do you see that affecting your business? >> So what we clearly see is that the mean time to detect, the mean time to repair, we would like to detect algae before they hit the end user. So you really would like to make sure that before they notice it's already been solved. Or when it goes wrong, they already say we're on top of it, we know, we know the impact, we know that the whole chain of events, a single network port or power outage somewhere in a room could cause a big effect on the whole IT service and therefore research now helps us to make sure that we are on top of the things. >> Sebastiaan you mentioned off camera that you are very intimately involved with ServiceNow and helping them with their roadmap, providing feedback so can you share with us some of the things that you talk about with them and what would you like to see, where's their white space, what's on their to do list from your perspective? >> So what, but of course, if you look to our portfolio, what we are doing as NXP. So a member of the product advisory council for IT operation management and I'm closely working also on the Lighthouse program with ServiceNow and all kind of new releases, what I really think if you see what you are investing, of course they are now coming forward with the chatbots, awesome but if I see how my children consume information, using YouTube and I think also John touched upon it, what we are building as NXP is in the flawless end user experience and everything as being you don't have a UI. If you look to your car, today you have a speedometer, an RPM meter, why do you have RPM on your dashboard, why? What's the value of you know? In the past you needed it to shift gears and why is it still there? Does it really add value? >> Cause it's cool. (laughing) We love dials, come on. >> So it's about the end user experience, it's about your lifestyle, your brand identity it's not as more about requirements so, of course UI is important, I believe it, what's more important I think to invest in that engine behind it machine learning, artificial intelligence and how to ingest data. So because what is really required to make smart decisions is a lot of data and still I think the platform has potential, but there's some room for improvement to get proper integration by onboarding more data making the right decisions and orchestrate the actions out of it and I think the learn think act, we have the same strategy as sense, think, act at NXP I think that's how robotics and AI will work in the future. >> Data is the fuel for your innovation. >> Yes. >> So it's a great point you're making. >> I wonder if you could talk a little bit about the feelings in Europe, you're based in the Netherlands, about automation and the future of jobs because in the United States there is a significant anxiety about the machines coming for our jobs and at least the media portray it that way and I'm curious from your perspective, what is the feeling in Europe? >> Of course I think I see the opportunity but automation will change of course, automation, machine learning, it will essentially change the whole way of working. Because what we say it's about helping the business by decision automation, making decisions so we try to reduce the human effort, we have a total equality culture but we still need more and more people to help them that ask the right questions. Because the innovation of course come from a lot of data But still have people who connect the dots of never existing connections before. If you have a lot of data and you don't know which questions to ask, would you build a new solution? So it's still about smart people and creativity and of course we know patterns, we know what people are doing. But still the real breakthroughs is being done by people and therefore we need those people still in the future. So the anxiety is there yes, automation is there but I think it's about building a joint incentive between your outsource provider, your source provider between your workforce is what's the incentive for them on automation because otherwise you get a culture of fear and anxiety and a lot of doubt and that will be counterproductive for your company value. >> What do you think as a journalist. I mean you're right, the mainstream media talks about this a lot and they're actually accurate, the data is there to suggest that machines are replacing humans and cognitive functions and that's a concern but there's not a lot written in the media about the opportunity, there is some about the opportunities but more importantly what to do about it, in other words, public policy, education, I mean maybe I'm just missing it but. >> No, I agree with you, I completely agree and also this idea that Sebastiaan is bringing up is showing, proving that this can work for you, I mean this is actually going to improve your work life by taking Carol out of the drudge work or show opportunities for humans and robots to work alongside of each other. >> Yes. >> Rebecca: So there you go. >> Well in tech you better be an optimist you know. >> It's true. >> Although it seems like Musk and Stephen Hawking weren't optimists but maybe they're thinking you know hundreds of years-- >> Light years ahead. >> Right, right, right, right. You report directly to the CIO, at this conference, we're hearing so much about the changing role of the CIO and how the CIO has to be thinking so much more broadly about the business than ever before I mean how do you see it? >> So that's an interesting question because that's exactly where we are in today so we have had the classic way of the CIO, financial risk control et cetera then we have the transforminal CIO, then we have the CDO, or we have the future COO who takes care of operations because today IT is often being seen in the enterprise companies as a shared service center, something you do with the lights off but clearly bank accounts, what I already told you before was we are now IT companies with a banking license as IT becomes more dominant, it becomes part of operations and yes, we need a transformational CIO, CDO or a new type of COO that sees IT as part of the operations and the way of working. And of course you can give the new title, but at the end it's just a smart guy who helps the company succeed and brings IT as one together to make success. It's not about the role or responsibility, I think there's still the name of a chief information, chief data officer it's still the right title because he makes sure he gets the right data towards the business to make the right decisions faster. >> Right, great. >> It's not about running only the lights on. When the lights doesn't go on, it's IT's fault, right? >> Rebecca: Always, always. >> Always. >> Yeah that need doesn't go away but it's table stakes now. >> Exactly, Sebastiaan, thanks so much for coming on theCUBE, it was a pleasure having you here. >> Thank you. >> I'm Rebecca Knight, for Dave Vallante we will have more from theCUBE's live coverage of ServiceNow Knowledge18 coming up just after this. (upbeat music)

[Music] [Music] [Music] [Music] ladies and gentlemen please welcome Red Hat senior vice president of engineering Matt Hicks [Music] welcome back I hope you're enjoying your first day of summit you know for us it is a lot of work throughout the year to get ready to get here but I love the energy walking into someone on that first opening day now this morning we kick off with Paul's keynote and you saw this morning just how evolved every aspect of open hybrid cloud has become based on an open source innovation model that opens source the power and potential of open source so we really brought me to Red Hat but at the end of the day the real value comes when were able to make customers like yourself successful with open source and as much passion and pride as we put into the open source community that requires more than just Red Hat given the complexity of your various businesses the solution set you're building that requires an entire technology ecosystem from system integrators that can provide the skills your domain expertise to software vendors that are going to provide the capabilities for your solutions even to the public cloud providers whether it's on the hosting side or consuming their services you need an entire technological ecosystem to be able to support you and your goals and that is exactly what we are gonna talk about this afternoon the technology ecosystem we work with that's ready to help you on your journey now you know this year's summit we talked about earlier it is about ideas worth exploring and we want to make sure you have all of the expertise you need to make those ideas a reality so with that let's talk about our first partner we have him today and that first partner is IBM when I talk about IBM I have a little bit of a nostalgia and that's because 16 years ago I was at IBM it was during my tenure at IBM where I deployed my first copy of Red Hat Enterprise Linux for a customer it's actually where I did my first professional Linux development as well you and that work on Linux it really was the spark that I had that showed me the potential that open source could have for enterprise customers now iBM has always been a steadfast supporter of Linux and a great Red Hat partner in fact this year we are celebrating 20 years of partnership with IBM but even after 20 years two decades I think we're working on some of the most innovative work that we ever have before so please give a warm welcome to Arvind Krishna from IBM to talk with us about what we are working on Arvind [Applause] hey my pleasure to be here thank you so two decades huh that's uh you know I think anything in this industry to going for two decades is special what would you say that that link is made right Hatton IBM so successful look I got to begin by first seeing something that I've been waiting to say for years it's a long strange trip it's been and for the San Francisco folks they'll get they'll get the connection you know what I was just thinking you said 16 it is strange because I probably met RedHat 20 years ago and so that's a little bit longer than you but that was out in Raleigh it was a much smaller company and when I think about the connection I think look IBM's had a long long investment and a long being a long fan of open source and when I think of Linux Linux really lights up our hardware and I think of the power box that you were showing this morning as well as the mainframe as well as all other hardware Linux really brings that to life and I think that's been at the root of our relationship yeah absolutely now I alluded to a little bit earlier we're working on some new stuff and this time it's a little bit higher in the software stack and we have before so what do you what would you say spearheaded that right so we think of software many people know about some people don't realize a lot of the words are called critical systems you know like reservation systems ATM systems retail banking a lot of the systems run on IBM software and when I say IBM software names such as WebSphere and MQ and db2 all sort of come to mind as being some of that software stack and really when I combine that with some of what you were talking about this morning along hybrid and I think this thing called containers you guys know a little about combining the two we think is going to make magic yeah and I certainly know containers and I think for myself seeing the rise of containers from just the introduction of the technology to customers consuming at mission-critical capacities it's been probably one of the fastest technology cycles I've ever seen before look we completely agree with that when you think back to what Paul talks about this morning on hybrid and we think about it we are made of firm commitment to containers all of our software will run on containers and all of our software runs Rell and you put those two together and this belief on hybrid and containers giving you their hybrid motion so that you can pick where you want to run all the software is really I think what has brought us together now even more than before yeah and the best part I think I've liked we haven't just done the product in downstream alignment we've been so tied in our technology approach we've been aligned all the way to the upstream communities absolutely look participating upstream participating in these projects really bringing all the innovation to bear you know when I hear all of you talk about you can't just be in a single company you got to tap into the world of innovation and everybody should contribute we firmly believe that instead of helping to do that is kind of why we're here yeah absolutely now the best part we're not just going to tell you about what we're doing together we're actually going to show you so how every once you tell the audience a little bit more about what we're doing I will go get the demo team ready in the back so you good okay so look we're doing a lot here together we're taking our software and we are begging to put it on top of Red Hat and openshift and really that's what I'm here to talk about for a few minutes and then we go to show it to you live and the demo guard should be with us so it'll hopefully go go well so when we look at extending our partnership it's really based on three fundamental principles and those principles are the following one it's a hybrid world every enterprise wants the ability to span across public private and their own premise world and we got to go there number two containers are strategic to both of us enterprise needs the agility you need a way to easily port things from place to place to place and containers is more than just wrapping something up containers give you all of the security the automation the deploy ability and we really firmly believe that and innovation is the path forward I mean you got to bring all the innovation to bear whether it's around security whether it's around all of the things we heard this morning around going across multiple infrastructures right the public or private and those are three firm beliefs that both of us have together so then explicitly what I'll be doing here number one all the IBM middleware is going to be certified on top of openshift and rel and through cloud private from IBM so that's number one all the middleware is going to run in rental containers on OpenShift on rail with all the cloud private automation and deployability in there number two we are going to make it so that this is the complete stack when you think about from hardware to hypervisor to os/2 the container platform to all of the middleware it's going to be certified up and down all the way so that you can get comfort that this is certified against all the cyber security attacks that come your way three because we do the certification that means a complete stack can be deployed wherever OpenShift runs so that way you give the complete flexibility and you no longer have to worry about that the development lifecycle is extended all the way from inception to production and the management plane then gives you all of the delivery and operation support needed to lower that cost and lastly professional services through the IBM garages as well as the Red Hat innovation labs and I think that this combination is really speaks to the power of both companies coming together and both of us working together to give all of you that flexibility and deployment capabilities across one can't can't help it one architecture chart and that's the only architecture chart I promise you so if you look at it right from the bottom this speaks to what I'm talking about you begin at the bottom and you have a choice of infrastructure the IBM cloud as well as other infrastructure as a service virtual machines as well as IBM power and IBM mainframe as is the infrastructure choices underneath so you choose what what is best suited for the workload well with the container service with the open shift platform managing all of that environment as well as giving the orchestration that kubernetes gives you up to the platform services from IBM cloud private so it contains the catalog of all middle we're both IBM's as well as open-source it contains all the deployment capability to go deploy that and it contains all the operational management so things like come back up if things go down worry about auto scaling all those features that you want come to you from there and that is why that combination is so so powerful but rather than just hear me talk about it I'm also going to now bring up a couple of people to talk about it and what all are they going to show you they're going to show you how you can deploy an application on this environment so you can think of that as either a cloud native application but you can also think about it as how do you modernize an application using micro services but you don't want to just keep your application always within its walls you also many times want to access different cloud services from this and how do you do that and I'm not going to tell you which ones they're going to come and tell you and how do you tackle the complexity of both hybrid data data that crosses both from the private world to the public world and as well as target the extra workloads that you want so that's kind of the sense of what you're going to see through through the demonstrations but with that I'm going to invite Chris and Michael to come up I'm not going to tell you which one's from IBM which runs from Red Hat hopefully you'll be able to make the right guess so with that Chris and Michael [Music] so so thank you Arvind hopefully people can guess which ones from Red Hat based on the shoes I you know it's some really exciting stuff that we just heard there what I believe that I'm I'm most excited about when I look out upon the audience and the opportunity for customers is with this announcement there are quite literally millions of applications now that can be modernized and made available on any cloud anywhere with the combination of IBM cloud private and OpenShift and I'm most thrilled to have mr. Michael elder a distinguished engineer from IBM here with us today and you know Michael would you maybe describe for the folks what we're actually going to go over today absolutely so when you think about how do I carry forward existing applications how do I build new applications as well you're creating micro services that always need a mixture of data and messaging and caching so this example application shows java-based micro services running on WebSphere Liberty each of which are then leveraging things like IBM MQ for messaging IBM db2 for data operational decision manager all of which is fully containerized and running on top of the Red Hat open chip container platform and in fact we're even gonna enhance stock trader to help it understand how you feel but okay hang on so I'm a little slow to the draw sometimes you said we're gonna have an application tell me how I feel exactly exactly you think about your enterprise apps you want to improve customer service understanding how your clients feel can't help you do that okay well this I'd like to see that in action all right let's do it okay so the first thing we'll do is we'll actually take a look at the catalog and here in the IBM cloud private catalog this is all of the content that's available to deploy now into this hybrid solution so we see workloads for IBM will see workloads for other open source packages etc each of these are packaged up as helm charts that are deploying a set of images that will be certified for Red Hat Linux and in this case we're going to go through and start with a simple example with a node out well click a few actions here we'll give it a name now do you have your console up over there I certainly do all right perfect so we'll deploy this into the new old namespace and will deploy notate okay alright anything happening of course it's come right up and so you know what what I really like about this is regardless of if I'm used to using IBM clout private or if I'm used to working with open shift yeah the experience is well with the tool of whatever I'm you know used to dealing with on a daily basis but I mean you know I got to tell you we we deployed node ourselves all the time what about and what about when was the last time you deployed MQ on open shift you never I maybe never all right let's fix that so MQ obviously is a critical component for messaging for lots of highly transactional systems here we'll deploy this as a container on the platform now I'm going to deploy this one again into new worlds I'm gonna disable persistence and for my application I'm going to need a queue manager so I'm going to have it automatically setup my queue manager as well now this will deploy a couple of things what do you see I see IBM in cube all right so there's your stateful set running MQ and of course there's a couple of other components that get stood up as needed here including things like credentials and secrets and the service etc but all of this is they're out of the box ok so impressive right but that's the what I think you know what I'm really looking at is maybe how a well is this running you know what else does this partnership bring when I look at IBM cloud private windows inches well so that's a key reason about why it's not just about IBM middleware running on open shift but also IBM cloud private because ultimately you need that common management plane when you deploy a container the next thing you have to worry about is how do I get its logs how do I manage its help how do I manage license consumption how do I have a common security plan right so cloud private is that enveloping wrapper around IBM middleware to provide those capabilities in a common way and so here we'll switch over to our dashboard this is our Griffin and Prometheus stack that's deployed also now on cloud private running on OpenShift and we're looking at a different namespace we're looking at the stock trader namespace we'll go back to this app here momentarily and we can see all the different pieces what if you switch over to the stock trader workspace on open shipped yeah I think we might be able to do that here hey there it is alright and so what you're gonna see here all the different pieces of this op right there's d b2 over here I see the portfolio Java microservice running on Webster Liberty I see my Redis cash I see MQ all of these are the components we saw in the architecture picture a minute ago ya know so this is really great I mean so maybe let's take a look at the actual application I see we have a fine stock trader app here now we mentioned understanding how I feel exactly you know well I feel good that this is you know a brand new stock trader app versus the one from ten years ago that don't feel like we used forever so the key thing is this app is actually all of those micro services in addition to things like business rules etc to help understand the loyalty program so one of the things we could do here is actually enhance it with a a AI service from Watson this is tone analyzer it helps me understand how that user actually feels and will be able to go through and submit some feedback to understand that user ok well let's see if we can take a look at that so I tried to click on youth clearly you're not very happy right now here I'll do one quick thing over here go for it we'll clear a cache for our sample lab so look you guys don't actually know as Michael and I just wrote this no js' front end backstage while Arvin was actually talking with Matt and we deployed it real-time using continuous integration and continuous delivery that we have available with openshift well the great thing is it's a live demo right so we're gonna do it all live all the time all right so you mentioned it'll tell me how I'm feeling right so if we look at so right there it looks like they're pretty angry probably because our cache hadn't been cleared before we started the demo maybe well that would make me angry but I should be happy because I mean I have a lot of money well it's it's more than I get today for sure so but you know again I don't want to remain angry so does Watson actually understand southern I know it speaks like eighty different languages but well you know I'm from South Carolina to understand South Carolina southern but I don't know about your North Carolina southern alright well let's give it a go here y'all done a real real know no profanity now this is live I've done a real real nice job on this here fancy demo all right hey all right likes me now all right cool and the key thing is just a quick note right it's showing you've got a free trade so we can integrate those business rules and then decide to I do put one trade if you're angry give me more it's all bringing it together into one platform all running on open show yeah and I can see the possibilities right of we've not only deployed services but getting that feedback from our customers to understand well how well the services are being used and are people really happy with what they have hey listen Michael this was amazing I read you joining us today I hope you guys enjoyed this demo as well so all of you know who this next company is as I look out through the crowd based on what I can actually see with the sun shining down on me right now I can see their influence everywhere you know Sports is in our everyday lives and these guys are equally innovative in that space as they are with hybrid cloud computing and they use that to help maintain and spread their message throughout the world of course I'm talking about Nike I think you'll enjoy this next video about Nike and their brand and then we're going to hear directly from my twitting about what they're doing with Red Hat technology new developments in the top story of the day the world has stopped turning on its axis top scientists are currently racing to come up with a solution everybody going this way [Music] the wrong way [Music] please welcome Nike vice president of infrastructure engineering Mike witig [Music] hi everybody over the last five years at Nike we have transformed our technology landscape to allow us to connect more directly to our consumers through our retail stores through Nike comm and our mobile apps the first step in doing that was redesigning our global network to allow us to have direct connectivity into both Asia and AWS in Europe in Asia and in the Americas having that proximity to those cloud providers allows us to make decisions about application workload placement based on our strategy instead of having design around latency concerns now some of those workloads are very elastic things like our sneakers app for example that needs to burst out during certain hours of the week there's certain moments of the year when we have our high heat product launches and for those type of workloads we write that code ourselves and we use native cloud services but being hybrid has allowed us to not have to write everything that would go into that app but rather just the parts that are in that application consumer facing experience and there are other back-end systems certain core functionalities like order management warehouse management finance ERP and those are workloads that are third-party applications that we host on relevent over the last 18 months we have started to deploy certain elements of those core applications into both Azure and AWS hosted on rel and at first we were pretty cautious that we started with development environments and what we realized after those first successful deployments is that are the impact of those cloud migrations on our operating model was very small and that's because the tools that we use for monitoring for security for performance tuning didn't change even though we moved those core applications into Azure in AWS because of rel under the covers and getting to the point where we have that flexibility is a real enabler as an infrastructure team that allows us to just be in the yes business and really doesn't matter where we want to deploy different workload if either cloud provider or on-prem anywhere on the planet it allows us to move much more quickly and stay much more directed to our consumers and so having rel at the core of our strategy is a huge enabler for that flexibility and allowing us to operate in this hybrid model thanks very much [Applause] what a great example it's really nice to hear an IQ story of using sort of relish that foundation to enable their hybrid clout enable their infrastructure and there's a lot that's the story we spent over ten years making that possible for rel to be that foundation and we've learned a lot in that but let's circle back for a minute to the software vendors and what kicked off the day today with IBM IBM s one of the largest software portfolios on the planet but we learned through our journey on rel that you need thousands of vendors to be able to sport you across all of your different industries solve any challenge that you might have and you need those vendors aligned with your technology direction this is doubly important when the technology direction is changing like with containers we saw that two years ago bread had introduced our container certification program now this program was focused on allowing you to identify vendors that had those shared technology goals but identification by itself wasn't enough in this fast-paced world so last year we introduced trusted content we introduced our container health index publicly grading red hats images that form the foundation for those vendor images and that was great because those of you that are familiar with containers know that you're taking software from vendors you're combining that with software from companies like Red Hat and you are putting those into a single container and for you to run those in a mission-critical capacity you have to know that we can both stand by and support those deployments but even trusted content wasn't enough so this year I'm excited that we are extending once again to introduce trusted operations now last week we announced that cube con kubernetes conference the kubernetes operator SDK the goal of the kubernetes operators is to allow any software provider on kubernetes to encode how that software should run this is a critical part of a container ecosystem not just being able to find the vendors that you want to work with not just knowing that you can trust what's inside the container but knowing that you can efficiently run that software now the exciting part is because this is so closely aligned with the upstream technology that today we already have four partners that have functioning operators specifically Couchbase dynaTrace crunchy and black dot so right out of the gate you have security monitoring data store options available to you these partners are really leading the charge in terms of what it means to run their software on OpenShift but behind these four we have many more in fact this morning we announced over 60 partners that are committed to building operators they're taking their domain expertise and the software that they wrote that they know and extending that into how you are going to run that on containers in environments like OpenShift this really brings the power of being able to find the vendors being able to trust what's inside and know that you can run their software as efficiently as anyone else on the planet but instead of just telling you about this we actually want to show you this in action so why don't we bring back up the demo team to give you a little tour of what's possible with it guys thanks Matt so Matt talked about the concept of operators and when when I think about operators and what they do it's taking OpenShift based services and making them even smarter giving you insight into how they do things for example have we had an operator for the nodejs service that I was running earlier it would have detected the problem and fixed itself but when we look at it what really operators do when I look at it from an ecosystem perspective is for ISVs it's going to be a catalyst that's going to allow them to make their services as manageable and it's flexible and as you know maintainable as any public cloud service no matter where OpenShift is running and to help demonstrate this I've got my buddy Rob here Rob are we ready on the demo front we're ready awesome now I notice this screen looks really familiar to me but you know I think we want to give folks here a dev preview of a couple of things well we want to show you is the first substantial integration of the core OS tectonic technology with OpenShift and then the other thing is we are going to dive in a little bit more into operators and their usefulness so Rob yeah so what we're looking at here is the service catalog that you know and love and openshift and we've got a few new things in here we've actually integrated operators into the Service Catalog and I'm going to take this filter and give you a look at some of them that we have today so you can see we've got a list of operators exposed and this is the same way that your developers are already used to integrating with products they're right in your catalog and so now these are actually smarter services but how can we maybe look at that I mentioned that there's maybe a new view I'm used to seeing this as a developer but I hear we've got some really cool stuff if I'm the administrator of the console yeah so we've got a whole new side of the console for cluster administrators to get a look at under the infrastructure versus this dev focused view that we're looking at today today so let's go take a look at it so the first thing you see here is we've got a really rich set of monitoring and health status so we can see that we've got some alerts firing our control plane is up and we can even do capacity planning anything that you need to do to maintenance your cluster okay so it's it's not only for the the services in the cluster and doing things that you know I may be normally as a human operator would have to do but this this console view also gives me insight into the infrastructure itself right like maybe the nodes and maybe handling the security context is that true yes so these are new capabilities that we're bringing to open shift is the ability to do node management things like drain and unscheduled nodes to do day-to-day maintenance and then as well as having security constraints and things like role bindings for example and the exciting thing about this is this is a view that you've never been able to see before it's cross-cutting across namespaces so here we've got a number of admin bindings and we can see that they're connected to a number of namespaces and these would represent our engineering teams all the groups that are using the cluster and we've never had this view before this is a perfect way to audit your security you know it actually is is pretty exciting I mean I've been fortunate enough to be on the up and shift team since day one and I know that operations view is is something that we've you know strived for and so it's really exciting to see that we can offer that now but you know really this was a we want to get into what operators do and what they can do for us and so maybe you show us what the operator console looks like yeah so let's jump on over and see all the operators that we have installed on the cluster you can see that these mirror what we saw on the Service Catalog earlier now what we care about though is this Couchbase operator and we're gonna jump into the demo namespace as I said you can share a number of different teams on a cluster so it's gonna jump into this namespace okay cool so now what we want to show you guys when we think about operators you know we're gonna have a scenario here where there's going to be multiple replicas of a Couchbase service running in the cluster and then we're going to have a stateful set and what's interesting is those two things are not enough if I'm really trying to run this as a true service where it's highly available in persistent there's things that you know as a DBA that I'm normally going to have to do if there's some sort of node failure and so what we want to demonstrate to you is where operators combined with the power that was already within OpenShift are now coming together to keep this you know particular database service highly available and something that we can continue using so Rob what have you got there yeah so as you can see we've got our couch based demo cluster running here and we can see that it's up and running we've got three members we've got an off secret this is what's controlling access to a UI that we're gonna look at in a second but what really shows the power of the operator is looking at this view of the resources that it's managing you can see that we've got a service that's doing load balancing into the cluster and then like you said we've got our pods that are actually running the software itself okay so that's cool so maybe for everyone's benefit so we can show that this is happening live could we bring up the the Couchbase console please and keep up the openshift console both sides so what we see there we go so what we see on the on the right hand side is obviously the same console Rob was working in on the left-hand side as you can see by the the actual names of the pods that are there the the couch based services that are available and so Rob maybe um let's let's kill something that's always fun to do on stage yeah this is the power of the operator it's going to recover it so let's browse on over here and kill node number two so we're gonna forcefully kill this and kick off the recovery and I see right away that because of the integration that we have with operators the Couchbase console immediately picked up that something has changed in the environment now why is that important normally a human being would have to get that alert right and so with operators now we've taken that capability and we've realized that there has been a new event within the environment this is not something that you know kubernetes or open shipped by itself would be able to understand now I'm presuming we're gonna end up doing something else it's not just seeing that it failed and sure enough there we go remember when you have a stateful application rebalancing that data and making it available is just as important as ensuring that the disk is attached so I mean Rob thank you so much for you know driving this for us today and being here I mean you know not only Couchbase but as was mentioned by matt we also have you know crunchy dynaTrace and black duck I would encourage you all to go visit their booths out on the floor today and understand what they have available which are all you know here with a dev preview and then talk to the many other partners that we have that are also looking at operators so again rub thank you for joining us today Matt come on out okay this is gonna make for an exciting year of just what it means to consume container base content I think containers change how customers can get that I believe operators are gonna change how much they can trust running that content let's circle back to one more partner this next partner we have has changed the landscape of computing specifically with their work on hardware design work on core Linux itself you know in fact I think they've become so ubiquitous with computing that we often overlook the technological marvels that they've been able to overcome now for myself I studied computer engineering so in the late 90s I had the chance to study processor design I actually got to build one of my own processors now in my case it was the most trivial processor that you could imagine it was an 8-bit subtractor which means it can subtract two numbers 256 or smaller but in that process I learned the sheer complexity that goes into processor design things like wire placements that are so close that electrons can cut through the insulation in short and then doing those wire placements across three dimensions to multiple layers jamming in as many logic components as you possibly can and again in my case this was to make a processor that could subtract two numbers but once I was done with this the second part of the course was studying the Pentium processor now remember that moment forever because looking at what the Pentium processor was able to accomplish it was like looking at alien technology and the incredible thing is that Intel our next partner has been able to keep up that alien like pace of innovation twenty years later so we're excited have Doug Fisher here let's hear a little bit more from Intel for business wide open skies an open mind no matter the context the idea of being open almost only suggests the potential of infinite possibilities and that's exactly the power of open source whether it's expanding what's possible in business the science and technology or for the greater good which is why-- open source requires the involvement of a truly diverse community of contributors to scale and succeed creating infinite possibilities for technology and more importantly what we do with it [Music] you know what Intel one of our core values is risk-taking and I'm gonna go just a bit off script for a second and say I was just backstage and I saw a gentleman that looked a lot like Scott Guthrie who runs all of Microsoft's cloud enterprise efforts wearing a red shirt talking to Cormier I'm just saying I don't know maybe I need some more sleep but that's what I saw as we approach Intel's 50th anniversary these words spoken by our co-founder Robert Noyce are as relevant today as they were decades ago don't be encumbered by history this is about breaking boundaries in technology and then go off and do something wonderful is about innovation and driving innovation in our industry and Intel we're constantly looking to break boundaries to advance our technology in the cloud in enterprise space that is no different so I'm going to talk a bit about some of the boundaries we've been breaking and innovations we've been driving at Intel starting with our Intel Xeon platform Orion Xeon scalable platform we launched several months ago which was the biggest and mark the most advanced movement in this technology in over a decade we were able to drive critical performance capabilities unmatched agility and added necessary and sufficient security to that platform I couldn't be happier with the work we do with Red Hat and ensuring that those hero features that we drive into our platform they fully expose to all of you to drive that innovation to go off and do something wonderful well there's taking advantage of the performance features or agility features like our advanced vector extensions or avx-512 or Intel quick exist those technologies are fully embraced by Red Hat Enterprise Linux or whether it's security technologies like txt or trusted execution technology are fully incorporated and we look forward to working with Red Hat on their next release to ensure that our advancements continue to be exposed and their platform and all these workloads that are driving the need for us to break boundaries and our technology are driving more and more need for flexibility and computing and that's why we're excited about Intel's family of FPGAs to help deliver that additional flexibility for you to build those capabilities in your environment we have a broad set of FPGA capabilities from our power fish at Mac's product line all the way to our performance product line on the 6/10 strat exten we have a broad set of bets FPGAs what i've been talking to customers what's really exciting is to see the combination of using our Intel Xeon scalable platform in combination with FPGAs in addition to the acceleration development capabilities we've given to software developers combining all that together to deliver better and better solutions whether it's helping to accelerate data compression well there's pattern recognition or data encryption and decryption one of the things I saw in a data center recently was taking our Intel Xeon scalable platform utilizing the capabilities of FPGA to do data encryption between servers behind the firewall all the while using the FPGA to do that they preserve those precious CPU cycles to ensure they delivered the SLA to the customer yet provided more security for their data in the data center one of the edges in cyber security is innovation and route of trust starts at the hardware we recently renewed our commitment to security with our security first pledge has really three elements to our security first pledge first is customer first urgency we have now completed the release of the micro code updates for protection on our Intel platforms nine plus years since launch to protect against things like the side channel exploits transparent and timely communication we are going to communicate timely and openly on our Intel comm website whether it's about our patches performance or other relevant information and then ongoing security assurance we drive security into every one of our products we redesigned a portion of our processor to add these partition capability which is adding additional walls between applications and user level privileges to further secure that environment from bad actors I want to pause for a second and think everyone in this room involved in helping us work through our security first pledge this isn't something we do on our own it takes everyone in this room to help us do that the partnership and collaboration was next to none it's the most amazing thing I've seen since I've been in this industry so thank you we don't stop there we continue to advance our security capabilities cross-platform solutions we recently had a conference discussion at RSA where we talked about Intel Security Essentials where we deliver a framework of capabilities and the end that are in our silicon available for those to innovate our customers and the security ecosystem to innovate on a platform in a consistent way delivering that assurance that those capabilities will be on that platform we also talked about things like our security threat technology threat detection technology is something that we believe in and we launched that at RSA incorporates several elements one is ability to utilize our internal graphics to accelerate some of the memory scanning capabilities we call this an accelerated memory scanning it allows you to use the integrated graphics to scan memory again preserving those precious cycles on the core processor Microsoft adopted this and are now incorporated into their defender product and are shipping it today we also launched our threat SDK which allows partners like Cisco to utilize telemetry information to further secure their environments for cloud workloads so we'll continue to drive differential experiences into our platform for our ecosystem to innovate and deliver more and more capabilities one of the key aspects you have to protect is data by 2020 the projection is 44 zettabytes of data will be available 44 zettabytes of data by 2025 they project that will grow to a hundred and eighty s data bytes of data massive amount of data and what all you want to do is you want to drive value from that data drive and value from that data is absolutely critical and to do that you need to have that data closer and closer to your computation this is why we've been working Intel to break the boundaries in memory technology with our investment in 3d NAND we're reducing costs and driving up density in that form factor to ensure we get warm data closer to the computing we're also innovating on form factors we have here what we call our ruler form factor this ruler form factor is designed to drive as much dense as you can in a 1u rack we're going to continue to advance the capabilities to drive one petabyte of data at low power consumption into this ruler form factor SSD form factor so our innovation continues the biggest breakthrough and memory technology in the last 25 years in memory media technology was done by Intel we call this our 3d crosspoint technology and our 3d crosspoint technology is now going to be driven into SSDs as well as in a persistent memory form factor to be on the memory bus giving you the speed of memory characteristics of memory as well as the characteristics of storage given a new tier of memory for developers to take full advantage of and as you can see Red Hat is fully committed to integrating this capability into their platform to take full advantage of that new capability so I want to thank Paul and team for engaging with us to make sure that that's available for all of you to innovate on and so we're breaking boundaries and technology across a broad set of elements that we deliver that's what we're about we're going to continue to do that not be encumbered by the past your role is to go off and doing something wonderful with that technology all ecosystems are embracing this and driving it including open source technology open source is a hub of innovation it's been that way for many many years that innovation that's being driven an open source is starting to transform many many businesses it's driving business transformation we're seeing this coming to light in the transformation of 5g driving 5g into the networked environment is a transformational moment an open source is playing a pivotal role in that with OpenStack own out and opie NFV and other open source projects were contributing to and participating in are helping drive that transformation in 5g as you do software-defined networks on our barrier breaking technology we're also seeing this transformation rapidly occurring in the cloud enterprise cloud enterprise are growing rapidly and innovation continues our work with virtualization and KVM continues to be aggressive to adopt technologies to advance and deliver more capabilities in virtualization as we look at this with Red Hat we're now working on Cube vert to help move virtualized workloads onto these platforms so that we can now have them managed at an open platform environment and Cube vert provides that so between Intel and Red Hat and the community we're investing resources to make certain that comes to product as containers a critical feature in Linux becomes more and more prevalent across the industry the growth of container elements continues at a rapid rapid pace one of the things that we wanted to bring to that is the ability to provide isolation without impairing the flexibility the speed and the footprint of a container with our clear container efforts along with hyper run v we were able to combine that and create we call cotta containers we launched this at the end of last year cotta containers is designed to have that container element available and adding elements like isolation both of these events need to have an orchestration and management capability Red Hat's OpenShift provides that capability for these workloads whether containerized or cube vert capabilities with virtual environments Red Hat openshift is designed to take that commercial capability to market and we've been working with Red Hat for several years now to develop what we call our Intel select solution Intel select solutions our Intel technology optimized for downstream workloads as we see a growth in a workload will work with a partner to optimize a solution on Intel technology to deliver the best solution that could be deployed quickly our effort here is to accelerate the adoption of these type of workloads in the market working with Red Hat's so now we're going to be deploying an Intel select solution design and optimized around Red Hat OpenShift we expect the industry's start deploying this capability very rapidly I'm excited to announce today that Lenovo is committed to be the first platform company to deliver this solution to market the Intel select solution to market will be delivered by Lenovo now I talked about what we're doing in industry and how we're transforming businesses our technology is also utilized for greater good there's no better example of this than the worked by dr. Stephen Hawking it was a sad day on March 14th of this year when dr. Stephen Hawking passed away but not before Intel had a 20-year relationship with dr. Hawking driving breakthrough capabilities innovating with him driving those robust capabilities to the rest of the world one of our Intel engineers an Intel fellow which is the highest technical achievement you can reach at Intel got to spend 10 years with dr. Hawking looking at innovative things they could do together with our technology and his breakthrough innovative thinking so I thought it'd be great to bring up our Intel fellow Lema notch Minh to talk about her work with dr. Hawking and what she learned in that experience come on up Elina [Music] great to see you Thanks something going on about the breakthrough breaking boundaries and Intel technology talk about how you use that in your work with dr. Hawking absolutely so the most important part was to really make that technology contextually aware because for people with disability every single interaction takes a long time so whether it was adapting for example the language model of his work predictor to understand whether he's gonna talk to people or whether he's writing a book on black holes or to even understand what specific application he might be using and then making sure that we're surfacing only enough actions that were relevant to reduce that amount of interaction so the tricky part is really to make all of that contextual awareness happen without totally confusing the user because it's constantly changing underneath it so how is that your work involving any open source so you know the problem with assistive technology in general is that it needs to be tailored to the specific disability which really makes it very hard and very expensive because it can't utilize the economies of scale so basically with the system that we built what we wanted to do is really enable unleashing innovation in the world right so you could take that framework you could tailor to a specific sensor for example a brain computer interface or something like that where you could actually then support a different set of users so that makes open-source a perfect fit because you could actually build and tailor and we you spoke with dr. Hawking what was this view of open source is it relevant to him so yeah so Stephen was adamant from the beginning that he wanted a system to benefit the world and not just himself so he spent a lot of time with us to actually build this system and he was adamant from day one that he would only engage with us if we were commit to actually open sourcing the technology that's fantastic and you had the privilege of working with them in 10 years I know you have some amazing stories to share so thank you so much for being here thank you so much in order for us to scale and that's what we're about at Intel is really scaling our capabilities it takes this community it takes this community of diverse capabilities it takes two births thought diverse thought of dr. Hawking couldn't be more relevant but we also are proud at Intel about leading efforts of diverse thought like women and Linux women in big data other areas like that where Intel feels that that diversity of thinking and engagement is critical for our success so as we look at Intel not to be encumbered by the past but break boundaries to deliver the technology that you all will go off and do something wonderful with we're going to remain committed to that and I look forward to continue working with you thank you and have a great conference [Applause] thank God now we have one more customer story for you today when you think about customers challenges in the technology landscape it is hard to ignore the public cloud these days public cloud is introducing capabilities that are driving the fastest rate of innovation that we've ever seen in our industry and our next customer they actually had that same challenge they wanted to tap into that innovation but they were also making bets for the long term they wanted flexibility and providers and they had to integrate to the systems that they already have and they have done a phenomenal job in executing to this so please give a warm welcome to Kerry Pierce from Cathay Pacific Kerry come on thanks very much Matt hi everyone thank you for giving me the opportunity to share a little bit about our our cloud journey let me start by telling you a little bit about Cathay Pacific we're an international airline based in Hong Kong and we serve a passenger and a cargo network to over 200 destinations in 52 countries and territories in the last seventy years and years seventy years we've made substantial investments to develop Hong Kong as one of the world's leading transportation hubs we invest in what matters most to our customers to you focusing on our exemplary service and our great product and it's both on the ground and in the air we're also investing and expanding our network beyond our multiple frequencies to the financial districts such as Tokyo New York and London and we're connecting Asia and Hong Kong with key tech hubs like San Francisco where we have multiple flights daily we're also connecting Asia in Hong Kong to places like Tel Aviv and our upcoming destination of Dublin in fact 2018 is actually going to be one of our biggest years in terms of network expansion and capacity growth and we will be launching in September our longest flight from Hong Kong direct to Washington DC and that'll be using a state-of-the-art Airbus a350 1000 aircraft so that's a little bit about Cathay Pacific let me tell you about our journey through the cloud I'm not going to go into technical details there's far smarter people out in the audience who will be able to do that for you just focus a little bit about what we were trying to achieve and the people side of it that helped us get there we had a couple of years ago no doubt the same issues that many of you do I don't think we're unique we had a traditional on-premise non-standardized fragile infrastructure it didn't meet our infrastructure needs and it didn't meet our development needs it was costly to maintain it was costly to grow and it really inhibited innovation most importantly it slowed the delivery of value to our customers at the same time you had the hype of cloud over the last few years cloud this cloud that clouds going to fix the world we were really keen on making sure we didn't get wound up and that so we focused on what we needed we started bottom up with a strategy we knew we wanted to be clouded Gnostic we wanted to have active active on-premise data centers with a single network and fabric and we wanted public clouds that were trusted and acted as an extension of that environment not independently we wanted to avoid single points of failure and we wanted to reduce inter dependencies by having loosely coupled designs and finally we wanted to be scalable we wanted to be able to cater for sudden surges of demand in a nutshell we kind of just wanted to make everything easier and a management level we wanted to be a broker of services so not one size fits all because that doesn't work but also not one of everything we want to standardize but a pragmatic range of services that met our development and support needs and worked in harmony with our public cloud not against it so we started on a journey with red hat we implemented Red Hat cloud forms and ansible to manage our hybrid cloud we also met implemented Red Hat satellite to maintain a manager environment we built a Red Hat OpenStack on crimson vironment to give us an alternative and at the same time we migrated a number of customer applications to a production public cloud open shift environment but it wasn't all Red Hat you love heard today that the Red Hat fits within an overall ecosystem we looked at a number of third-party tools and services and looked at developing those into our core solution I think at last count we had tried and tested somewhere past eight different tools and at the moment we still have around 62 in our environment that help us through that journey but let me put the technical solution aside a little bit because it doesn't matter how good your technical solution is if you don't have the culture and the people to get it right as a group we needed to be aligned for delivery and we focused on three core behaviors we focused on accountability agility and collaboration now I was really lucky we've got a pretty fantastic team for whom that was actually pretty easy but but again don't underestimate the importance of getting the culture and the people right because all the technology in the world doesn't matter if you don't have that right I asked the team what did we do differently because in our situation we didn't go out and hire a bunch of new people we didn't go out and hire a bunch of consultants we had the staff that had been with us for 10 20 and in some cases 30 years so what did we do differently it was really simple we just empowered and supported our staff we knew they were the smart ones they were the ones that were dealing with a legacy environment and they had the passion to make the change so as a team we encouraged suggestions and contributions from our overall IT community from the bottom up we started small we proved the case we told the story and then we got by him and only did did we implement wider the benefits the benefit through our staff were a huge increase in staff satisfaction reduction and application and platform outage support incidents risk free and failsafe application releases work-life balance no more midnight deployments and our application and infrastructure people could really focus on delivering customer value not on firefighting and for our end customers the people that travel with us it was really really simple we could provide a stable service that allowed for faster releases which meant we could deliver value faster in terms of stats we migrated 16 production b2c applications to a public cloud OpenShift environment in 12 months we decreased provisioning time from weeks or occasionally months we were waiting for hardware two minutes and we had a hundred percent availability of our key customer facing systems but most importantly it was about people we'd built a culture a culture of innovation that was built on a foundation of collaboration agility and accountability and that permeated throughout the IT organization not those just those people that were involved in the project everyone with an IT could see what good looked like and to see what it worked what it looked like in terms of working together and that was a key foundation for us the future for us you will have heard today everything's changing so we're going to continue to develop our open hybrid cloud onboard more public cloud service providers continue to build more modern applications and leverage the emerging technology integrate and automate everything we possibly can and leverage more open source products with the great support from the open source community so there you have it that's our journey I think we succeeded by not being over awed and by starting with the basics the technology was key obviously it's a cool component but most importantly it was a way we approached our transition we had a clear strategy that was actually developed bottom-up by the people that were involved day to day and we empowered those people to deliver and that provided benefits to both our staff and to our customers so thank you for giving the opportunity to share and I hope you enjoy the rest of the summer [Applause] I got one thanks what a great story would a great customer story to close on and we have one more partner to come up and this is a partner that all of you know that's Microsoft Microsoft has gone through an amazing transformation they've we've built an incredibly meaningful partnership with them all the way from our open source collaboration to what we do in the business side we started with support for Red Hat Enterprise Linux on hyper-v and that was truly just the beginning today we're announcing one of the most exciting joint product offerings on the market today let's please give a warm welcome to Paul correr and Scott Scott Guthrie to tell us about it guys come on out you know Scot welcome welcome to the Red Hat summer thanks for coming really appreciate it great to be here you know many surprises a lot of people when we you know published a list of speakers and then you rock you were on it and you and I are on stage here it's really really important and exciting to us exciting new partnership we've worked together a long time from the hypervisor up to common support and now around hybrid hybrid cloud maybe from your perspective a little bit of of what led us here well you know I think the thing that's really led us here is customers and you know Microsoft we've been on kind of a transformation journey the last several years where you know we really try to put customers at the center of everything that we do and you know as part of that you quickly learned from customers in terms of I'm including everyone here just you know you've got a hybrid of state you know both in terms of what you run on premises where it has a lot of Red Hat software a lot of Microsoft software and then really is they take the journey to the cloud looking at a hybrid of state in terms of how do you run that now between on-premises and a public cloud provider and so I think the thing that both of us are recognized and certainly you know our focus here at Microsoft has been you know how do we really meet customers with where they're at and where they want to go and make them successful in that journey and you know it's been fantastic working with Paul and the Red Hat team over the last two years in particular we spend a lot of time together and you know really excited about the journey ahead so um maybe you can share a bit more about the announcement where we're about to make today yeah so it's it's it's a really exciting announcement it's and really kind of I think first of its kind in that we're delivering a Red Hat openshift on Azure service that we're jointly developing and jointly managing together so this is different than sort of traditional offering where it's just running inside VMs and it's sort of two vendors working this is really a jointly managed service that we're providing with full enterprise support with a full SLA where the you know single throat to choke if you will although it's collectively both are choke the throats in terms of making sure that it works well and it's really uniquely designed around this hybrid world and in that it supports will support both Windows and Linux containers and it role you know it's the same open ship that runs both in the public cloud on Azure and on-premises and you know it's something that we hear a lot from customers I know there's a lot of people here that have asked both of us for this and super excited to be able to talk about it today and we're gonna show off the first demo of it just a bit okay well I'm gonna ask you to elaborate a bit more about this how this fits into the bigger Microsoft picture and I'll get out of your way and so thanks again thank you for coming here we go thanks Paul so I thought I'd spend just a few minutes talking about wouldn't you know that some of the work that we're doing with Microsoft Asher and the overall Microsoft cloud I didn't go deeper in terms of the new offering that we're announcing today together with red hat and show demo of it actually in action in a few minutes you know the high level in terms of you know some of the work that we've been doing at Microsoft the last couple years you know it's really been around this this journey to the cloud that we see every organization going on today and specifically the Microsoft Azure we've been providing really a cloud platform that delivers the infrastructure the application and kind of the core computing needs that organizations have as they want to be able to take advantage of what the cloud has to offer and in terms of our focus with Azure you know we've really focused we deliver lots and lots of different services and features but we focused really in particular on kind of four key themes and we see these four key themes aligning very well with the journey Red Hat it's been on and it's partly why you know we think the partnership between the two companies makes so much sense and you know for us the thing that we've been really focused on has been with a or in terms of how do we deliver a really productive cloud meaning how do we enable you to take advantage of cutting-edge technology and how do we kind of accelerate the successful adoption of it whether it's around the integration of managed services that we provide both in terms of the application space in the data space the analytic and AI space but also in terms of just the end-to-end management and development tools and how all those services work together so that teams can basically adopt them and be super successful yeah we deeply believe in hybrid and believe that the world is going to be a multi cloud and a multi distributed world and how do we enable organizations to be able to take the existing investments that they already have and be able to easily integrate them in a public cloud and with a public cloud environment and get immediate ROI on day one without how to rip and replace tons of solutions you know we're moving very aggressively in the AI space and are looking to provide a rich set of AI services both finished AI models things like speech detection vision detection object motion etc that any developer even at non data scientists can integrate to make application smarter and then we provide a rich set of AI tooling that enables organizations to build custom models and be able to integrate them also as part of their applications and with their data and then we invest very very heavily on trust Trust is sort of at the core of a sure and we now have more compliant certifications than any other cloud provider we run in more countries than any other cloud provider and we really focus around unique promises around data residency data sovereignty and privacy that are really differentiated across the industry and terms of where Iser runs today we're in 50 regions around the world so our region for us is typically a cluster of multiple data centers that are grouped together and you can see we're pretty much on every continent with the exception of Antarctica today and the beauty is you're going to be able to take the Red Hat open shift service and run it on ashore in each of these different locations and really have a truly global footprint as you look to build and deploy solutions and you know we've seen kind of this focus on productivity hybrid intelligence and Trust really resonate in the market and about 90 percent of Fortune 500 companies today are deployed on Azure and you heard Nike talked a little bit earlier this afternoon about some of their journeys as they've moved to a dot public cloud this is a small logo of just a couple of the companies that are on ashore today and what I do is actually even before we dive into the open ship demo is actually just show a quick video you know one of the companies thing there are actually several people from that organization here today Deutsche Bank who have been working with both Microsoft and Red Hat for many years Microsoft on the other side Red Hat both on the rel side and then on the OpenShift side and it's just one of these customers that have helped bring the two companies together to deliver this managed openshift service on Azure and so I'm just going to play a quick video of some of the folks that Deutsche Bank talking about their experiences and what they're trying to get out of it so we could roll the video that'd be great technology is at the absolute heart of Deutsche Bank we've recognized that the cost of running our infrastructure was particularly high there was a enormous amount of under utilization we needed a platform which was open to polyglot architecture supporting any kind of application workload across the various business lines of the third we analyzed over 60 different vendor products and we ended up with Red Hat openshift I'm super excited Microsoft or supporting Linux so strongly to adopting a hybrid approach we chose as here because Microsoft was the ideal partner to work with on constructs around security compliance business continuity as you as in all the places geographically that we need to be we have applications now able to go from a proof of concept to production in three weeks that is already breaking records openshift gives us given entities and containers allows us to apply the same sets of processes automation across a wide range of our application landscape on any given day we run between seven and twelve thousand containers across three regions we start see huge levels of cost reduction because of the level of multi-tenancy that we can achieve through containers open ship gives us an abstraction layer which is allows us to move our applications between providers without having to reconfigure or recode those applications what's really exciting for me about this journey is the way they're both Red Hat and Microsoft have embraced not just what we're doing but what each other are doing and have worked together to build open shift as a first-class citizen with Microsoft [Applause] in terms of what we're announcing today is a new fully managed OpenShift service on Azure and it's really the first fully managed service provided end-to-end across any of the cloud providers and it's jointly engineer operated and supported by both Microsoft and Red Hat and that means again sort of one service one SLA and both companies standing for a link firmly behind it really again focusing around how do we make customers successful and as part of that really providing the enterprise-grade not just isolates but also support and integration testing so you can also take advantage of all your rel and linux-based containers and all of your Windows server based containers and how can you run them in a joint way with a common management stack taking the advantage of one service and get maximum density get maximum code reuse and be able to take advantage of a containerized world in a better way than ever before and make this customer focus is very much at the center of what both companies are really centered around and so what if I do be fun is rather than just talk about openshift as actually kind of show off a little bit of a journey in terms of what this move to take advantage of it looks like and so I'd like to invite Brendan and Chris onstage who are actually going to show off a live demo of openshift on Azure in action and really walk through how to provision the service and basically how to start taking advantage of it using the full open ship ecosystem so please welcome Brendan and Chris we're going to join us on stage for a demo thanks God thanks man it's been a good afternoon so you know what we want to get into right now first I'd like to think Brandon burns for joining us from Microsoft build it's a busy week for you I'm sure your own stage there a few times as well you know what I like most about what we just announced is not only the business and technical aspects but it's that operational aspect the uniqueness the expertise that RedHat has for running OpenShift combined with the expertise that Microsoft has within Azure and customers are going to get this joint offering if you will with you know Red Hat OpenShift on Microsoft Azure and so you know kind of with that again Brendan I really appreciate you being here maybe talk to the folks about what we're going to show yeah so we're going to take a look at what it looks like to deploy OpenShift on to Azure via the new OpenShift service and the real selling point the really great part of this is the the deep integration with a cloud native app API so the same tooling that you would use to create virtual machines to create disks trade databases is now the tooling that you're going to use to create an open chip cluster so to show you this first we're going to create a resource group here so we're going to create that resource group in East us using the AZ tool that's the the azure command-line tooling a resource group is sort of a folder on Azure that holds all of your stuff so that's gonna come back into the second I've created my resource group in East us and now we're gonna use that exact same tool calling into into Azure api's to provision an open shift cluster so here we go we have AZ open shift that's our new command line tool putting it into that resource group I'm gonna get into East us alright so it's gonna take a little bit of time to deploy that open shift cluster it's doing a bunch of work behind the scenes provisioning all kinds of resources as well as credentials to access a bunch of different as your API so are we actually able to see this to you yeah so we can cut over to in just a second we can cut over to that resource group in a reload so Brendan while relating the beauty of what you know the teams have been doing together already is the fact that now open shift is a first-class citizen as it were yeah absolutely within the agent so I presume not only can I do a deployment but I can do things like scale and check my credentials and pretty much everything that I could do with any other service with that that's exactly right so we can anything that you you were used to doing via the my computer has locked up there we go the demo gods are totally with me oh there we go oh no I hit reload yeah that was that was just evil timing on the house this is another use for operators as we talked about earlier today that's right my dashboard should be coming up do I do I dare click on something that's awesome that was totally it was there there we go good job so what's really interesting about this I've also heard that it deploys you know in as little as five to six minutes which is really good for customers they want to get up and running with it but all right there we go there it is who managed to make it see that shows that it's real right you see the sweat coming off of me there but there you can see the I feel it you can see the various resources that are being created in order to create this openshift cluster virtual machines disks all of the pieces provision for you automatically via that one single command line call now of course it takes a few minutes to to create the cluster so in order to show the other side of that integration the integration between openshift and Azure I'm going to cut over to an open shipped cluster that I already have created alright so here you can see my open shift cluster that's running on Microsoft Azure I'm gonna actually log in over here and the first sign you're gonna see of the integration is it's actually using my credentials my login and going through Active Directory and any corporate policies that I may have around smart cards two-factor off anything like that authenticate myself to that open chef cluster so I'll accept that it can access my and now we're gonna load up the OpenShift web console so now this looks familiar to me oh yeah so if anybody's used OpenShift out there this is the exact same console and what we're going to show though is how this console via the open service broker and the open service broker implementation for Azure integrates natively with OpenShift all right so we can go down here and we can actually see I want to deploy a database I'm gonna deploy Mongo as my key value store that I'm going to use but you know like as we talk about management and having a OpenShift cluster that's managed for you I don't really want to have to manage my database either so I'm actually going to use cosmos DB it's a native Azure service it's a multilingual database that offers me the ability to access my data in a variety of different formats including MongoDB fully managed replicated around the world a pretty incredible service so I'm going to go ahead and create that so now Brendan what's interesting I think to me is you know we talked about the operational aspects and clearly it's not you and I running the clusters but you do need that way to interface with it and so when customers are able to deploy this all of this is out of the box there's no additional contemporary like this is what you get when you create when you use that tool to create that open chef cluster this is what you get with all of that integration ok great step through here and go ahead don't have any IP ranges there we go all right and we create that binding all right and so now behind the scenes openshift is integrated with the azure api's with all of my credentials to go ahead and create that distributed database once it's done provisioning actually all of the credentials necessary to access the database are going to be automatically populated into kubernetes available for me inside of OpenShift via service discovery to access from my application without any further work so I think that really shows not only the power of integrating openshift with an azure based API but actually the power of integrating a Druze API is inside of OpenShift to make a truly seamless experience for managing and deploying your containers across a variety of different platforms yeah hey you know Brendan this is great I know you've got a flight to catch because I think you're back onstage in a few hours but you know really appreciate you joining us today absolutely I look forward to seeing what else we do yeah absolutely thank you so much thanks guys Matt you want to come back on up thanks a lot guys if you have never had the opportunity to do a live demo in front of 8,000 people it'll give you a new appreciation for standing up there and doing it and that was really good you know every time I get the chance just to take a step back and think about the technology that we have at our command today I'm in awe just the progress over the last 10 or 20 years is incredible on to think about what might come in the next 10 or 20 years really is unthinkable you even forget 10 years what might come in the next five years even the next two years but this can create a lot of uncertainty in the environment of what's going to be to come but I believe I am certain about one thing and that is if ever there was a time when any idea is achievable it is now just think about what you've seen today every aspect of open hybrid cloud you have the world's infrastructure at your fingertips and it's not stopping you've heard about this the innovation of open source how fast that's evolving and improving this capability you've heard this afternoon from an entire technology ecosystem that's ready to help you on this journey and you've heard from customer after customer that's already started their journey in the successes that they've had you're one of the neat parts about this afternoon you will aren't later this week you will actually get to put your hands on all of this technology together in our live audience demo you know this is what some it's all about for us it's a chance to bring together the technology experts that you can work with to help formulate how to pull off those ideas we have the chance to bring together technology experts our customers and our partners and really create an environment where everyone can experience the power of open source that same spark that I talked about when I was at IBM where I understood the but intial that open-source had for enterprise customers we want to create the environment where you can have your own spark you can have that same inspiration let's make this you know in tomorrow's keynote actually you will hear a story about how open-source is changing medicine as we know it and literally saving lives it is a great example of expanding the ideas it might be possible that we came into this event with so let's make this the best summit ever thank you very much for being here let's kick things off right head down to the Welcome Reception in the expo hall and please enjoy the summit thank you all so much [Music] [Music]

>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)

>> Narrator: Live from Las Vegas It's theCUBE covering IBM Think 2018. Brought to you by IBM. >> We're back at Mandalay Bay in Las Vegas. This is IBM Think 2018. This is day three of theCUBE's wall-to-wall coverage. My name is Dave Vellante, I'm here with Peter Burris. You're watching theCUBE, the leader in live tech coverage. Daniel Hernandez is here. He's the Vice President of IBM Analytics, a CUBE alum. It's great to see you again, Daniel >> Thanks >> Dave: Thanks for coming back on >> Happy to be here. >> Big tech show, consolidating a bunch of shows, you guys, you kind of used to have your own sort of analytics show but now you've got all the clients here. How do you like it? Compare and contrast. >> IBM Analytics loves to share so having all our clients in one place, I actually like it. We're going to work out some of the kinks a little bit but I think one show where you can have a conversation around Artificial Intelligence, data, analytics, power systems, is beneficial to all of us, actually. >> Well in many respects, the whole industry is munging together. Folks focus more on workloads as opposed to technology or even roles. So having an event like this where folks can talk about what they're trying to do, the workloads they're trying to create, the role that analytics, AI, et cetera is going to play in informing those workloads. Not a bad place to get that crosspollination. What do you think? >> Daniel: Totally. You talk to a client, there are so many problems. Problems are a combination of stuff that we have to offer and analytics stuff that our friends in Hybrid Integration have to offer. So for me, logistically, I could say oh, Mike Gilfix, business process automation. Go talk to him. And he's here. That's happened probably at least a dozen times so far in not even two days. >> Alright so I got to ask, your tagline. Making data ready for AI. What does that mean? >> We get excited about amazing tech. Artificial intelligence is amazing technology. I remember when Watson beat Jeopardy. Just being inspired by all the things that I thought it could do to solve problems that matter to me. And if you look over the last many years, virtual assistants, image recognition systems that solve pretty big problems like catching bad guys are inspirational pieces of work that were inspired a lot by what we did then. And in business, it's triggered a wave of artificial intelligence can help me solve business critical issues. And I will tell you that many clients simply aren't ready to get started. And because they're not ready, they're going to fail. And so our attitude about things are, through IBM Analytics, we're going to deliver the critical capabilities you need to be ready for AI. And if you don't have that, 100% of your projects will fail. >> But how do you get the business ready to think about data differently? You can do a lot to say, the technology you need to do this looks differently but you also need to get the organization to acculturate, appreciate that their business is going to run differently as a consequence of data and what you do with it. How do you get the business to start making adjustments? >> I think you just said the magic word, the business. Which is to say, at least all the conversations I have with my customers, they can't even tell that I'm from the analytics because I'm asking them about the problems. What do you try to do? How would you measure success? What are the critical issues that you're trying to solve? Are you trying to make money, save money, those kinds of things. And by focusing on it, we can advise them then based on that how we can help. So the data culture that you're describing I think it's a fact, like you become data aware and understand the power of it by doing. You do by starting with the problems, developing successes and then iterating. >> An approach to solving problems. >> Yeah >> So that's kind of a step zero to getting data ready for AI >> Right. But in no conversation that leads to success does it ever start with we're going to do AI or machine learning, what problem are we going to solve? It's always the other way around. And when we do that, our technology then is easily explainable. It's like okay, you want to build a system for better customer interactions in your call center. Well, what does that mean? You need data about how they have interacted with you, products they have interacted with, you might want predictions that anticipate what their needs are before they tell you. And so we can systematically address them through the capabilities we've got. >> Dave, if I could amplify one thing. It makes the technology easier when you put it in these constants I think that's a really crucial important point. >> It's super simple. All of us have had to have it, if we're in technology. Going the other way around, my stuff is cool. Here's why it's cool. What problems can you solve? Not helpful for most of our clients. >> I wonder if you could comment on this Daniel. I feel like we're, the last ten years about cloud mobile, social, big data. We seem to be entering an era now of sense, speak, act, optimize, see, learn. This sort of pervasive AI, if you will. How- is that a reasonable notion, that we're entering that era, and what do you see clients doing to take advantage of that? What's their mindset like when you talk to them? >> I think the evidence is there. You just got to look around the show and see what's possible, technically. The Watson team has been doing quite a bit of stuff around speech, around image. It's fascinating tech, stuff that feels magical to me. And I know how this stuff works and it still feels kind of fascinating. Now the question is how do you apply that to solve problems. I think it's only a matter of time where most companies are implementing artificial intelligence systems in business critical and core parts of their processes and they're going to get there by starting, by doing what they're already doing now with us, and that is what problem am I solving? What data do I need to get that done? How do I control and organize that information so I can exploit it? How can I exploit machine learning and deep learning and all these other technologies to then solve that problem. How do I measure success? How do I track that? And just systematically running these experiments. I think that crescendos to a critical mass. >> Let me ask you a question. Because you're a technologist and you said it's amazing, it's like magic even to you. Imagine non technologists, what `it's like to me. There's a black box component of AI, and maybe that's okay. I'm just wondering if that's, is that a headwind, are clients comfortable with that? If you have to describe how you really know it's a cat. I mean, I know a cat when I see it. And the machine can tell me it's a cat, or not a hot dog Silicon Valley reference. (Peter laughs) But to tell me actually how it works, to figure that out there's a black box component. Does that scare people? Or are they okay with that? >> You've probably given me too much credit. So I really can't explain how all that just works but what I can tell you is how certainly, I mean, lets take regulated industries like banks and insurance companies that are building machine learning models throughout their enterprise. They've got to explain to a regulator that they are offering considerations around anti discriminatory, basically they're not buying systems that cause them to do things that are against the law, effectively. So what are they doing? Well, they're using tools like ones from IBM to build these models to track the process of creating these models which includes what data they used, how that training was done, prove that the inputs and outputs are not anti-discriminatory and actually go through their own internal general counsel and regulators to get it done. So whether you can explain the model in this particular case doesn't matter. What they're trying to prove is that the effect is not violating the law, which the tool sets and the process around those tool sets allow you to get that done today. >> Well, let me build on that because one of the ways that it does work is that, as Ginni said yesterday, Ginni Rometty said yesterday that it's always going to be a machine human component to it. And so the way it typically works is a machine says I think this is a cat and a human validates it or not. The machine still doesn't really know if it's a cat but coming back to this point, one of the key things that we see anyway, and one of the advantages that IBM likely has, is today the folks running Operational Systems, the core of the business, trust their data sources. >> Do they? >> They trust their DB2 database, they trust their Oracle database, they trust the data that's in the applications. >> Dave: So it's the data that's in their Data Lake? >> I'm not saying they do but that's the key question. At what point in time, and I think the real important part of your question is, at what point in time do the hardcore people allow AI to provide a critical input that's going to significantly or potentially dramatically change the behavior of the core operational systems. That seems a really crucial point. What kind of feedback do you get from customers as you talk about turning AI from something that has an insight every now and then to becoming effectively, an element or essential to the operation of the business? >> One of the critical issues in getting especially machine learning models, integrated in business critical processes and workflows is getting those models running where that work is done. So if you look, I mean, when I was here last time I was talking about the, we were focused on portfolio simplification and bringing machine learning where the data was. We brought machine learning to private cloud, we brought it onto Gadook, we brought it on mainframe. I think it is a critical necessary ingredient that you need to deliver that outcome. Like, bring that technology where the data is. Otherwise it just won't work. Why? As soon as you move, you've got latency. As soon as you move, you've got data quality issues you're going to have contending. That's going to exacerbate whatever mistrust you might have. >> Or the stuff's not cheap to move. It's not cheap to ingest. >> Yeah. By the way, the Machine Learning on Z offering that we launched last year in March, April was one of our highest, most successful offerings last year. >> Let's talk about some of the offerings. I mean, at the end of the day you're in the business of selling stuff. You've talked about Machine Learning on Z X, whatever platform. Cloud Private, I know you've got perspectives on that. Db2 Event Store is something that you're obviously familiar with. SPSS is part of the portfolio. >> 50 year, the anniversary. >> Give us the update on some of these products. >> Making data ready for AI requires a design principled on simplicity. We launched in January three core offerings that help clients benefit from the capability that we deliver to capture data, to organize and control that data and analyze that data. So we delivered a Hybrid Data Management offering which gives you everything you need to collect data, it's anchored by Db2. We have the Unified Governance and Integration portfolio that gives you everything you need to organize and control that data as anchored by our information server product set. And we've got our Data Science and Businesses Analytics portfolio, which is anchored by our data science experience, SPSS and Cognos Analytics portfolio. So clients that want to mix and match those capabilities in support of artificial intelligence systems, or otherwise, can benefit from that easily. We just announced here a radical- an even radical step forward in simplification, which we thought that there already was. So if you want to move to the public cloud but can't, don't want to move to the public cloud for whatever reason and we think, by the way, public cloud for workload to like, you should try to run as much as you can there because the benefits of it. But if for whatever reason you can't, we need to deliver those benefits behind the firewall where those workloads are. So last year the Hybrid Integration team led by Denis Kennelly, introduced an IBM cloud private offering. It's basically application paths behind the firewall. It's like run on a Kubernetes environment. Your applications do buildouts, do migrations of existing workloads to it. What we did with IBM Cloud Private for data is have the data companion for that. IBM Cloud Private was a runaway success for us. You could imagine the data companion to that just being like, what application doesn't need data? It's peanut butter and jelly for us. >> Last question, oh you had another point? >> It's alright. I wanted to talk about Db2 and SPCC. >> Oh yes, let's go there, yeah. >> Db2 Event Store, I forget if anybody- It has 100x performance improvement on Ingest relative to the current state of the order. You say, why does that matter? If you do an analysis or analytics, machine learning, artificial intelligence, you're only as good as whatever data you have captured of your, whatever your reality is. Currently our databases don't allow you to capture everything you would want. So Db2 Event Store with that Ingest lets you capture more than you could ever imagine you would want. 250 billion events per year is basically what it's rated at. So we think that's a massive improvement in database technology and it happens to be based in open source, so the programming model is something that developers feel is familiar. SPSS is celebrating it's 50th year anniversary. It's the number one digital offering inside of IBM. It had 510,000 users trying it out last year. We just renovated the user experience and made it even more simple on stats. We're doing the same thing on Modeler and we're bringing SPSS and our data science experience together so that there's one tool chain for data science end to end in the Private Cloud. It's pretty phenomenal stuff. >> Okay great, appreciate you running down the portfolio for us. Last question. It's kind of a, get out of your telescope. When you talk to clients, when you think about technology from a technologist's perspective, how far can we take machine intelligence? Think 20 plus years, how far can we take it and how far should we take it? >> Can they ever really know what a cat is? (chuckles) >> I don't know what the answer to that question is, to be honest. >> Are people asking you that question, in the client base? >> No. >> Are they still figuring out, how do I apply it today? >> Surely they're not asking me, probably because I'm not the smartest guy in the room. They're probably asking some of the smarter guys-- >> Dave: Well, Elon Musk is talking about it. Stephen Hawking was talking about it. >> I think it's so hard to anticipate. I think where we are today is magical and I couldn't have anticipated it seven years ago, to be honest, so I can't imagine. >> It's really hard to predict, isn't it? >> Yeah. I've been wrong on three to four year horizons. I can't do 20 realistically. So I'm sorry to disappoint you. >> No, that's okay. Because it leads to my real last question which is what kinds of things can machines do that humans can't and you don't even have to answer this, but I just want to put it out there to the audience to think about how are they going to complement each other. How are they going to compete with each other? These are some of the big questions that I think society is asking. And IBM has some answers, but we're going to apply it here, here and here, you guys are clear about augmented intelligence, not replacing. But there are big questions that I think we want to get out there and have people ponder. I don't know if you have a comment. >> I do. I think there are non obvious things to human beings, relationships between data that's expressing some part of your reality that a machine through machine learning can see that we can't. Now, what does it mean? Do you take action on it? Is it simply an observation? Is it something that a human being can do? So I think that combination is something that companies can take advantage of today. Those non obvious relationships inside of your data, non obvious insights into your data is what machines can get done now. It's how machine learning is being used today. Is it going to be able to reason on what to do about it? Not yet, so you still need human beings in the middle too, especially when you deal with consequential decisions. >> Yeah but nonetheless, I think the impact on industry is going to be significant. Other questions we ask are retail stores going to be the exception versus the normal. Banks lose control of the payment systems. Will cyber be the future of warfare? Et cetera et cetera. These are really interesting questions that we try and cover on theCUBE and we appreciate you helping us explore those. Daniel, it's always great to see you. >> Thank you, Dave. Thank you, Peter. >> Alright keep it right there buddy, we'll be back with our next guest right after this short break. (electronic music)

>> Narrator: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. >> Welcome back to IBM's Machine Learning Everywhere. Build your ladder to AI, along with Dave Vellante, John Walls here, wrapping up here in New York City. Just about done with the programming here in Midtown. Dave, let's just take a step back. We've heard a lot, seen a lot, talked to a lot of folks today. First off, tell me, AI. We've heard some optimistic outlooks, some, I wouldn't say pessimistic, but some folks saying, "Eh, hold off." Not as daunting as some might think. So just your take on the artificial intelligence conversation we've heard so far today. >> I think generally, John, that people don't realize what's coming. I think the industry, in general, our industry, technology industry, the consumers of technology, the businesses that are out there, they're steeped in the past, that's what they know. They know what they've done, they know the history and they're looking at that as past equals prologue. Everybody knows that's not the case, but I think it's hard for people to envision what's coming, and what the potential of AI is. Having said that, Jennifer Shin is a near-term pessimist on the potential for AI, and rightly so. There are a lot of implementation challenges. But as we said at the open, I'm very convinced that we are now entering a new era. The Hadoop big data industry is going to pale in comparison to what we're seeing. And we're already seeing very clear glimpses of it. The obvious things are Airbnb and Uber, and the disruptions that are going on with Netflix and over-the-top programming, and how Google has changed advertising, and how Amazon is changing and has changed retail. But what you can see, and again, the best examples are Apple getting into financial services, moving into healthcare, trying to solve that problem. Amazon buying a grocer. The rumor that I heard about Amazon potentially buying Nordstrom, which my wife said is a horrible idea. (John laughs) But think about the fact that they can do that is a function of, that they are a digital-first company. Are built around data, and they can take those data models and they can apply it to different places. Who would have thought, for example, that Alexa would be so successful? That Siri is not so great? >> Alexa's become our best friend. >> And it came out of the blue. And it seems like Google has a pretty competitive piece there, but I can almost guarantee that doing this with our thumbs is not the way in which we're going to communicate in the future. It's going to be some kind of natural language interface that's going to rely on artificial intelligence and machine learning and the like. And so, I think it's hard for people to envision what's coming, other than fast forward where machines take over the world and Stephen Hawking and Elon Musk say, "Hey, we should be concerned." Maybe they're right, not in the next 10 years. >> You mentioned Jennifer, we were talking about her and the influencer panel, and we've heard from others as well, it's a combination of human intelligence and artificial intelligence. That combination's more powerful than just artificial intelligence, and so, there is a human component to this. So, for those who might be on the edge of their seat a little bit, or looking at this from a slightly more concerning perspective, maybe not the case. Maybe not necessary, is what you're thinking. >> I guess at the end of the day, the question is, "Is the world going to be a better place with all this AI? "Are we going to be more prosperous, more productive, "healthier, safer on the roads?" I am an optimist, I come down on the side of yes. I would not want to go back to the days where I didn't have GPS. That's worth it to me. >> Can you imagine, right? If you did that now, you go back five years, just five years from where we are now, back to where we were. Waze was nowhere, right? >> All the downside of these things, I feel is offset by that. And I do think it's incumbent upon the industry to try to deal with the problem, especially with young people, the blue light problem. >> John: The addictive issue. >> That's right. But I feel like those downsides are manageable, and the upsides are of enough value that society is going to continue to move forward. And I do think that humans and machines are going to continue to coexist, at least in the near- to mid- reasonable long-term. But the question is, "What can machines "do that humans can't do?" And "What can humans do that machines can't do?" And the answer to that changes every year. It's like I said earlier, not too long ago, machines couldn't climb stairs. They can now, robots can climb stairs. Can they negotiate? Can they identify cats? Who would've imagined that all these cats on the Internet would've led to facial recognition technology. It's improving very, very rapidly. So, I guess my point is that that is changing very rapidly, and there's no question it's going to have an impact on society and an impact on jobs, and all those other negative things that people talk about. To me, the key is, how do we embrace that and turn it into an opportunity? And it's about education, it's about creativity, it's about having multi-talented disciplines that you can tap. So we talked about this earlier, not just being an expert in marketing, but being an expert in marketing with digital as an understanding in your toolbox. So it's that two-tool star that I think is going to emerge. And maybe it's more than two tools. So that's how I see it shaping up. And the last thing is disruption, we talked a lot about disruption. I don't think there's any industry that's safe. Colin was saying, "Well, certain industries "that are highly regulated-" In some respects, I can see those taking longer. But I see those as the most ripe for disruption. Financial services, healthcare. Can't we solve the HIPAA challenge? We can't get access to our own healthcare information. Well, things like artificial intelligence and blockchain, we were talking off-camera about blockchain, those things, I think, can help solve the challenge of, maybe I can carry around my health profile, my medical records. I don't have access to them, it's hard to get them. So can things like artificial intelligence improve our lives? I think there's no question about it. >> What about, on the other side of the coin, if you will, the misuse concerns? There are a lot of great applications. There are a lot of great services. As you pointed out, a lot of positive, a lot of upside here. But as opportunities become available and technology develops, that you run the risk of somebody crossing the line for nefarious means. And there's a lot more at stake now because there's a lot more of us out there, if you will. So, how do you balance that? >> There's no question that's going to happen. And it has to be managed. But even if you could stop it, I would say you shouldn't because the benefits are going to outweigh the risks. And again, the question we asked the panelists, "How far can we take machines? "How far can we go?" That's question number one, number two is, "How far should we go?" We're not even close to the "should we go" yet. We're still on the, "How far can we go?" Jennifer was pointing out, I can't get my password reset 'cause I got to call somebody. That problem will be solved. >> So, you're saying it's more of a practical consideration now than an ethical one, right now? >> Right now. Moreso, and there's certainly still ethical considerations, don't get me wrong, but I see light at the end of the privacy tunnel, I see artificial intelligence as, well, analytics is helping us solve credit card fraud and things of that nature. Autonomous vehicles are just fascinating, right? Both culturally, we talked about that, you know, we learned how to drive a stick shift. (both laugh) It's a funny story you told me. >> Not going to worry about that anymore, right? >> But it was an exciting time in our lives, so there's a cultural downside of that. I don't know what the highway death toll number is, but it's enormous. If cell phones caused that many deaths, we wouldn't be using them. So that's a problem that I think things like artificial intelligence and machine intelligence can solve. And then the other big thing that we talked about is, I see a huge gap between traditional companies and these born-in-the-cloud, born-data-oriented companies. We talked about the top five companies by market cap. Microsoft, Amazon, Facebook, Alphabet, which is Google, who am I missing? >> John: Apple. >> Apple, right. And those are pretty much very much data companies. Apple's got the data from the phones, Google, we know where they get their data, et cetera, et cetera. Traditional companies, however, their data resides in silos. Jennifer talked about this, Craig, as well as Colin. Data resides in silos, it's hard to get to. It's a very human-driven business and the data is bolted on. With the companies that we just talked about, it's a data-driven business, and the humans have expertise to exploit that data, which is very important. So there's a giant skills gap in existing companies. There's data silos. The other thing we touched on this is, where does innovation come from? Innovation drives value drives disruption. So the innovation comes from data. He or she who has the best data wins. It comes from artificial intelligence, and the ability to apply artificial intelligence and machine learning. And I think something that we take for granted a lot, but it's cloud economics. And it's more than just, and somebody, one of the folks mentioned this on the interview, it's more than just putting stuff in the cloud. It's certainly managed services, that's part of it. But it's also economies of scale. It's marginal economics that are essentially zero. It's speed, it's low latency. It's, and again, global scale. You combine those things, data, artificial intelligence, and cloud economics, that's where the innovation is going to come from. And if you think about what Uber's done, what Airbnb have done, where Waze came from, they were picking and choosing from the best digital services out there, and then developing their own software from this, what I say my colleague Dave Misheloff calls this matrix. And, just to repeat, that matrix is, the vertical matrix is industries. The horizontal matrix are technology platforms, cloud, data, mobile, social, security, et cetera. They're building companies on top of that matrix. So, it's how you leverage the matrix is going to determine your future. Whether or not you get disrupted, whether your the disruptor or the disruptee. It's not just about, we talked about this at the open. Cloud, SaaS, mobile, social, big data. They're kind of yesterday's news. It's now new artificial intelligence, machine intelligence, deep learning, machine learning, cognitive. We're still trying to figure out the parlance. You could feel the changes coming. I think this matrix idea is very powerful, and how that gets leveraged in organizations ultimately will determine the levels of disruption. But every single industry is at risk. Because every single industry is going digital, digital allows you to traverse industries. We've said it many times today. Amazon went from bookseller to content producer to grocer- >> John: To grocer now, right? >> To maybe high-end retailer. Content company, Apple with Apple Pay and companies getting into healthcare, trying to solve healthcare problems. The future of warfare, you live in the Beltway. The future of warfare and cybersecurity are just coming together. One of the biggest issues I think we face as a country is we have fake news, we're seeing the weaponization of social media, as James Scott said on theCUBE. So, all these things are coming together that I think are going to make the last 10 years look tame. >> Let's just switch over to the currency of AI, data. And we've talked to, Sam Lightstone today was talking about the database querying that they've developed with the Plex product. Some fascinating capabilities now that make it a lot richer, a lot more meaningful, a lot more relevant. And that seems to be, really, an integral step to making that stuff come alive and really making it applicable to improving your business. Because they've come up with some fantastic new ways to squeeze data that's relevant out, and get it out to the user. >> Well, if you think about what I was saying earlier about data as a foundational core and human expertise around it, versus what most companies are, is human expertise with data bolted on or data in silos. What was interesting about Queryplex, I think they called it, is it essentially virtualizes the data. Well, what does that mean? That means i can have data in place, but I can have access to that data, I can democratize that data, make it accessible to people so that they can become data-driven, data is the core. Now, what I don't know, and I don't know enough, just heard about it today, I missed that announcement, I think they announced it a year ago. He mentioned DB2, he mentioned Netezza. Most of the world is not on DB2 and Netezza even though IBM customers are. I think they can get to Hadoop data stores and other data stores, I just don't know how wide that goes, what the standards look like. He joked about the standards as, the great thing about standards is- >> There are a lot of 'em. (laughs) >> There's always another one you can pick if this one fails. And he's right about that. So, that was very interesting. And so, this is again, the question, can traditional companies close that machine learning, machine intelligence, AI gap? Close being, close the gap that the big five have created. And even the small guys, small guys like Uber and Airbnb, and so forth, but even those guys are getting disrupted. The Airbnbs and the Ubers, right? Again, blockchain comes in and you say, "Why do I need a trusted third party called Uber? "Why can't I do this on the blockchain?" I predict you're going to see even those guys get disrupted. And I'll say something else, it's hard to imagine that a Google or a Facebook can be unseated. But I feel like we may be entering an era where this is their peak. Could be wrong, I'm an Apple customer. I don't know, I'm not as enthralled as I used to be. They got trillions in the bank. But is it possible that opensource and blockchain and the citizen developer, the weekend and nighttime developers, can actually attack that engine of growth for the last 10 years, 20 years, and really break that monopoly? The Internet has basically become an oligopoly where five companies, six companies, whatever, 10 companies kind of control things. Is it possible that opensource software, AI, cryptography, all this activity could challenge the status quo? Being in this business as long as I have, things never stay the same. Leaders come, leaders go. >> I just want to say, never say never. You don't know. >> So, it brings it back to IBM, which is interesting to me. It was funny, I was asking Rob Thomas a question about disruption, and I think he misinterpreted it. I think he was thinking that I was saying, "Hey, you're going to get disrupted by all these little guys." IBM's been getting disrupted for years. They know how to reinvent. A lot of people criticize IBM, how many quarters they haven't had growth, blah, blah, blah, but IBM's made some big, big bets on the future. People criticizing Watson, but it's going to be really interesting to see how all this investment that IBM has made is going to pay off. They were early on. People in the Valley like to say, "Well, the Facebooks, and even Amazon, "Google, they got the best AI. "IBM is not there with them." But think about what IBM is trying to do versus what Google is doing. They're very consumer-oriented, solving consumer problems. Consumers have really led the consumerization of IT, that's true, but none of those guys are trying to solve cancer. So IBM is talking about some big, hairy, audacious goals. And I'm not as pessimistic as some others you've seen in the trade press, it's popular to do. So, bringing it back to IBM, I saw IBM as trying to disrupt itself. The challenge IBM has, is it's got a lot of legacy software products that have purchased over the years. And it's got to figure out how to get through those. So, things like Queryplex allow them to create abstraction layers. Things like Bluemix allow them to bring together their hundreds and hundreds and hundreds of SaaS applications. That takes time, but I do see IBM making some big investments to disrupt themselves. They've got a huge analytics business. We've been covering them for quite some time now. They're a leader, if not the leader, in that business. So, their challenge is, "Okay, how do we now "apply all these technologies to help "our customers create innovation?" What I like about the IBM story is they're not out saying, "We're going to go disrupt industries." Silicon Valley has a bifurcated disruption agenda. On the one hand, they're trying to, cloud, and SaaS, and mobile, and social, very disruptive technologies. On the other hand, is Silicon Valley going to disrupt financial services, healthcare, government, education? I think they have plans to do so. Are they going to be able to execute that dual disruption agenda? Or are the consumers of AI and the doers of AI going to be the ones who actually do the disrupting? We'll see, I mean, Uber's obviously disrupted taxis, Silicon Valley company. Is that too much to ask Silicon Valley to do? That's going to be interesting to see. So, my point is, IBM is not trying to disrupt its customers' businesses, and it can point to Amazon trying to do that. Rather, it's saying, "We're going to enable you." So it could be really interesting to see what happens. You're down in DC, Jeff Bezos spent a lot of time there at the Washington Post. >> We just want the headquarters, that's all we want. We just want the headquarters. >> Well, to the point, if you've got such a growing company monopoly, maybe you should set up an HQ2 in DC. >> Three of the 20, right, for a DC base? >> Yeah, he was saying the other day that, maybe we should think about enhancing, he didn't call it social security, but the government, essentially, helping people plan for retirement and the like. I heard that and said, "Whoa, is he basically "telling us he's going to put us all out of jobs?" (both laugh) So, that, if I'm a customer of Amazon's, I'm kind of scary. So, one of the things they should absolutely do is spin out AWS, I think that helps solve that problem. But, back to IBM, Ginni Rometty was very clear at the World of Watson conference, the inaugural one, that we are not out trying to compete with our customers. I would think that resonates to a lot of people. >> Well, to be continued, right? Next month, back with IBM again? Right, three days? >> Yeah, I think third week in March. Monday, Tuesday, Wednesday, theCUBE's going to be there. Next week we're in the Bahamas. This week, actually. >> Not as a group taking vacation. Actually a working expedition. >> No, it's that blockchain conference. Actually, it's this week, what am I saying next week? >> Although I'm happy to volunteer to grip on that shoot, by the way. >> Flying out tomorrow, it's happening fast. >> Well, enjoyed this, always good to spend time with you. And good to spend time with you as well. So, you've been watching theCUBE, machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Have a good one. (techno music)

>> Announcer: Live from Madrid, Spain, it's the Cube, covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid, Spain everybody, this is the Cube, the leader in live tech coverage. We're here covering HPE Discover 2017. I'm Dave Vellante with my cohost for the week, Peter Burris, Randy Meyer is back, he's the vice president and general manager Synergy and Mission Critical Solutions at Hewlett Packard Enterprise and Paul Shellerd is here, the director of the Center for Theoretical Cosmology at Cambridge University, thank you very much for coming on the Cube. >> It's a pleasure. >> Good to see you again. >> Yeah good to be back for the second time this week. I think that's, day stay outlets play too. >> Talking about computing meets the cosmos. >> Well it's exciting, yesterday we talked about Superdome Flex that we announced, we talked about it in the commercial space, where it's taking HANA and Orcale databases to the next level but there's a whole different side to what you can do with in memory compute. It's all in this high performance computing space. You think about the problems people want to solve in fluid dynamics, in forecasting, in all sorts of analytics problems, high performance compute, one of the things it does is it generates massive amounts of data that people then want to do things with. They want to compare that data to what their model said, okay can I run that against, they want to take that data and visualize it, okay how do I go do that. The more you can do that in memory, it means it's just faster to deal with because you're not going and writing this stuff off the disk, you're not moving it to another cluster back and forth, so we're seeing this burgeoning, the HPC guys would call it fat nodes, where you want to put lots of memory and eliminate the IO to go make their jobs easier and Professor Shallard will talk about a lot of that in terms of what they're doing at the Cosmos Institute, but this is a trend, you don't have to be a university. We're seeing this inside of oil and gas companies, aerospace engineering companies, anybody that's solving these complex computational problems that have an analytical element to whether it's comparative model, visualize, do something with that once you've done that. >> Paul, explain more about what it is you do. >> Well in the Cosmos Group, of which I'm the head, we're interested in two things, cosmology, which is trying to understand where the universe comes from, the whole big bang and then we're interested in black holes, particularly their collisions which produce gravitational waves, so they're the two main areas, relativity and cosmology. >> That's a big topic. I don't even know where to start, I just want to know okay what have you learned and can you summarize it for a lay person, where are you today, what can you share with us that we can understand? >> What we do is we take our mathematical models and we make predictions about the real universe and so we try and compare those to the latest observational data. We're in a particularly exciting period of time at the moment because of a flood of new data about the universe and about black holes and in the last two years, gravitational waves were discovered, there's a Nobel prize this year so lots of things are happening. It's a very data driven science so we have to try and keep up with this flood of new data which is getting larger and larger and also with new types of data, because suddenly gravitational waves are the latest thing to look at. >> What are the sources of data and new sources of data that you're tapping? >> Well, in cosmology we're mainly interested in the cosmic microwave background. >> Peter: Yeah the sources of data are the cosmos. >> Yeah right, so this is relic radiation left over from the big bang fireball, it's like a photograph of the universe, a blueprint and then also in the distribution of galaxies, so 3D maps of the universe and we've only, we're in a new age of exploration, we've only got a tiny fraction of the universe mapped so far and we're trying to extract new information about the origin of the universe from that data. In relativity, we've got these gravitational waves, these ripples in space time, they're traversing across the universe, they're essentially earthquakes in the universe and they're sound waves or seismic waves that propagate to us from these very violent events. >> I want to take you to the gravitational waves because in many respects, it's an example of a lot of what's here in action. Here's what I mean, that the experiment and correct me if I'm wrong, but it's basically, you create a, have two lasers perpendicular to each other, shooting a signal about two or three miles in that direction and it is the most precise experiment ever undertaken because what you're doing is you're measuring the time it takes for one laser versus another laser and that time is a function of the slight stretching that comes from the gravitational rays. That is an unbelievable example of edge computing, where you have just the tolerances to do that, that's not something you can send back to the cloud, you gotta do a lot of the compute right there, right? >> That's right, yes so a gravitational wave comes by and you shrink one way and you stretch the other. >> Peter: It distorts the space time. >> Yeah you become thinner and these tiny, tiny changes are what's measured and nobody expected gravitational waves to be discovered in 2015, we all thought, oh another five years, another five years, they've always been saying, we'll discover them, we'll discover them, but it happened. >> And since then, it's been used two or three times to discover new types of things and there's now a whole, I'm sure this is very centric to what you're doing, there's now a whole concept of gravitational information, can in fact becomes an entirely new branch of cosmology, have I got that right? >> Yeah you have, it's called multimessenger astronomy now because you don't just see the universe in electromagnetic waves, in light, you hear the universe. This is qualitatively different, it's sound waves coming across the universe and so combining these two, the latest event was where they heard the event first, then they turned their telescope and they saw it. So much information came out of that, even information about cosmology, because these signals are traveling hundreds of billions of light years across to us, we're getting a picture of the whole universe as they propagate all that way, so we're able to measure the expansion rate of the universe from that point. >> The techniques for the observational, the technology for observation, what is that, how has that evolved? >> Well you've got the wrong guy here. I'm from the theory group, we're doing the predictions and these guys with their incredible technology, are seeing the data, seeing and it's imagined, the whole point is you've gotta get the predictions and then you've gotta look in the data for a needle in the haystack which is this signature of these black holes colliding. >> You think about that, I have a model, I'm looking for the needle in the haystack, that's a different way to describe an in memory analytic search pattern recognition problem, that's really what it is. This is the world's largest pattern recognition problem. >> Most precise, and literally. >> And that's an observation that confirms your theory right? >> Confirms the theory, maybe it was your theory. >> I'm actually a cosmologist, so in my group we have relativists who are actively working on the black hole collisions and making predictions about this stuff. >> But they're dampening vibration from passing trucks and these things and correcting it, it's unbelievable. But coming back to the technology, the technology is, one of the reasons why this becomes so exciting and becomes practical is because for the first time, the technology has gotten to the point where you can assume that the problem you're trying to solve, that you're focused on and you don't have to translate it in technology terms, so talk a little bit about, because in many respects, that's where business is. Business wants to be able to focus on the problem and how to think the problem differently and have the technology to just respond. They don't want to have to start with the technology and then imagine what they can do with it. >> I think from our point of view, it's a very fast moving field, things are changing, new data's coming in. The data's getting bigger and bigger because instruments are getting packed tighter and tighter, there's more information, so we've got a computational problem as well, so we've got to get more computational power but there's new types of data, like suddenly there's gravitational waves. There's new types of analysis that we want to do so we want to be able to look at this data in a very flexible way and ingest it and explore new ideas more quickly because things are happening so fast, so that's why we've adopted this in memory paradigm for a number of years now and the latest incarnation of this is the HP Superdome flex and that's a shared memory system, so you can just pull in all your data and explore it without carefully programming how the memory is distributed around. We find this is very easy for our users to develop data analytic pipelines to develop their new theoretical models and to compare the two on the single system. It's also very easy for new users to use. You don't have to be an advanced programmer to get going, you can just stay with the science in a sense. >> You gotta have a PhD in Physics to do great in Physics, you don't have to have a PhD in Physics and technology. >> That's right, yeah it's a very flexible program. A flexible architecture with which to program so you can more or less take your laptop pipeline, develop your pipeline on a laptop, take it to the Superdome and then scale it up to these huge memory problems. >> And get it done fast and you can iterate. >> You know these are the most brilliant scientists in the world, bar none, I made the analogy the other day. >> Oh, thanks. >> You're supposed to say aw, chucks. >> Peter: Aw, chucks. >> Present company excepted. >> Oh yeah, that's right. >> I made the analogy of, imagine I.M. Pei or Frank Lloyd Wright or someone had to be their own general contractor, right? No, they're brilliant at designing architectures and imagining things that no one else could imagine and then they had people to go do that. This allows the people to focus on the brilliance of the science without having to go become the expert programmer, we see that in business too. Parallel programming techniques are difficult, spoken like an old tandem guy, parallelism is hard but to the extent that you can free yourself up and focus on the problem and not have to mess around with that, it makes life easier. Some problems parallelize well, but a lot of them don't need to be and you can allow the data to shine, you can allow the science to shine. >> Is it correct that the barrier in your ability to reach a conclusion or make a discovery is the ability to find that needle in a haystack or maybe there are many, but. >> Well, if you're talking about obstacles to progress, I would say computational power isn't the obstacle, it's developing the software pipelines and it's the human personnel, the smart people writing the codes that can look for the needle in the haystack who have the efficient algorithms to do that and if they're cobbled by having to think very hard about the hardware and the architecture they're working with and how they've parallelized the problem, our philosophy is much more that you solve the problem, you validate it, it can be quite inefficient if you like, but as long as it's a working program that gets you to where you want, then your second stage you worry about making it efficient, putting it on accelerators, putting it on GPUs, making it go really fast and that's, for many years now we've bought these very flexible shared memory or in memory is the new word for it, in memory architectures which allow new users, graduate students to come straight in without a Master's degree in high performance computing, they can start to tackle problems straight away. >> It's interesting, we hear the same, you talk about it at the outer reaches of the universe, I hear it at the inner reaches of the universe from the life sciences companies, we want to map the genome and we want to understand the interaction of various drug combinations with that genetic structure to say can I tune exactly a vaccine or a drug or something else for that patient's genetic makeup to improve medical outcomes? The same kind of problem, I want to have all this data that I have to run against a complex genome sequence to find the one that gets me to the answer. From the macro to the micro, we hear this problem in all different sorts of languages. >> One of the things we have our clients, mainly in business asking us all the time, is with each, let me step back, as analysts, not the smartest people in the world, as you'll attest I'm sure for real, as analysts, we like to talk about change and we always talked about mainframe being replaced by minicomputer being replaced by this or that. I like to talk in terms of the problems that computing's been able to take on, it's been able to take on increasingly complex, challenging, more difficult problems as a consequence of the advance of technology, very much like you're saying, the advance of technology allows us to focus increasingly on the problem. What kinds of problems do you think physicists are gonna be able to attack in the next five years or so as we think about the combination of increasingly powerful computing and an increasingly simple approach to use it? >> I think the simplification you're indicating here is really going to more memory. Holding your whole workload in memory, so that you, one of the biggest bottlenecks we find is ingesting the data and then writing it out, but if you can do everything at once, then that's the key element, so one of the things we've been working on a great deal is in situ visualization for example, so that you see the black holes coming together and you see that you've set the right parameters, they haven't missed each other or something's gone wrong with your simulation, so that you do the post-processing at the same time, you never need the intermediate data products, so larger and larger memory and the computational power that balances with that large memory. It's all very well to get a fat node, but you don't have the computational power to use all those terrabytes, so that's why this in memory architecture of the Superdome Flex is much more balanced between the two. What are the problems that we're looking forward to in terms of physics? Well, in cosmology we're looking for these hints about the origin of the universe and we've made a lot of progress analyzing the Plank satellite data about the cosmic microwave background. We're honing in on theories of inflation, which is where all the structure in the universe comes from, from Heisenberg's uncertainty principle, rapid period of expansion just like inflation in the financial markets in the very early universe, okay and so we're trying to identify can we distinguish between different types and are they gonna tell us whether the universe comes from a higher dimensional theory, ten dimensions, gets reduced to three plus one or lots of clues like that, we're looking for statistical fingerprints of these different models. In gravitational waves of course, this whole new area, we think of the cosmic microwave background as a photograph of the early universe, well in fact gravitational waves look right back to the earliest moment, fractions of a nanosecond after the big bang and so it may be that the answers, the clues that we're looking for come from gravitational waves and of course there's so much in astrophysics that we'll learn about compact objects, about neutron stars, about the most energetic events there are in the whole universe. >> I never thought about the idea, because cosmic radiation background goes back what, about 300,000 years if that's right. >> Yeah that's right, you're very well informed, 400,000 years because 300 is. >> Not that well informed. >> 370,000. >> I never thought about the idea of gravitational waves as being noise from the big bang and you make sense with that. >> Well with the cosmic microwave background, we're actually looking for a primordial signal from the big bang, from inflation, so it's yeah. Well anyway, what were you gonna say Randy? >> No, I just, it's amazing the frontiers we're heading down, it's kind of an honor to be able to enable some of these things, I've spent 30 years in the technology business and heard customers tell me you transformed by business or you helped me save costs, you helped me enter a new market. Never before in 30 plus years of being in this business have I had somebody tell me the things that you're providing are helping me understand the origins of the universe. It's an honor to be affiliated with you guys. >> Oh no, the honor's mine Randy, you're producing the hardware, the tools that allow us to do this work. >> Well now the honor's ours for coming onto the Cube. >> That's right, how do we learn more about your work and your discoveries, inclusions. >> In terms of looking at. >> Are there popular authors we could read other than Stephen Hawking? >> Well, read Stephen's books, they're very good, he's got a new one called A Briefer History of Time so it's more accessible than the Brief History of Time. >> So your website is. >> Yeah our website is ctc.cam.ac.uk, the center for theoretical cosmology and we've got some popular pages there, we've got some news stories about the latest things that have happened like the HP partnership that we're developing and some nice videos about the work that we're doing actually, very nice videos of that. >> Certainly, there were several videos run here this week that if people haven't seen them, go out, they're available on Youtube, they're available at your website, they're on Stephen's Facebook page also I think. >> Can you share that website again? >> Well, actually you can get the beautiful videos of Stephen and the rest of his group on the Discover website, is that right? >> I believe so. >> So that's at HP Discover website, but your website is? >> Is ctc.cam.ac.uk and we're just about to upload those videos ourselves. >> Can I make a marketing suggestion. >> Yeah. >> Simplify that. >> Ctc.cam.ac.uk. >> Yeah right, thank you. >> We gotta get the Cube at one of these conferences, one of these physics conferences and talk about gravitational waves. >> Bone up a little bit, you're kind of embarrassing us here, 100,000 years off. >> He's better informed than you are. >> You didn't need to remind me sir. Thanks very much for coming on the Cube, great pleasure having you today. >> Thank you. >> Keep it right there everybody, Mr. Universe and I will be back after this short break. (upbeat techno music)

>> Man: Live from Madrid, Spain, it's the Cube! Covering HPE Discover Madrid, 2017. Brought to you by: Hewlett Packard Enterprise. >> Welcome back to Madrid, everybody, this is The Cube, the leader in live tech coverage. My name is Dave Vellante, I'm here with my co-host, Peter Burris, and this is our second day of coverage of HPE's Madrid Conference, HPE Discover. Sharad Singhal is back, Director of Machine Software and Applications, HPE and Corps and Labs >> Good to be back. And Mike Woodacre is here, a distinguished engineer from Mission Critical Solutions at Hewlett-Packard Enterprise. Gentlemen, welcome to the Cube, welcome back. Good to see you, Mike. >> Good to be here. >> Superdome Flex is all the rage here! (laughs) At this show. You guys are happy about that? You were explaining off-camera that is the first jointly-engineered product from SGI and HPE, so you hit a milestone. >> Yeah, and I came into Hewett Packard Enterprise just over a year ago with the SGI Acquisition. We're already working on our next generation in memory computing platform. We basically hit the ground running, integrated the engineering teams immediately that we closed the acquisition so we could drive through the finish line and with the product announcement just recently, we're really excited to get that out into the market. Really represent the leading in memory, computing system in the industry. >> Sharad, a high performance computer, you've always been big data, needing big memories, lots of performance... How has, or has, the acquisition of SGI shaped your agenda in any way or your thinking, or advanced some of the innovations that you guys are coming up with? >> Actually, it was truly like a meeting of the minds when these guys came into HPE. We had been talking about memory-driven computing, the machine prototype, for the last two years. Some of us were aware of it, but a lot of us were not aware of it. These guys had been working essentially in parallel on similar concepts. Some of the work we had done, we were thinking in terms of our road maps and they were looking at the same things. Their road maps were looking incredibly similar to what we were talking about. As the engineering teams came about, we brought both the Superdome X technology and The UV300 technology together into this new product that Mike can talk a lot more about. From my side, I was talking about the machine and the machine research project. When I first met Mike and I started talking to him about what they were doing, my immediate reaction was, "Oh wow wait a minute, this is exactly what I need!" I was talking about something where I could take the machine concepts and deliver products to customers in the 2020 time frame. With the help of Mike and his team, we are able to now do essentially something where we can take the benefits we are describing in the machine program and- make those ideas available to customers right now. I think to me that was the fun part of this journey here. >> So what are the key problems that your team is attacking with this new offering? >> The primary use case for the Superdome Flex is really high-performance in memory database applications, typically SAP Hana is sort of the industry leading solution in that space right now. One of the key things with the Superdome Flex, you know, Flex is the active word, it's the flexibility. You can start with a small building block of four socket, three terabyte building block, and then you just connect these boxes together. The memory footprint just grows linearly. The latency across our fabric just stays constant as you add these modules together. We can deliver up to 32 processes, 48 terabytes of in-memory data in a single rack. So it's really the flexibility, sort of a pay as you grow model. As their needs grow, they don't have to throw out the infrastructure. They can add to it. >> So when you take a look ultimately at the combination, we talked a little bit about some of the new types of problems that can be addressed, but let's bring it practical to the average enterprise. What can the enterprise do today, as a consequence of this machine, that they couldn't do just a few weeks ago? >> So it sort of builds on the modularity, as Lance explained. If you ask a CEO today, "what's my database requirement going to be in two or three years?" they're like, "I hope my business is successful, I hope I'm gonna grow my needs," but I really don't know where that side is going to grow, so the flexibility to just add modules and scale up the capacity of memory to bring that- so the whole concept of in-memory databases is basically bringing your online transaction processing and your data-analytics processing together. So then you can do this in real time and instead of your data going to a data warehouse and looking at how the business is operating days or weeks or months ago, I can see how it's acting right now with the latest updates of transactions. >> So this is important. You mentioned two different things. Number one is you mentioned you can envision- or three things. You can start using modern technology immediately on an extremely modern platform. Number two, you can grow this and scale this as needs follow, because Hana in memory is not gonna have the same scaling limitations that you know, Oracle on a bunch of spinning discs had. >> Mike: Exactly. >> So, you still have the flexibility to learn and then very importantly, you can start adding new functions, including automation, because now you can put the analytics and the transaction processing together, close that loop so you can bring transactions, analytics, boom, into a piece of automation, and scale that in unprecedented ways. That's kind of three things that the business can now think about. Have I got that right? >> Yeah, that's exactly right. It lets people really understand how their business is operating in real time, look for trends, look for new signatures in how the business is operating. They can basically build on their success and basically having this sort of technology gives them a competitive advantage over their competitors so they can out-compute or out-compete and get ahead of the competition. >> But it also presumably leads to new kinds of efficiencies because you can converge, that converge word that we've heard so much. You can not just converge the hardware and converge the system software management, but you can now increasingly converge tasks. Bring those tasks in the system, but also at a business level, down onto the same platform. >> Exactly, and so moving in memory is really about bringing real time to the problem instead of batch mode processing, you bring in the real-time aspect. Humans, we're interactive, we like to ask a question, get an answer, get on to the next question in real time. When processes move from batch mode to real time, you just get a step change in the innovation that can occur. We think with this foundation, we're really enabling the industry to step forward. >> So let's create a practical example here. Let's apply this platform to a sizeable system that's looking at customer behavior patterns. Then let's imagine how we can take the e-commerce system that's actually handling order, bill, fulfillment and all those other things. We can bring those two things together not just in a way that might work, if we have someone online for five minutes, but right now. Is that kind of one of those examples that we're looking at? >> Absolutely, you can basically- you have a history of the customers you're working with. In retail when you go in a store, the store will know your history of transactions with them. They can decide if they want to offer you real time discounts on particular items. They'll also be taking in other data, weather conditions to drive their business. Suddenly there's going to be a heat wave, I want more ice cream in the store, or it's gonna be freezing next week, I'm gonna order in more coats and mittens for everyone to buy. So taking in lots of transactional data, not just the actual business transaction, but environmental data, you can accelerate your ability to provide consumers with the things they will need. >> Okay, so I remember when you guys launched Apollo. Antonio Neri was running the server division, you might have had networking to him. He did a little reveal on the floor. Antonio's actually in the house over there. >> Mike: (laughs) Next door. There was an astronaut at the reveal. We covered it on the Cube. He's always been very focused on this part of the business of the high-performance computing, and obviously the machine has been a huge project. How has the leadership been? We had a lot of skeptics early on that said you were crazy. What was the conversation like with Meg and Antonio? Were they continuously supportive, were they sometimes skeptical too? What was that like? >> So if you think about the total amount of effort we've put in the machine program, and truly speaking, that kind of effort would not be possible if the senior leadership was not behind us inside this company. Right? A lot of us in HP labs were working on it. It was not just a labs project, it was a project where our business partners were working on it. We brought together engineering teams from the business groups who understood how projects were put together. We had software people working with us who were working inside the business, we had researchers from labs working, we had supply chain partners working with us inside this project. A project of this scale and scope does not succeed if it's a handful of researchers doing this work. We had enormous support from the business side and from our leadership team. I give enormous thanks to our leadership team to allow us to do this, because it's an industry thing, not just an HP Enterprise thing. At the same time, with this kind of investment, there's clearly an expectation that we will make it real. It's taken us three years to go from, "here is a vague idea from a group of crazy people in labs," to something which actually works and is real. Frankly, the conversation in the last six months has been, "okay, so how do we actually take it to customers?" That's where the partnership with Mike and his team has become so valuable. At this point in time, we have a shared vision of where we need to take the thing. We have something where we can on-board customers right now. We have something where, frankly, even I'm working on the examples we were talking about earlier today. Not everybody can afford a 16-socket, giant machine. The Superdome Flex allows my customer, or anybody who is playing with an application to start small, something that is reasonably affordable, try that application out. If that application is working, they have the ability to scale up. This is what makes the Superdome Flex such a nice environment to work in for the types of applications I'm worrying about because it takes something which when we had started this program, people would ask us, "when will the machine product be?" From day one, we said, "the machine product will be something that might become available to you in some form or another by the end of the decade." Well, suddenly with Mike, I think I can make it happen right now. It's not quite the end of the decade yet, right? So I think that's what excited me about this partnership we have with the Superdome Flex team. The fact that they had the same vision and the same aspirations that we do. It's a platform that allows my current customers with their current applications like Mike described within the context of say, SAB Hana, a scalable platform, they can operate it now. It's also something that allows them to involve towards the future and start putting new applications that they haven't even thought about today. Those were the kinds of applications we were talking about. It makes it possible for them to move into this journey today. >> So what is the availability of Superdome Flex? Can I buy it today? >> Mike: You can buy it today. Actually, I had the pleasure of installing the first early-access system in the UK last week. We've been delivering large memory platforms to Stephen Hawking's team at Cambridge University for the last twenty years because they really like the in-memory capability to allow them, as they say, to be scientists, not computer scientists, in working through their algorithms and data. Yeah, it's ready for sale today. >> What's going on with Hawking's team? I don't know if this is fake news or not, but I saw something come across that said he says the world's gonna blow up in 600 years. (laughter) I was like, uh-oh, what's Hawking got going now? (laughs) That's gotta be fun working with those guys. >> Yeah, I know, it's been fun working with that team. Actually, what I would say following up on Sharad's comment, it's been really fun this last year, because I've sort of been following the machine from outside when the announcements were made a couple of years ago. Immediately when the acquisition closed, I was like, "tell me about the software you've been developing, tell me about the photonics and all these technologies," because boy, I can now accelerate where I want to go with the technology we've been developing. Superdome Flex is really the first step on the path. It's a better product than either company could have delivered on their own. Now over time, we can integrate other learnings and technologies from the machine research program. It's a really exciting time. >> Excellent. Gentlemen, I always love the SGI acquisitions. Thought it made a lot of sense. Great brand, kind of put SGI back on the map in a lot of ways. Gentlemen, thanks very much for coming on the Cube. >> Thank you again. >> We appreciate you. >> Mike: Thank you. >> Thanks for coming on. Alright everybody, We'll be back with our next guest right after this short break. This is the Cube, live from HGE Discover Madrid. Be right back. (energetic synth)

>> Announcer: Live from Madrid, Spain. It's the Cube. Covering HP Discover Madrid 2017. Brought to you by Hewlett Packard Enterprise. >> Good afternoon from Madrid everybody. Good morning on the East Coast. Good really early morning on the West Coast. This is the Cube, the leader in live tech coverage. We're here day one at HPE Discover Madrid 2017. My name is Dave Velonte, I'm here with my cohost Peter Berse. Randy Meyers here is the Vice President and General Manager of the Mission Critical business unit at Hewlett Packard Enterprise. And he's joined by Alexander Zhuk, who is the SAP practice lead at Eldorado. Welcome to the Cube, thanks for coming on. >> Thanks for having us. >> Thank you. >> Randy we were just reminiscing about the number of times you've been on the Cube, consecutive years, it's like the Patriots winning the AFC East it just keeps happening. >> Or Cal Ripkin would probably be you. >> Me and Tom Brady. >> You're the Cal Ripken of the Cube. So give us the update, what's happening in the Mission Critical Business unit. What's going on here at Discover. >> Well, actually just lots of exciting things going on, in fact we just finished the main general session keynote. And that was the coming out party for our new Superdome Flex product. So, we've been in the Mission Critical space for quite some time now. Driving the HANA business, we've got 2500 customers around the world, small, large. And with out acquisition last year of SGI, we got this fabulous technology, that not only scales up to the biggest and most baddest thing that you can imagine to the point where we're talking about Stephen Hawking using that to explore the universe. But it scales down, four sockets, one terabyte, for lots of customers doing various things. So I look at that part of the Mission Critical business, and it's just so exciting to take technology, and watch it scale both directions, to the biggest problems that are out there, whether they are commercial and enterprise, and Alexander will talk about lots of things we're doing in that space. Or even high performance computing now, so we've kind of expanded into that arena. So, that's really the big news Super Dome Flex coming out, and really expanding that customer base. >> Yeah, Super Dome Flex, any memory in that baby? (laughing) >> 32 sockets, 48 terabyte if you want to go that big, and it will get bigger and bigger and bigger over time as we get more density that's there. And we really do have customers in the commercial space using that. I've got customers that are building massive ERP systems, massive data warehouses to address that kind of memory. >> Alright, let's hear from the customer. Alexander, first of all, tell us about your role, and tell us about Eldorado. >> I'm responsible for SAP basis and infrastructure. I'm working in Eldorado who is one of the largest consumer electronics network in Russia. We have more than 600 shops all over the country in more than 200 cities and towns, and have more than 16,000 employees. We have more than 50,000 stock keeping units, and proceeding over three and a half million orders with our international primarily. >> SAP practice lead, obviously this is a HANA story, so can you take us through your HANA journey, what led to the decision for HANA, maybe give us the before, during and after. Leading up to the decision to move to HANA, what was life like, and why HANA? >> We first moved our business warehouse system to HANA back in 2011. It's a time we got strong business requirements to have weak reporting. So, retail business, it's a business whose needs and very rapid decision making. So after we moved to HANA, we get the speed increasing of reports giving at 15 times. We got stock replenishment reports nine times faster. We got 50 minute sales reports every hour, instead of two hours. May I repeat this? >> No, it makes sense. So, the move to HANA was really precipitated by a need to get more data faster, so in memory allows you to do that. What about the infrastructure platform underneath, was it always HP at the time, that was 2011. What's HP's role, HPE's role in that, HANA? >> Initially we were on our business system in Germany, primarily on IBM solutions. But then according to the law requirements, we intended to go to Russia. And here we choose HP solutions as the main platform for our HANA database and traditional data bases. >> Okay Data residency forced you to move this whole solution back to Russia. If I may, Dave, one of the things that we're talking about and I want to test this with you, Alexander, is businesses not only have to be able to scale, but we talk about plastic infrastructure, where they have to be able to change their work loads. They have to be able to go up and down, but they also have to be able to add quickly. As you went through the migration process, how were you able to use the technology to introduce new capabilities into the systems to help your business to grow even faster? >> At that time, before migration, we had strong business requirements for our business growing and had some forecasts how HANA will grow. So we represented to our possible partners, our needs, for example, our main requirement was the possibility to scale up our CRM system up to nine terabytes memory. So, at that time, there was only HP who could provide that kind of solution. >> So, you migrated from a traditional RDBMS environment, your data warehouse previously was a traditional data base, is that right? And then you moved to HANA? >> Not all systems, but the most critical, the most speed critical system, it's our business warehouse and our CRM system. >> How hard was that? So, the EDW and the CRM, how difficult was that migration, did you have to freeze code, was it a painful migration? >> Yes, from the application point of view it was very painful, because we had to change everything, some our reports they had to be completely changed, reviewed, they had to adopt some abap code for the new data base. Also, we got some HANA level troubles, because it was very elaborate. >> Early days of HANA, I think it was announced in 2011. Maybe 2012... (laughing) >> That's one of the things for most customers that we talk to, it's a journey. You're moving from a tried and true environment that you've run for years, but you want the benefits in memory of speed, of massive data that you can use to change your business. But you have to plan that. It was a great point. You have to plan it's gonna scale up, some things might have to scale out, and at the same time you have to think about the application migration, the data migration, the data residency rules, different countries have different rules on what has to be there. And I think that's one of the things we try to take into account as HPE when we're designing systems. I want to let you partition them. I want to let you scale them up or down depending on the work load that's there. Because you don't just have one, you have BW and CRM, you have development environments, test environments, staging environments. The more we can help that look similar, and give you flexibility, the easier that is for customers. And then I think it's incumbent on us also to make sure we support our customers with knowledge, service, expertise, because it really is a journey, but you're right, 2011 it was the Wild West. >> So, give us the HPE HANA commercial. Everybody always tells us, we're great at HANA, we're best at HANA. What makes HPE best at HANA, different with HANA? >> What makes us best at HANA, one, we're all in on this, we have a partnership with SAP, we're designing for the large scale, as you said, that nobody else is building up into this space. Lots of people are building one terabyte things, okay. But when you really want to get real, when you want to get to 12 terabytes, when you want to get to 24 to 48. We're not only building systems capable of that, we're doing co-engineering and co-innovation work with SAP to make that work, to test that. I put systems on site in Waldorf, Germany, to allow them to go do that. We'll go diagnose software issues in the HANA code jointly, and say, here's where you're stressing that, and how we can go leverage that. You couple that with our services capability, and our move towards, you'll consume HANA in a lot of different ways. There will be some of it that you want on premise, in house, there will be some things that you say, that part of it might want to be in the Cloud. Yes, my answer to all of those things is yes. How do I make it easy to fit your business model, your business requirements, and the way you want to consume things economically? How do I alow you to say yes to that? 2500 customers, more than half of the installed base of all HANA systems worldwide reside on Hewlett Packard Enterprise. I think we're doing a pretty good job of enabling customers to say, that's a real choice that we can go forward with, not just today, but tomorrow. >> Alexander, are you doing things in the Cloud? I'm sure you are, what are you doing in the Cloud? Are you doing HANA in the Cloud? >> We have not traditional Cloud, as to use it to say, now we have a private Cloud. We have during some circumstance, we got all the hardware into our property. Now, it's operating by our partner. Between two company they are responsible for all those layers from hardware layer, service contracts, hardware maintenance, to the basic operation systems support, SEP support. >> So, if you had to do it all over again, what might you do differently? What advice would you give to other customers going down this journey? >> My advice is to at first, choose the right team and the right service provider. Because when you go to solution, some technical overview, architectural overview, you should get some confirmation from vendor. At first, it should be confirmed by HP. It should be confirmed by SEP. Also, there is a financial question, how to sponsor all this thing. And we got all these things from HP and our service partner. >> Right, give you the last word. >> So, one, it's an exciting time. We're watching this explosion of data happening. I believe we've only just scratched the surface. Today, we're looking at tens of thousands of skews for a customer, and looking at the velocity of that going through a retail chain. But every device that we have, is gonna have a sensor in it, it's gonna be connected all the time. It's gonna be generating data to the point where you say, I'm gonna keep it, and I'm gonna use it, because it's gonna let me take real time action. Some day they will be able to know that the mobile phone they care about is in their store, and pop up an offer to a customer that's exactly meaningful to do that. That confluence of sensor data, location data, all the things that we will generate over time. The ability to take action on that in real time, whether it's fix a part before it fails, create a marketing offer to the person that's already in the store, that allows them to buy more. That allows us to search the universe, in search for how did we all get here. That's what's happening with data. It is exploding. We are at the very front edge of what I think is gonna be transformative for businesses and organizations everywhere. It is cool. I think the advent of in memory, data analytics, real time, it's gonna change how we work, it's gonna change how we play. Frankly, it's gonna change human kind when we watch some of these researchers doing things on a massive level. It's pretty cool. >> Yeah, and the key is being able to do that wherever the data lives. >> Randy: Absolutely >> Gentlemen, thanks very much for coming on the Cube. >> Thank you for having us. >> Your welcome, great to see you guys again. Alright, keep it right there everybody, Peter and I will be back with our next guest, right after this short break. This is the Cube, we're live from HPE Discover Madrid 2017. We'll be right back. (upbeat music)

>> Announcer: Live from midtown Manhattan, it's theCUBE, covering BigData New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Okay welcome back everyone here live in New York City. This is theCUBE special presentation of our annual event with theCUBE and Wikibon Research called BigData NYC, it's our own event that we have every year, celebrating what's going on in the big data world now. It's evolving to all data, cloud applications, AI, you name it, it's happening. In the enterprise, the impact is huge for developers, the impact is huge. I'm John Furrier, cohost of the theCUBE, with Peter Burris, Head of Research, SiliconANGLE Media and General Manager of Wikibon Research. Our next guest is Jagane Sundar, who's the CTO of WANdisco, Cube alumni, great to see you again as usual here on theCUBE. >> Thank you John, thank you Peter, it's great to be back on theCUBE. >> So we've been talking the big data for many years, certainly with you guys, and it's been a great evolution. I don't want to get into the whole backstory and history, we covered that before, but right now is a really, really important time, we see you know the hurricanes come through, we see the floods in Texas, we've seen Florida, and Puerto Rico now on the main conversation. You're seeing it, you're seeing disasters happen. Disaster recovery's been the low hanging fruit for you guys, and we talked about this when New York City got flooded years and years ago. This is a huge issue for IT, because they have to have disaster recovery. But now it's moving more beyond just disaster recovery. It's cloud. What's the update from WANdisco? You guys have a unique perspective on this. >> Yes, absolutely. So we have capabilities to replicate between the cloud and Hadoop multi data centers across geos, so disasters are not a problem for us. And we have some unique technologies we use. One of the things we do is we can replicate in an active-active mode between different cloud vendors, between cloud and on-prem Hadoop, and we are the only game in town. Nobody else can do that. >> So okay let me just stop right there. When you say the only game in town I got a little skeptic here. Are you saying that nobody does active-active replication at all? >> That is exactly what I'm saying. We had some wonderful announcements from Hortonworks, they have a great product called the Dataplane. But if you dig deep, you'll find that it's actually an active-passive architecture, because to do active-active, you need this capability called the Paxos algorithm for resolving conflict. That's a very hard algorithm to implement. We have over 10 years' experience in that. That's what gives us our ability to do this active-active replication, between clouds, between on-prem and cloud. >> All right so just to take that a step further, I know we're having a CTO conversation, but the classic cliche is skate to where the puck is going to be. So you kind of didn't just decide one morning you're going to be the active-active for cloud. You kind of backed into this. You know the world spun in your direction, the puck came to you guys. Is that a fair statement? >> That is a very fair statement. We've always known there's tremendous value in this technology we own, and with the global infrastructure trends, we knew that this was coming. It wasn't called the cloud when we started out, but that's exactly what it is now, and we're benefiting from it. >> And the cloud is just a data center, it's just, you don't own it. (mumbles) Peter, what's your reaction to this? Because when he says only game in town, implies some scarcity. >> Well, WANdisco has a patent, and it actually is very interesting technology, if I can summarize very quickly. You do continuous replication based on writes that are performed against the database, so that you can have two writers and two separate databases and you guarantee that they will be synchronized at some point in time because you guarantee that the writing of the logs and the messaging to both locations >> Absolutely. >> in order, which is a big issue. You guys put a stamp on the stuff, and it actually writes to the different locations with order guaranteed, and that's not the way most replication software works. >> Yes, that's exactly right. That's very hard to do, and that's the only way for you to allow your clients in different data centers to write to the same data store, whether it's a database, a Hadoop folder, whether it's a bucket in a cloud object store, it doesn't matter. The core fact remains, the Paxos algorithm is the only way for you to do active-active replication, and ours is the only Paxos implementation that can work over the >> John: And that's patented by you guys? >> Yes, it's patented. >> And so someone to replicate that, they'd have to essentially reverse engineer and have a little twist on it to not get around the patents. Are you licensing the technology or are you guys hoarding it for yourselves? >> We have different ways of engaging with partners. We are very reasonable with that, and we work with several powerful partners >> So you partner with the technology. >> Yes. >> But the key thing, John, in answer to your question is that it's unassailable. I mean there's no argument, that is, companies move more towards a digital way of doing things, largely driven by what customers want, your data becomes more of an asset. As you data becomes more of an asset, you make money by using that data in more places, more applications and more times. That is possible with data, but the problem you end up with consistency issues, and for certain applications, it's not an issue, you're basically writing, or if you're basically reading data it's not an issue. But the minute that you're trying to write on behalf of a particular business event or a particular value proposition, then now you have a challenge, you are limited in how you can do it unless you have this kind of a technology. And so this notion of continuous replication in a world that's going to become increasingly dependent upon data, data that is increasingly distributed, data that you want to ensure has common governance and policy in place, technologies like WANdisco provides are going to be increasingly important to the overall way that a business organizes itself, institutes its work and makes sure it takes care of its data assets. >> Okay, so my next question then, thanks for the clarification, it's good input there and thanks for summarizing it like that, 'cause I couldn't have done that. But when we last talked, I always was enamored by the fact that you guys have the data center replication thing down. I always saw that as a great thing for you guys. Okay, I get that, that's an on-premise situation, you have active-active, good for disaster recovery, lot of use cases, people should be beating down your door 'cause you have a better mousetrap, I get that. Now how does that translate to the cloud? So take me through why the cloud now fits nicely with that same paradigm. >> So, I mean, these are industry trends, right. What we've found is that the cloud object stores are very, very cost effective and efficient, so customers are moving towards that. They're using their Hadoop applications but on cloud object stores. Now it's trivial for us to add plugins that enable us to replicate between a cloud object store on one side, and a Hadoop on the other side. It could also be another cloud object store from a different cloud provider on the other side. Once you have that capability, now customers are freed from lock-in from either a cloud vendor or a Hadoop vendor, and they love that, they're looking at it as another way to leverage their data assets. And we enable them to do that without fear of lock-in from any of these vendors. >> So on the cloud side, the regions have always been a big thing. So we've heard Amazon have a region down here, and there was fix it. We saw at VMworld push their VMware solution to only one western region. What's the geo landscape look like in the cloud? Does that relate to anything in your tech? >> So yes, it does relate, and one of the things that people forget is that when you create an Amazon S3 bucket, for example, you specify a region. Well, but this is the cloud, isn't it worldwide? Turns out that object store actually resides in one region, and you can use some shaky technologies like cross-region replication to eventually get the data to the other region. >> Peter: Which just boosts the prices you pay. >> Yes, not just boost the price. >> Well they're trying to save price but then they're exposed on reliability. >> Reliability, exactly. You don't know when the data's going to be there, there are no guarantees. What we offer is, take your cloud storage, but we'll guarantee that we can replicate it in a synchronous fashion to another region. Could be the same provider, could be another provider. That gives tremendous benefits to the customers. >> So you actually have a guarantee when you go to customers, say with an SLA guarantee? Do you back it up with like money back, what's the guarantee? >> So the guarantees are, you know we are willing to back it up with contracts and such like, and our customers put us through rigorous testing procedures, naturally. But we stand up to every one of those. We can scale and maintain the consistency guarantees that they need for modern businesses. >> Okay, so take me through the benefits. Who wants this? Because you can almost get kind of sucked into the complexities of it, and the nuances of cloud and everything as Peter laid out, it's pretty complex even as he simplified it. Who buys this? (laughs) I mean, who's the guy, is it the IT department, is it the ops guy, is it the facilities, who... >> So we sell to the IT departments, and they absolutely love the technology. But to go back to your initial statement, we have all these disasters happening, you know, hopefully people are all doing reasonably okay at the end of these horrible disasters, but if you're an enterprise of any size, it doesn't have to be a big enterprise, you cannot go back to your users or customers and say that because of a hurricane you cannot have access to your data. That's sometimes legally not allowed, and other times it's just suicide for a business >> And HPE in Houston, it's a huge plant down there. >> Jagane: Indeed. >> They got hit hard. >> Yep, in those sort of circumstances, you want to make sure that your data is available in multiple data centers spread throughout the world, and we give you that capability. >> Okay, what are some of the successes? Let's talk through now, obviously you've got the technology, I get that. Where's the stakes in the ground? Who's adopting it? I know you do a lot of biz dev deals. I don't know if they're actually OEM-type deals, or they're just licensing deals. Take us through to where your successes are with this technology. >> So, biz dev wise, we have a mix of OEM deals and licenses and co-selling agreements. The strong ones are all OEMs, of course. We have great partnerships with IBM, Amazon, Microsoft, just wonderful partnerships. The actual end customers, we started off selling mostly to the financial industry because they have a legal mandate, so they were the first to look into this sort of a thing. But now we've expanded into automobile companies. A lot of the auto companies are generating vast amounts of data from their cars, and you can't push all that data into a single data center, that's just not reasonable. You want to push that data into a single data store that's distributed across the world in just wherever the car is closest to. We offer that capability that nobody else can, so that we've got big auto manufacturers signed up, we've got big retailers signed up for exactly the same capability. You cannot imagine ingesting all that data into a single location. You want this replicated across, you want it available no matter what happens to any single region or a data center. So we've got tremendous success in retail, banking, and a lot of this is through partnerships again. >> Well congratulations, I got to ask, you know, what's new with you guys? Obviously you have success with the active-active. We'll dig into the Hortonworks things to check your comment around them not having it, so we'll certainly look with the Dataplane, which we like. We interviewed Rob Bearden. Love the announcement, but they don't have the active-active, we're going to document that, and get that on the record. But you guys are doing well. What's new here, what's in New York, what are some of your wins, can you just give a quick update on what's going on at WANdisco? >> Okay, so quick recap, we love the Hortonworks Dataplane as well. We think that we can build value into that ecosystem by building a plugin for them. And we love the whole technology. I have wonderful friends there as well. As for our own company, we see all of our, a lot of our business coming from cloud and hybrid environments. It's just the reality of the situation. You had, you know, 20 years ago, you had NFS, which was the great appender of all storage, but turned out to be very expensive, and you had 10 years, seven years ago you had HDFS come along, and that appended the cost model of NFS and SANs, which those industries were still working their way through. And now we have cloud object stores, which have appended the HDFS model, it's much more cost-efficient to operate using cloud object stores. So we will be there, we have replication products for that. >> John: And you're in the major clouds, you in Azure? >> Yes, we are in Azure. >> Google? >> Jagane: Yes, absolutely. >> AWS? >> AWS, of course. >> Oracle? >> Oracle, of course. >> So you got all the top four companies. >> We're in all of them. >> All right, so here's the next question is, >> And you're also in IBM stuff too. >> Yes, we're built tightly into IBM >> So you've got a pretty strong legacy >> And a monopoly. >> On the mainframe. >> Like the fiber channel of replication. (John and Jagane laugh) That was a bad analogy. I mean it's like... Well, I mean fiber channel has only limited suppliers 'cause they have unique technology, it was highly important. >> But the basic proposition is look, any customer that wants to ensure that a particular data source is going to be available in a distributed way, and you're going to have some degree of consistency, is going to look at this as an option. >> Yes. >> Well you guys certainly had a great team under your leadership, it's got great tech. The final question I have for you here is, you know, we've had many conversations about the industry, we like to pontificate, I certainly like to speculate, but now we have eight years of history now in the big data world, we look back, you know, we're doing our own event in New York City, you know, thanks to great support from you guys and other great friends in the community. Appreciate everyone out there supporting theCUBE, that's awesome. But the world's changed. So I got to ask you, you're a student of the industry, I know that and knowing you personally. What's been the success formula that keeps the winners around today, and what do people need to do going forward? 'Cause we've seen the train wreck, we've seen the dead bodies in the industry, we've kind of seen what's happened, there've been some survivors. Why did the current list of characters and companies survive, and what's the winning formula in your opinion to stay relevant as big data grows in a huge way from IoT to AI cloud and everything in between? >> I'll quote Stephen Hawking in this. Intelligence is the capability to adapt to changes. That's what keeps industries, that's what keeps companies, that what keeps executives around. If you can adapt to change, if you can see things coming, and adapt your core values, your core technology to that, you can offer customers a value proposition that's going to last a long time. >> And in a big data space, what is that adaptive key focus, what should they be focused on? >> I think at this point, it's extracting information from this volume of data, whether you use machine learning in the modern days, or whether it was simple hive queries, that's the value proposition, and making sure the data's available everywhere so you can do that processing on it, that remains the strength. >> So the whole concept of digital business suggests that increasingly we're going to see our assets rendered in some form as data. >> Yes. >> And we want to be able to ensure that that data is able to be where it needs to be when it needs to be there for any number of reasons. It's a very, very interesting world we're entering into. >> Peter, I think you have a good grasp on this, and I love the narrative of programming the world in real time. What's the phrase you use? It's real time but it's programming the world... Programming the real world. >> Yeah, programming the real world. >> That's a huge, that means something completely, it's not a tech, it's a not a speed or feed. >> Well the way we think about it, is that we look at IoT as a big information transducer, where information's in one form, and then you turn it into another form to do different kinds of work. And that big data's a crucial feature in how you take data from one form and turn it into another form so that it can perform work. But then you have to be able to turn that around and have it perform work back in the real world. There's a lot of new development, a lot of new technology that's coming on to help us do that. But any way you look at it, we're going to have to move data with some degree of consistency, we're still going to have to worry about making sure that if our policy says that that action needs to take place there, and that action needs to take place there, that it actually happens the way we want it to, and that's going to require a whole raft of new technologies. We're just at the very beginning of this. >> And active-active, things like active-active in what you're talking about really is about value creation. >> Well the thing that makes active-active interesting is, again, borrowing from your terms, it's a new term to both of us, I think, today. I like it actually. But the thing that makes it interesting is the idea that you can have a source here that is writing things, and you can have a source over there that are writing things, and as a consequence, you can nonetheless look at a distributed database and keep it consistent. >> Consistent, yeah. >> And that is a major, major challenge that's going to become increasingly a fundamental feature of our digital business as well. >> It's an enabling technology for the value creation and you call it work. >> Yeah, that's right. >> Transformation of work. Jagane, congratulations on the active-active, and WANdiscos's technology and all your deals you're doing, got all the cloud locked up. What's next? Well you going to lock up the edge? You're going to lock up the edge too, the cloud. >> We do like this notion of the edge cloud and all the intermediate steps. We think that replicating data between those systems or running consistent compute across those systems is an interesting problem for us to solve. We've got all the ingredients to solve that problem. We will be on that. >> Jagane Sundar, CTO of WANdisco, back on theCUBE, bringing it down. New tech, whole new generation of modern apps and infrastructure happening in distributed and decentralized networks. Of course theCUBE's got it covered for you, and more live coverage here in New York City for BigData NYC, our annual event, theCUBE and Wikibon here in Hell's Kitchen in Manhattan, more live coverage after this short break.

>> 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)

>> Welcome everyone. Thank you for coming to the Intel AI lounge and joining us here for this economist world event. My name is Jack. I'm the chief architect of our autonomist driving solutions at Intel and I'm very happy to be here and to be joined by an esteemed panel of colleagues who are joining to, I hope, engage you all in a frayed dialogue and discussion. There will be time for questions as well, so keep your questions in mind. Jot them down so you ask them to us later. So first, let me introduce the panel. Next to me we have Michelle, who's the co-founder and CEO of Fine Mind. She just did an interview here shortly. Fine Mind is a company that provides a technology platform for retailers and brands that uses artificial intelligence as the heart of the experiences that her company's technology provides. Joe from Intel is the head of partnerships and acquisitions for artificial intelligence and software technologies. He participated in the recent acquisition of Movidius, a computer vision company that Intel recently acquired and is involved in a lot of smart city activities as well. And then finally, Sarush, who is data scientist by training, but now has JDA labs, which is researching emerging technologies and their application in the supply chain worldwide. So at the end of the day, the internet things that artificial intelligence really promises to improve our lives in quite incredible ways and change the way that we live and work. Often times the first thing that we think about when we think about AI is Skynet, but we at Intel believe in AI for good and that there's a lot of things that can happen to improve the way people live, work, and enjoy life. So as things in the Internet, as things become connected, smart, and automated, artificial intelligence is really going to be at the heart of those new experiences. So as I said my role is the architect for autonomous driving. It's a common place when people think about artificial intelligence, because what we're trying to do is replace a human brain with a machine brain, which means we need to endow that machine with intelligent thoughts, contexts, experiences. All of these things that sort of make us human. So computer vision is the space, obviously, with cameras in your car that people often think about, but it's actually more complicated than that. How many of us have been in a situation on a two lane road, maybe there's a car coming towards us, there's a road off to the right, and you sort of sense, "You know what? That car might turn in front of me." There's no signal. There's no real physical cue, but just something about what that driver's doing where they're looking tells us. So what do we do? We take our foot off the accelerator. We maybe hover it over the brake, just in case, right? But that's intelligence that we take for granted through years and years and years of driving experience that tells us something interesting is happening there. And so that's the challenge that we face in terms of how to bring that level of human intelligence into machines to make our lives better and richer. So enough about automated vehicles though, let's talk to our panelists about some of the areas in which they have expertise. So first for Michelle, I'll ask... Many of us probably buy stuff online everyday, every week, every hour, hourly delivery now. So a lot has been written about the death of traditional retail experiences. How will artificial intelligence and the technology that your company has rejuvenate that retail experience, whether it be online or in the traditional brick and mortar store? >> Yeah, excuse me. So one of the things that I think is a common misconception. You hear about the death of the brick and mortar store, the growth of e-commerce. It's really that e-commerce is beating brick and mortar in growth only and there's still over 90% of the world's commerce is done in physical brick and mortar store. So e-commerce, while it has the growth, has a really long way to go and I think one of the things that's going to be really hard to replace is the very human element of interaction and connection that you get by going to a store. So just because a robot named Pepper comes up to you and asks you some questions, they might get you the answer you need faster and maybe more efficiently, but I think as humans we crave interaction and shopping for certain products especially, is an experience better enjoyed in person with other people, whether that's an associate in the store or people you come with to the store to enjoy that experience with you. So I think artificial intelligence can help it be a more frictionless experience, whether you're in store or online to get you from point A to buying the thing you need faster, but I don't think that it's going to ever completely replace the joy that we get by physically going out into the world and interacting with other people to buy products. >> You said something really profound. You said that the real revolution for artificial intelligence in retail will be invisible. What did you mean by that? >> Yeah, so right now I think that most of the artificial intelligence that's being applied in the retail space is actually not something that shoppers like you and I see when we're on a website or when we're in the store. It's actually happening behind the scenes. It's happening to dynamically change the webpage to show you different stuff. It's happening further up the supply chain, right? With how the products are getting manufactured, put together, packaged, shipped, delivered to you, and that efficiency is just helping retailers be smarter and more effective with their budgets. And so, as they can save money in the supply chain, as they can sell more product with less work, they can reinvest in experience, they can reinvest in the brand, they can reinvest in the quality of the products, so we might start noticing those things change, but you won't actually know that that has anything to do with artificial intelligence, because not always in a robot that's rolling up to you in an aisle. >> So you mentioned the supply chain. That's something that we hear about a lot, but frankly for most of us, I think it's very hard to understand what exactly that means, so could you educate us a bit on what exactly is the supply chain and how is artificial intelligence being implied to improve it? >> Sure, sure. So for a lot of us, supply chain is the term that we picked up when we went to school or we read about it every so often, but we're not that far away from it. It is in fact a key part of what Michelle calls the invisible part of one's experience. So when you go to a store and you're buying a pair of shoes or you're picking up a box of cereal, how often do we think about, "How did it ever make it's way here?" We're the constituent components. They probably came from multiple countries and so they had to be manufactured. They had to be assembled in these plants. They had to then be moved, either through an ocean vessel or through trucks. They probably have gone through multiple warehouses and distribution centers and then finally into the store. And what do we see? We want to make sure that when I go to pick up my favorite brand of cereal, it better be there. And so, one of the things where AI is going to help and we're doing a lot of active work in this, is in the notion of the self learning supply chain. And what that means is really bringing in these various assets and actors of the supply chain. First of all, through IOT and others, generating the data, obviously connecting them, and through AI driving the intelligence, so that I can dynamically figure out the fact that the ocean vessel that left China on it's way to Long Beach has been delayed by 24 hours. What does that mean when you go to a Foot Locker to buy your new pair of shoes? Can I come up with alternate sourcing decisions, so it's not just predicting. It's prescribing and recommending as well. So behind the scenes, bringing in a lot of the, generating a lot of the data, connecting a lot of these actors and then really deriving the smarts. That's what the self learning supply chain is all about. >> Are supply chains always international or can they be local as well? >> Definitely local as well. I think what we've seen over the last decades, it's kind of gotten more and more global, but a lot of the supply chain can really just be within the store as well. You'd be surprised at how often retailers do not know where their product is. Even is it in the front of the store? Is it in the back of the store? Is it in the fitting room? Even that local information is not really available. So to have sensors to discover where things are and to really provide that efficiency, which right now doesn't exist, is a key part of what we're doing. >> So Joe, as you look at companies out there to partner or potentially acquire, do you tend to see technologies that are very domain specific for retail or supply chain or do you see technologies that could bridge multiple different domains in terms of the experiences we could enjoy? >> Yeah, definitely. So both. A lot of infant technologies start out in very niched use cases, but then there are technologies that are pervasive across multiple geographies and multiple markets. So, smart cities is a good way to look at that. So let's level set really quick on smart cities and how we think about that. I have a little sheet here to help me. Alright, so, if anybody here played Sim City before, you have your little city that's a real world that sits here, okay? So this is reality and you have little buildings and cars and they all travel around and you have people walking around with cell phones. And what's happening is as we develop smart cities, we're putting sensors everywhere. We're putting them around utilities, energies, water. They're in our phones. We have cameras and we have audio sensors in our phones. We're placing these on light poles, which is existing sustaining power points around the city. So we have all these different sensors and they're not just cameras and microphones, but they're particulate sensors. They're able to do environmental monitoring and things like that. And so, what we have is we have this physical world with all these sensors here. And then what we have is we've created basically this virtual world that has a great memory because it has all the data from all the sensors and those sensors really act as ties, if you think of it like a quilt, trying a quilt together. You bring it down together and everywhere you have a stitch, you're stitching that virtual world on top of the physical world and that just enables incredible amounts of innovation and creation for developers, for entrepreneurs, to do whatever they want to do to create and solve specific problems. So what really makes that possible is communications, connectivity. So that's where 5G comes in. So with 5G it's not just a faster form of connectivity. It's new infrastructure. It's new communication. It includes multiple types of communication and connectivity. And what it allows it to do is all those little sensors can talk to each other again. So the camera on the light pole can talk to the vehicle driving by or the sensor on the light pole. And so you start to connect everything and that's really where artificial intelligence can now come in and sense what's going on. It can then reason, which is neat, to have computer or some sort of algorithm that actually reasons based on a situation that's happening real time. And it acts on that, but then you can iterate on that or you can adapt that in the future. So if we think of an actual use case, we'll think of a camera on a light post that observes an accident. Well it's programmed to automatically notify emergency services that there's been an accident. But it knows the difference between a fender bender and an actual major crash where we need to send an ambulance or maybe multiple firetrucks. And then you can create iterations and that learns to become more smart. Let's say there was a vehicle that was in the accident that had a little yellow placard on it that said hazard. You're going to want to send different types of emergency services out there. So you can iterate on what it actually does and that's a fantastic world to be in and that's where I see AI really playing. >> That's a great example of what it's all about in terms of making things smart, connective, and autonomous. So Michelle as somebody who has founded the company and the space with technology that's trying to bring some of these experiences to market, there may be folks in the audience who have aspirations to do the same. So what have you learned over the course of starting your company and developing the technology that you're now deploying to market? >> Yeah, I think because AI is such a buzz word. You can get a dot AI domain now, doesn't mean that you should use it for everything. Maybe 7, 10, 15 years ago... These trends have happened before. In the late 90s, it was technology and there was technology companies and they sat over here and there was everybody else. Well that not true anymore. Every company uses technology. Then fast forward a little bit, there was social media was a thing. Social media was these companies over here and then there was everybody else and now every company needs to use social media or actually maybe not. Maybe it's a really bad idea for you to spend a ton of money on social media and you have to make that choice for yourself. So the same thing is true with artificial intelligence and what I tell... I did a panel on AI for Adventure Capitalists last week, trying to help them figure out when to invest and how to evaluate and all that kind of stuff. And what I would tell other aspiring entrepreneurs is "AI is means to an end. "It's not an end in itself." So unless you're a PH.D in machine learning and you want to start an AI as a service business, you're probably not going to start an AI only company. You're going to start a company for a specific purpose, to solve a problem, and you're going to use AI as a means to an end, maybe, if it makes sense to get there, to make it more efficient and all that stuff. But if you wouldn't get up everyday for ten years to do this business that's going to solve whatever problem you're solving or if you wouldn't invest in it if AI didn't exist, then adding dot AI at the end of a domain is not going to work. So don't think that that will help you make a better business. >> That's great advice. Thank you. Surash, as you talked about the automation then of the supply chain, what about people? What about the workers whose jobs may be lost or displaced because of the introduction of this automation? What's your perspective on that? >> Well, that's a great question. It's one that I'm asked quite a bit. So if you think about the supply chain with a lot of the manufacturing plants, with a lot of the distribution centers, a lot of the transportation, not only are we talking about driverless cars as in cars that you and I own, but we're talking about driverless delivery vehicles. We're talking about drones and all of these on the surface appears like it's going to displace human beings. What humans used to do, now machines will do and potentially do better. So what are the implications around human beings. So I'm asked that question quite a bit, especially from our customers and my general perception on this is that I'm actually cautiously optimistic that human beings will continue to do things that are strategic. Human beings will continue to do things that are creative and human being will probably continue to do things that are truly catastrophic, that machines simply have not been able to learn because it doesn't happen very often. One thing that comes to mind is when ATM machines came about several years ago before my time, that displaced a lot of teller jobs in the banking industry, but the banking industry did not go belly up. They found other things to do. If anything, they offered more services. They were more branches that were closed and if I were to ask any of you now if you would go back and not have 24/7 access to cash, you would probably laugh at me. So the thing is, this is AI for good. I think these things might have temporary impact in terms of what it will do to labor and to human beings but I think we as human beings will find bigger, better, different things to do and that's just in the nature of the human journey. >> Yeah, there's definitely a social acceptance angle to this technology, right? Many of us technologists in the room, it's easier for us to understand what the technology is, how it works, how it was created, but for many of our friends and family, they don't. So there's a social acceptance angle to this. So Michelle as you see this technology deployed in retail environments, which is a space where almost every person in every country goes, how do you think about making it feel comfortable for people to interact with this kind of technology and not be afraid of the robots or the machines behind the curtain. >> Yeah, that's a great question. I think that user experience always has to come first, so if you're using AI for AI's sake or for the cool factor, the wow factor, you're already doing it wrong. Again, it needs to solve a problem and what I tend to tell people who are like, "Oh my God. AI sounds so scary. "We can't let this happen." I'm like, "It's already happening "and you're already liking it. "You just don't know "because it's invisible in a lot of ways." So if you can point of those scenarios where AI has already benefited you and it wasn't scary because it was a friendly kind of interaction, you might not even have realized it was there versus something that looks so different and... Like panic driving. I think that's why the driverless car thing is a big deal because you're so used to seeing, in America at least, someone on the left side of the car in the front seat. And not seeing that is like, woah, crazy. So I think that it starts with the experience and making it an acceptable kind of interface or format that doesn't give you that, "Oh my God. Something is wrong here," kind of feeling. >> Yeah, that's a great answer. In fact, it reminds me there was this really amazing study by a Professor Nicholas Eppily that was published in the journal of social psychology and the name of this study was called A Mind In A Machine. And what he did was he took subjects and had a fully functional automated vehicle and then a second identical fully functional automated vehicle, but this one had a name and it had a voice and it had sort of a personality. So it had human anthropomorphics characteristics. And he took people through these two different scenarios and in both scenarios he's evil and introduced a crash in the scenario where it was unavoidable. There was nothing going to happen. You were going to get into an accident in these cars. And then afterwards, he pulled the subjects and said, "Well, what did you feel about that accident? "First, what did you feel about the car?" They were more comfortable in the one that had anthropomorphic features. They felt it was safer and they'd be more willing to get into it, which is not terribly surprising, but the kicker was the accident. In the vehicle that had a voice and a name, they actually didn't blame the self-driving car they were in. They blamed the other car. But in the car that didn't have anthropomorphic features, they blamed the machine. They said there's something wrong with that car. So it's one of my favorite studies because I think it does illustrate that we have to remember the human element to these experiences and as artificial intelligence begins to replace humans, or some of us even, we need to remember that we are still social beings and how we interact with other things, whether they be human or non-human, is important. So, Joe, you talk about evaluating companies. Michelle started a company. She's gotten funding. As you go out and look at new companies that are starting up, there's just so much activity, companies that just add dot AI to the name as Michelle said, how do you cut through the noise and try to get to the heart of is there any value in a technology that a company's bringing or not? >> Definitely. Well, each company has it's unique, special sauce, right? And so, just to reiterate what Michelle was talking about, we look for companies that are really good at doing what they do best, whatever that may be, whatever that problem that they're solving that a customer's willing to pay for, we want to make sure that that company's doing that. No one wants a company that just has AI in the name. So we look for that number one and the other thing we do is once we establish that we have a need or we're looking at a company based on either talent or intellectual property, we'll go in and we'll have to do a vetting process and it takes a whole. It's a very long process and there's legal involved but at the end of the day, the most important thing for the start up to remember is to continue doing what they do best and continue to build upon their special sauce and make sure that it's very valuable to their customer. And if someone else wants to look at them for acquisition so be it, but you need to be meniacally focused on your own customer. That's my two cents. >> I'm thinking again about this concept of embedding human intelligence, but humans have biases right? And sometimes those biases aren't always good. So how do we as technologists in this industry try to create AI for good and not unintentionally put some of our own human biases into models that we train about what's socially acceptable or not? Anyone have any thoughts on that? >> I actually think that the hype about AI taking over and destroying humanity, it's possible and I don't want to disagree with Steven Hawking as he's way smarter than I am. But he kind of recognizes it could go both ways and so right now, we're in a world where we're still feeding the machine. And so, there's a bunch of different issues that came up with humans feeding the machine with their foibles of racism and hatred and bias and humans experience shame which causes them to lash out and what to put somebody else down. And so we saw that with Tay, the Microsoft chatbot. We saw that with even Google's fake news. They're like picking sources now to answer the question in the top box that might be the wrong source. Ads that Google serves often show men high paying jobs, $200,000 a year jobs, and women don't get those same ones. So if you trace that back, it's always coming back to the inputs and the lens that humans are coming at it from. So I actually think that we could be in a way better place after this singularity happens and the machines are smarter than us and they take over and they become our overlords. Because when we think about the future, it's a very common tendency for humans to fill in the blanks of what you don't know in the future with what's true today. And I was talking to you guys at lunch. We were talking about this harbored psychology professor who wrote a book and in the book he was talking about how 1950s, they were imagining the future and all these scifi stories and they have flying cars and hovercrafts and they're living in space, but the woman still stays at home and everyone's white. So they forgot to extrapolate the social things to paint the picture in, but I think when we're extrapolating into the future where the computers are our overlords, we're painting them with our current reality, which is where humans are kind of terrible (laughs). And maybe computers won't be and they'll actually create this Utopia for us. So it could be positive. >> That's a very positive view. >> Thanks. >> That's great. So do we have this all figured out? Are there any big challenges that remain in our industries? >> I want to add a little bit more to the learning because I'm a data scientist by training and a lot of times, I run into folks who think that everything's been figured out. Everything is done. This is so cool. We're good to go and one of the things that I share with them is something that I'm sure everyone here can relate to. So if a kindergartner goes to school and starts to spell profanity, that's not because the kid knows anything good or bad. That is what the kid has learned at home. Likewise, if we don't train machines well, it's training will in fact be biased to your point. So one of the things that we have to kep in mind when we talk about this is we have to be careful as well because we're the ones doing the training. It doesn't automatically know what is good or bad unless that set of data is also fed to it. So I just wanted to kind of add to your... >> Good. Thank you. So why don't we open it up a little bit for questions. Any questions in the audience for our panelists? There's one there looks like (laughs). Emily, we'll get to you soon. >> I had a question for Sarush based on what you just said about us training or you all training these models and teaching them things. So when you deploy these models to the public with them being machine learning and AI based, is it possible for us to retrain them and how do you build in redundancies for the public like throwing off your model and things like that? What are some of the considerations that go into that? >> Well, one thing for sure is training is continuous. So no system should be trained once, deployed, and then forgotten. So that is something that we as AI professionals need to absolutely, because... Trends change as well. What was optimal two years ago is no longer optimal. So that part needs to continue to happen and we're the where the whole IOT space is so important is it will continue to generate relevant consumable data that these machines can continuously learn. >> So how do you decide what data though, is good or bad, as you retrain and evolve that data over time? As a data scientist, how do you do selection on data? >> So, and I want to piggyback on what Michelle said because she's spot on. What is the problem that you're trying to solve? It always starts from there because we have folks who come in to CIOs, "Oh look. "When big data was hot, we started to collect "a lot of the data, but nothing has happened." But data by itself doesn't automatically do magic for you, so we ask, "What kind of problem are you trying to solve? "Are you trying to figure out "what kinds of products to sell? "Are you trying to figure out "the optimal assortment mix for you? "Are you trying to find the shortest path "in order to get to your stores?" And then the question is, "Do you now have the right data "to solve that problem?" A lot of times we put the science and I'm a data scientist by training. I would love to talk about the science, but really, it's the problem first. The data and the science, they come after. >> Thanks, good advice. Any other questions in the audience? Yes, one right up here. (laughing) >> Test, test. Can you hear me? >> Yep. >> So with AI machinery becoming more commonplace and becoming more accessible to developers and visionaries and thinkers alike rather than being just a giant warehouse of a ton of machines and you get one tiny machine learning, do you foresee more governance coming into play in terms of what AI is allowed to do and the decisions of what training data is allowed to be fed to Ais in terms of influence? You talk about data determining if AI will become good or bad, but humans being the ones responsible for the training in the first place, obviously, they can use that data to influence as they, just the governance and the influence. >> Jack: Who wants to take that one? >> I'll take a quick stab at it. So, yes, it's going to be an open discussion. It's going to have to take place, because really, they're just machines. It's machine learning. We teach it. We teach it what to do, how to act. It's just an extension of us and in fact, I think you had a really great conversation or a statement at lunch where you talked about your product being an extension of a designer because, and we can get into that a little bit, but really, it's just going to do what we tell it to do. So there's definitely going to have to be discussions about what type of data we feed. It's all going to be centered around the use case and what that solves the use case. But I imagine that that will be a topic of discussion for a long time about what we're going to decide to do. >> Jack: Michelle do you want to comment on this thought of taking a designer's brain and putting it into a model somehow? >> Well, actually, what I wanted to say was that I think that the regulation and the governance around it is going to be self imposed by the the developer and data science community first, because I feel like even experts who have been doing this for a long time don't rally have their arms fully around what we're dealing with here. And so to expect our senators, our congressmen, women, to actually make regulation around it is a lot, because they're not technologists by training. They have a lot of other stuff going on. If the community that's already doing the work doesn't quite know what we're dealing with, then how can we expect them to get there? So I feel like that's going to be a long way off, but I think that the people who touch and feel and deal with models and with data sets and stuff everyday are the kind of people who are going to get together and self-regulate for a while, if they're good hearted people. And we talk about AI for good. Some people are bad. Those people won't respect those convenance that we come up with, but I think that's the place we have to start. >> So really you're saying, I think, for data scientists and those of us working in this space, we have a social, ethical, or moral obligation to humanity to ensure that our work is used for good. >> Michelle: No pressure. (laughing) >> None taken. Any other questions? Anything else? >> I just wanted to talk about the second part of what she said. We've been working with a company that builds robots for the store, a store associate if you will. And one of their very interesting findings was that the greatest acceptance of it right now has been at car dealerships because when someone goes to the car dealer and we all have had terrible experiences doing that. That's why we try to buy it online, but just this perception that a robot would be unbiased, that it will give you the information without trying to push me one way or the other. >> The hard sell. >> So there's that perception side of it too that, it isn't that the governance part of your question, but more the biased perception side of what you said. I think it's fascinating how we're already trained to think that this is going to have an unbiased opinion, whether or not that true. >> That's fascinating. Very cool. Thank you Sarush. Any other questions in the audience? No, okay. Michelle, could I ask, you've got a station over there that talks a little bit more about your company, but for those that haven't seen it yet, could you tell us a little bit about what is the experience like or how is the shopping experience different for someone that's using your company's technology than what it was before? >> Oh, free advertising. I would love to. No, but actually, I started this company because as a consumer I found myself going back to the user experience piece, just constantly frustrated with the user experience of buying products one at a time and then getting zero help. And then here I am having to google how to wear a white blazer to not look like an idiot in the morning when I get dressed with my white blazer that I just bought and I was excited about. And it's a really simple thing, which is how do I use the product that I'm buying and that really simple thing has been just abysmally handled in the retail industry, because the only tool that the retailers have right now are manual. So in fashion, some of our fashion customers like John Varvatos is an example we have over there, it's like a designer for high-end men's clothing, and John Varvatos is a person, it's not just the name of the company. He's an actual person and he has a vision for what he wants his products to look like and the aesthetic and the style and there's a rockstar vibe and to get that information into the organization, he would share it verbally with PDFs, thing like that. And then his team of merchandisers would literally go manually and make outfits on one page and then go make an outfit on another page with the same exact items and then products would go out of stock and they'd go around in circles and that's a terrible, terrible job. So to the conversation earlier about people losing jobs because of artificial intelligence. I hope people do lose jobs and I hope they're the terrible jobs that no one wanted to do in the first place, because the merchandisers that we help, like the one form John Varvatos, literally said she was weeks away from quitting and she got a new boss and said, "If you don't ix this part of my job, I'm out of here." And he had heard about us. He knew about us and so he brought us in to solve that problem. So I don't think it's always a bad thing, because if we can take that route, boring, repetitive task off of human's plates, what more amazing things can we do with our brain that is only human and very unique to us and how much more can we advance ourselves and our society by giving the boring work to a robot or a machine. >> Well, that's fantastic. So Joe, when you talk about Smart Cities, it seems like people have been talking about Smart Cities for decades and often people cite funding issues, regulatory environment or a host of other reasons why these things haven't happened. Do you think we're on the cusp of breaking through there or what challenges still remain for fulfilling that vision of a smart city? >> I do, I do think we're on the cusp. I think a lot of it has to do, largely actually, with 5G and connectivity, the ability to process and send all this data that needs to be shared across the system. I also think that we're getting closer and more conscientious about security, which is a major issue with IOT, making sure that our in devices or our edge devices, those things out there sensing, are secure. And I think interocular ability is something that we need to champion as well and make sure that we basically work together to enable these systems. So very, very difficult to create little, tiny walled gardens of solutions in a smart city. You may corner a certain part of the market, but you're definitely not going to have that ubiquitous benefit to society if you establish those little walled gardens, so those are the areas I think we need to focus on and I think we are making serious progress in all of them. >> Very good. Michelle, you mentioned earlier that artificial intelligence was all around us in lots of places and things that we do on a daily basis, but we probably don't realize it. Could you share a couple examples? >> Yeah, so I think everything you do online for the most part, literally anything you might do, whether that's googling something or you go to some article, the ads might be dynamically picked for you using machine learning models that have decided what is appropriate based on you and your treasure trove of data that you have out there that you're giving up all the time and not really understanding you're giving up >> The shoes that follow you around the internet right? >> Yeah, exactly. So that's basically anything online. I'm trying to give in the real-world. I think that, to your point earlier about he supply chain, just picking a box of cereal off the shelf and taking it home, there's not artificial intelligence in that at all, but the supply chain behind it. So the supply chain behind pretty much everything we do even in television, like how media gets to us and get consumed. At some point in the supply chain, there's artificial intelligence playing in there as well. >> So to start us in the supply chain where we can get the same day even within the hour delivery. How do you get better than that? What's coming that's innovative in the supply chain that will be new in the future? >> Well, so that is one example of it, but you'd be surprised at how inefficient the supply chain is, even with all the advances that have already gone in, whether it's physical advances around building modern warehouses and modern manufacturing plants, whether it's through software and others that really help schedule things and optimize things. What has happened in the supply chain just given how they've evolved is they're very siloed, so a lot of times the manufacturing plant does things that the distribution folks do not know. The distribution folks do things that the transportation folks don't know and then the store folks know nothing other than when the trucks pulls up, that's the first time they find out about things. So where the great opportunity in my mind is, in the space that I'm in, is really the generation of data, the connection of data, and finally, deriving the smarts that really help us improve efficiency. There's huge opportunity there. And again, we don't know it because it's all invisible to us. >> Good. Let me pause and see if there's any questions in the audience. There, we got one there. >> Thank you. Hi guys, you alright? I just had a question about ethics and the teaching of ethics. As you were saying, we feed the artificial intelligence, whereas in a scenario which is probably a little bit more attuned to automated driving, in a car crash scenario between do we crash these two people or three people? I would be choosing two, whereas the scenario may be it's actually better to just crash the car and kill myself. That thought would never go through my mind, because I'm human. My rule number one is self preservation. So how do we teach the computer this sort of side of it? Is there actually the AI ethic going to be better than our own ethics? How do we start? >> Yeah, that's a great question. I think the opportunity is there as Michelle was talking earlier about maybe when you cross that chasm and you get this new singularity, maybe the AI ethics will be better than human ethics because the machine will be able to think about greater concerns perhaps other than ourselves. But I think just from my point of view, working in the space of automated vehicles, I think it is going to have to be something that the industry, and societies are different, different geographies, and different countries. We have different ways of looking at the world. Cultures value different things and so I think technologists in those spaces are going to have to get together and agree amongst the community from a social contract theory standpoint perhaps in a way that's going to be acceptable to everyone who lives in that environment. I don't think we can come up with a uniform model that would apply to all spaces, but it's got to be something though that we all, as members of a community, can accept. And so yeah, that would be the right thing to do in that situation and that's not going to be an easy task by any means, which is, I think, one of the reasons why you'll continue to see humans have an important role to play in automated vehicles so that the human could take over in exactly that kind of scenario, because the machines perhaps aren't quite smart enough to do it or maybe it's not the smarts or the processing capability. It's maybe that we haven't as technologists and ethicists gotten together long enough to figure out what are those moral and ethical frameworks that we could use to apply to those situations. Any other thoughts? >> Yeah, I wanted to jump in there real quick. Absolutely questions that need to be answered, but let's come together and make a solution that needs to have those questions answered. So let's come together first and fix the problems that need to be fixed now so that we can build out those types of scenarios. We can now put our brainpower to work to decide what to do next. There was a quote I believe by Andrew Ningh Bidou and he was saying in concerning deep questions about what's going to happen in the future with AI. Are we going to have AI overlords or anything like that? And it's kind of like worrying about overpopulation at the point of Mars. Because maybe we're going to get there someday and maybe we're going to send people there and maybe we're going to establish a human population on Mars and then maybe it will get too big and then maybe we'll have problems on Mars, but right now we haven't landed on the planet and I thought that really does a good job of putting in perspective that that overall concern about AI taking over. >> So when you think about AI being applied for good and Michelle you talked about don't do AI just for AI's sake, have a problem to solve, I'll open it up to any of the three of you, what's a problem in your life or in your work experience that you'd love somebody out here would go solve with AI? >> I have one. Sorry, I wanted to do this real quick. There's roads blocked off and it's raining and I have to walk a mile to find a taxi in the rain right now after this to go home. I would love for us to have some sort of ability to manage parking spaces and determine when and who can come in to which parts of the city and when there's a spot downtown, I want my autonomous vehicle to know which one's available and go directly to that spot and I want it to be cued in a certain manner to where I'm next in line and I know. And so I would love for someone to go solve that problem. There's been some development on the infrastructure side for that kind of solution. We have a partnership Intel does with GE and we're putting sensors that have, it's an IOT sensor basically. It's called City IQ. It has environmental monitoring, audio, visual sensors and it allows this type of use case to take place. So I would love to see iterations on that. I would love to see, sorry there's another one that I'm particular about. Growing up I lived in Southern California right against the hills, a housing development, because the hills and there was not a factory, but a bunch of oil derricks back there. I would love to have sensor that senses the particulate in the air to see if there was too many fumes coming from that oil field into my yard growing up as a little kid. I would love for us to solve problems like that, so that's the type of thing that we'll be able to solve. Those are the types of innovations that will be able to take place once we have these sensors in place, so I'm going to sit down on that one and let someone else take over. >> I'm really glad you said the second one because I was thinking, "What I'm about to say is totally going to "trivialize Joe's pain and I don't want to do that." But cancer is my answer, because there's so much data in health and all these patterns are there waiting to be recognized. There's so many things you don't know about cancer and so many indicators that we could capture if we just were able to unmask the data and take a look, but I knew a brilliant company that was using artificial intelligence specifically around image processing to look at CAT scans and figure out what the leading indicators might be in a cancerous scenario. And they pivoted to some way more trivial problem which is still a problem and not to trivialize parking an whatnot, but it's not cancer. And they pivoted away from this amazing opportunity because of the privacy and the issues with HIPPA around health data. And I understand there's a ton of concern with it getting into the wrong hands and hacking and all of this stuff. I get that, but the opportunity in my mind far outweighs the risk and the fact that they had to change their business model and change their company essentially broke my heart because they were really onto something. >> Yeah that's a shame and it's funny you mention that. Intel has an effort that we're calling the cancer cloud and what we're trying to do is provide some infrastructure to help with that problem and the way cancer treatments work today is if you go to a university hospital let's say here in Texas, how you interpret that scan and how you respond and apply treatment, that knowledge is basically just kept within that hospital and within that staff. And so on the other side of the country, somebody could go in and get a scan and maybe that scan brand new to that facility and so they don't know how to treat it, but if you had an opportunity with machine learning to be able to compare scans from people, not only just in this country, but around the world and understand globally, all of the hundreds of different treatment pads that were applied to that particular kind of cancer, think how many lives could be saved, because then you're sharing knowledge with what courses of treatment worked. But it's one of those things like you say, sometimes it's the regulatory environment or it's other factors that hold us back from applying this technology to do some really good things, so it's a great example. Okay, any other questions in the audience? >> I have one. >> Good Emily. >> So this goes off of the HIPPA question, which is, and you were talking about just dynamically displaying ads earlier. What does privacy look like in a fully autonomous world? Anybody can answer that one. Are we still private citizens? What does it look like? >> How about from a supply chain standpoint? You can learn a lot about somebody in terms of the products that they buy and I think to all of us, we sort of know maybe somebody's tracking what we're buying but it's still creepy when we think about how people could potentially use that against us. So, how do you from a supply chain standpoint approach that problem? >> Yeah and it's something that comes up in my life almost every day because one of the thing's we'd like to do is to understand consumer behavior. How often am I buying? What kinds of products am I buying? What am I returning? And so for that you need transactional data. You really get to understand the individual. That then starts to get into this area of privacy. Do you know too much about me? And so a lot of times what we do is data is clearly anonymized so all we know is customer A has this tendency, customer B has this tendency. And that then helps the retailers offer the right products to these customers, but to your point, there are those privacy concerns and I think issues around governance, issues around ethics, issues around privacy, these will continue to be ironed out. I don't think there's a solid answer for any of these just yet. >> And it's largely a reflection of society. How comfortable are we with how much privacy? Right now I believe we put the individual in control of as much information as possible that they are able to release or not. And so a lot of what you said, everyone's anonymizing everything at the moment, but that may change as society's values change slightly and we'll be able to adapt to what's necessary. >> Why don't we try to stump the panel. Anyone have any ideas on things in your life you'd like to be solved with AI for good? Any suggestions out there that we could then hear from our data scientist and technologist and folks here? Any ideas? No? Alright good. Alright, well, thank you everyone. Really appreciate your time. Thank you for joining Intel here at the AI lounge at Autonomous World. We hope you've enjoyed the panel and we wish you a great rest of your event here at South by Southwest. (audience clapping) (bright music)

>> live from the Congress Centre in London, England. It's the queue at M i t. And the digital economy The second machine age Brought to you by headlines sponsor M i t. >> I already We're back Dave along with Student of American Nelson and Macca Fear are back here after the day Each of them gave a detailed presentation today related to the book Gentlemen, welcome back to to see you >> Good to see you again I want to start with you >> on a question. That last question That and he got from a woman when you're >> starting with him on a question that was asked of him Yes. And you'LL see why when you find something you like. You dodged the question by the way. Fair for record Hanging out with you guys makes us smarter. Thank you. Hear it? So the question was >> around education She expressed real concern, particularly around education for younger people. I guess by the time they get to secondary education it's too late. You talked about in the book about the three r's we need to read. Obviously we need to write Teo be able to do arithmetic in our head. Sure. What's your take on that on that question. You >> know those basics, our table stakes. I mean, you have to be able to do that kind of stuff. But the real payoff comes from creativity doing something really new and original. The good news is that most people love being creative and original. You look at a kid playing, you know, whether it there two or three years old, that's all that you put some blocks in front of them. They start building, creating things, and our school system is, Andy was saying in his his talkers, questions was, is that many of the schools are almost explicitly designed to tamp that down to get people to conform, get them to all be consistent. Which is exactly what Henry Ford needed for his factories, you know, to work on the assembly line. But now that machines could do that repetitive, consistent kind of work, it's time to let creativity flourish again. And that's when you got to do on top of those basic skills. >> So I have one, and it's pretty clear that that that are Kramer education model. It's really hard for some kids to accept. They just want they want to run around. They want to go express themselves. They wantto poke a world. That's not what that grid full of desks is designed to do. >> We call that a d d. Now I follow. Yeah, I have one >> Montessori kid out of my foot. Really? He's by far the most creative most ano didactic. You're a Montessori Travel Marie, not the story. Have it right? Is that >> Look, I'm not educational research. I am Amon a story kid. I think she got it right. And she was able to demonstrate that she could take kids out of the slums of Bologna who were, at the time considered mentally defective. There's this notion that the reason the poor are poor because they were they were just mentally insufficient. And she could show their learning and their progress. So I completely agree with Eric. We need all of our students need to be able to Teo, accomplish the basics, to read, to write, to do basic math. What Montessori taught me is you can get there via this completely kind of hippie freeform route. And I'm really happy for that education talk. Talk about you and your students. >> Your brainstorm on things that people could do with computers. Can't. >> Yeah, a lot of money >> this and exercise that you do pretty regularly. What's that? How is >> that evolved? A little >> something. We do it more systematically, I almost always doing in at talking over where With Forum. It's a kind of dinner conversation out we can't get away from. So we're hearing a lot. And you know, there's a recurring patterns that emerged, and you heard some of them today around interpersonal skills around creativity. Still, coordination is still physical coordination. What some of these have in common is that their skills that we've evolved over literally, you know, hundreds of thousands or millions of years. And there are billions of neurons devoted to some of these skills. Coordination, vision, interpersonal skills and other skills like arithmetic is something that's really very recent, and we don't have a lot of neurons devoted to that. So it's not surprising the machines can pick up those more recent skills more than the Maurin eight ones. Now overtime, will machines be able to do more of those other skills? I suspect they probably will exactly how long it will take. That's the question for neuroscientists. The AI researchers >> made me make that country think about not just diagnosing a patient but getting them to comply with the treatment regimen. Take your medicine. Eat better. Stop smoking. We know the compliance rates for terrible for demonstrably good ideas. How do we improve them? Is in a technology solution a little bit. Is it an interpersonal solution? Absolutely. I think we need deeply empathetic, deeply capable people to help each other become healthier, become better people. Right Program might come from an algorithm, but that algorithm on the computer that spits it out is going to be lousy at getting most people to comply. Way need human beings for that. So when >> we talking technology space, we've been evangelizing that people need to get rid of what we call the undifferentiated having lifting. And I wonder if there's an opportunity in our personal life, you think about how much time we spend Well, you know, what are we doing for dinner when we're running the kids around? You know, how do I get dressed in the different things that have here their studies sometimes like waste so much brain power, trying to get rid of these things and there's opportunities. Welcome, Jetsons. Actually, no, they >> didn't have these problems that can help us with some of that. I think people should actually help us with over of it. You know, I actually I have a personal trainer and he's one of the last people that I would ever have exclude from my life because he's the guy who could actually help me lead a healthier life. And I play so much value on that. >> I like your metaphor of this is undifferentiated stuff, that really it's not the stuff that makes you great. It's just stuff you have to do. And I remember having a conversation with folks that s AP, and they said, you know, sure would like to brag about this, but we take away a lot of stuff that isn't what differentiates companies in the back office stuff. Getting your basic bookkeeping, accounting, supply chain stuff done and it's interesting. I think we could use the same thing for for personal lives. Let's get rid of that sort of underbrush of necessity stuff so we can focus on the things that are uniquely good at >> alright so way have to run out when I need garbage bags with toilet paper. Honestly, a drone should show up and drop that on my friends. >> So I wonder when I look at the self driving car that you've talked about, will we reach a point that not only do we trust computers in the car, it's cars to drive herself? But we've reached a point where we're just got nothing. Trust humans anymore because self driving cars there just so much safer and better than what we've got is that coming >> in the next twenty years? I personally think so, and the first time is deeply weird and unsettling. I think both of us were a little bit terrified the first time we drove in the Google Autonomous Car and the Google or driving it hit the button and took his hands off the controls. That was a weird moment. I liken it to when I was learning to scuba dive. Very first breath you take underwater is deeply unsettling because you're not supposed to be doing this. After a few breaths, it becomes background. >> But you know, I was I was driving to the airport to come here, and I look in the lanes left to me. There's a woman, you know, texting, and I'd be much you're terrifying if she wasn't driving. If the computer is doing because then we could be more, that's the right way to think about it. I think the time will come and it may not be that far away. We're the norm's shift exactly the other way around and be considered risky to have a human at the wheel and the safety. That thing that the insurance company will want is to have a machine there. You know, I think this is a temporary phase with Newt technology. We become frightened of them. When microwave ovens first came out, they were weird and wonderful. Not most of us think of them is really kind of boring and routine. Same thing is gonna happen with self driving to accidents. Well, that's the story is, that is, But none of them were. Of course, according to the story >> driving, what's clear is that they're safer than the human driver. As of today, they are only going to get safer. We're not evolving that quick, >> but you got the question. Is that self driving, car driven story? Dr. We laughed because we're live in Boston. But your answer was, Will drive started driving, driving, >> you know, eventually, you know, I think it's fair to say that there's a big difference. You know, the first nineteen, ninety five, ninety nine percent of driving is something that's a lot easier. That last one percent or one hundredth of one percent becomes much, much harder. And right now we've had There's a card just last week that drove across the United States, but there were half a dozen times when he had to have a human interviews and particularly unusual situations. And I think because of our norms and expectations, that won't be enough for a self driving car to be safer than humans will need it to be te next paper or something like maybe >> like the just example may be the ultimate combination is a combination of human and self driving car, >> Maybe situation after situation. I think that's going to be the case and I'LL go back to medical diagnosis. I would at least for the short to medium term, I would like to have a pair of human eyes over the treatment plan that the that being completely digital diagnostician spits out. Maybe over time it will be clear that there are no flaws in that. We could go totally digital, but we can combine the two. >> I think in most cases what anything is right, what you brought up. But you know the case of self driving cars in particular, and other situations where humans have to take over for a machine that's failing for someway like aircraft. When the autopilot is doing things right, it turns out that that transition could be very, very rocky and expecting a human to be on call to be able to quickly grasp what's going on in the middle of a crisis of a freak out that's not reasonable isn't necessarily the best time to be swishing over. So there's a there's a fuel. Human factors issued their of how you design it, not just to the human could take over, but you could make a kind of a seamless transition. And that's not easy. >> Okay, so maybe self driving cars, that doesn't happen. But back to the medical example. Maybe Watson will replace Dr Welby, but have not Dr Oz >> interaction or any nurse or somebody who actually gets me to comply again. But also, I do think that Dr Watson can and should take over for people in the developing world who only have access instead of First World medical care. They've got a smartphone. OK, we're going to be able to deliver absolute top shelf world class medical diagnostics to those people fairly quickly. Of course, we should >> do that and then combine it with a coach who gets people to take the prescription when they're supposed to do it, change their eating habits or communities or whatever else you hear your peers are all losing weight. >> Why aren't you? >> I wantto askyou something coming on. Time here has been gracious with your time and your talk. We're very out spoken about. A couple of things I would summarize. It is you lot must Bill Gates and Stephen Hawking. You're paranoid tens. There's no privacy in the Internet, so get over. >> I didn't say there's no privacy. I know working. I think it's important to be clear on this. I think privacy is really important. I do think it's right that we have, and we should have. What I don't want to do is have a bureaucrat defined my privacy rights for me and start telling >> companies what they can and can't do is a result. What >> I'd much prefer instead is to say, Look, if there are things that we know >> Cos we're doing that we do not approve >> of let's deal with that situation as opposed to trying to put the guard rails in place and fence off the different kinds of innovative, strict growth, right? >> I mean, there's two kinds of mistakes you could make. One is, you can let companies do things and you should have regulated them. The other is. You could regulate them preemptively when you really should have let them do things and both kinds of errors or possible. Our sense of looking at what's happening in Jinan is that we've thrived where we allow more permission, listen innovation. We allowed companies to do things and then go back and fix things rather than when we try and locked down the past in the existing processes, so are leaning. In most cases, not every case is to be a little more free, a little more open recognized that there will be mistakes. It's not gonna be that we're perfectly guaranteed is that there is a risk when you walk across the street but go back and fix things at that point rather than preemptively define exactly how things are gonna play. Let >> me give you an example. If Google were to say to me, Hey, Andy, unless you pay us x dollars per month, we're gonna show the world your last fifty Google searches. I would completely pay for that kind of blackmail, right? Certain your search history is incredibly personal reveals a lot about you. Google is not going to do that. It would just it would crater their own business. So trying to trying to fence that kind of stuff often advance makes a lot of sense to me. Then then then relying on this. This sounds a little bit weird, but a combination of for profit companies and people with three choice that that's a really good guarantor of our freedoms and our rights. So you >> guys have a pretty good thing going. It doesn't look like strangle each other anytime soon. But >> how do you How do you decide who >> does one treat by how you operate with reading the book? It's like, Okay, like I think that was Andy because he's talking about Erica. I think that was Erica's. He's talking, >> but I couldn't tell you. I think it's hard for you to reverse engineer because it gets so co mingled over time. And, you know, I gave the example the end of the talk about humans and machines working together synergistically. I think the same thing is true with Indian me out. You may disagree, but I find that we are smarter when we work together so much smarter. Then when we work individually, we go and bring some things on the blackboard. And I had these aha moments that I don't think I would've had just sitting by myself and do I should be that ah ha moment to Andy. To me, it's actually to this Borg of us working together >> and fundamentally, these air bumper sticker things to say. If after working with someone, you become convinced that they respect you and that you could trust them and like Erik says that you're better off together, that you would be individually, it's a complete no brainer to >> keep doing the work together. Well, we're really humbled to be here. You guys are great contact. Everything is free and available. We really believe in that sort of economics. And so thank you very much for having us here. >> Well, it's just a real pleasure. >> All right, Right there, buddy. We'LL be back to wrap up right after this is Q relied from London. My tea.