(upbeat music) >> Announcer: From the cube Studios in Palo Alto and Boston, it's the cube! Covering IBM Think. Brought to you by IBM. >> Hi everybody, this is Dave Vellante of theCUBE, and you're watching our coverage of the IBM digital event experience. A multi-day program, tons of content, and it's our pleasure to be able to bring in experts, practitioners, customers, and partners. Sriram Raghavan is here. He's the Vice President of IBM Research in AI. Sriram, thanks so much for coming on thecUBE. >> Thank you, pleasure to be here. >> I love this title, I love the role. It's great work if you're qualified for it.(laughs) So, tell us a little bit about your role and your background. You came out of Stanford, you had the pleasure, I'm sure, of hanging out in South San Jose at the Almaden labs. Beautiful place to create. But give us a little background. >> Absolutely, yeah. So, let me start, maybe go backwards in time. What do I do now? My role's responsible for AI strategy, planning, and execution in IBM Research across our global footprint, all our labs worldwide and their working area. I also work closely with the commercial parts. The parts of IBM, our Software and Services business that take the innovation, AI innovation, from IBM Research to market. That's the second part of what I do. And where did I begin life in IBM? As you said, I began life at our Almaden Research Center up in San Jose, up in the hills. Beautiful, I had in a view. I still think it's the best view I had. I spent many years there doing work at the intersection of AI and large-scale data management, NLP. Went back to India, I was running the India lab there for a few years, and now I'm back here in New York running AI strategy. >> That's awesome. Let's talk a little bit about AI, the landscape of AI. IBM has always made it clear that you're not doing consumer AI. You're really tying to help businesses. But how do you look at the landscape? >> So, it's a great question. It's one of those things that, you know, we constantly measure ourselves and our partners tell us. I think we, you've probably heard us talk about the cloud journey . But look barely 20% of the workloads are in the cloud, 80% still waiting. AI, at that number is even less. But, of course, it varies. Depending on who you ask, you would say AI adoption is anywhere from 4% to 30% depending on who you ask in this case. But I think it's more important to look at where is this, directionally? And it's very, very clear. Adoption is rising. The value is more, it's getting better appreciated. But I think more important, I think is, there is broader recognition, awareness and investment, knowing that to get value out of AI, you start with where AI begins, which is data. So, the story around having a solid enterprise information architecture as the base on which to drive AI, is starting to happen. So, as the investments in data platform, becoming making your data ready for AI, starts to come through. We're definitely seeing that adoption. And I think, you know, the second imperative that businesses look for obviously is the skills. The tools and the skills to scale AI. It can't take me months and months and hours to go build an AI model, I got to accelerate it, and then comes operationalizing. But this is happening, and the upward trajectory is very, very clear. >> We've been talking a lot on theCUBE over the last couple of years, it's not the innovation engine of our industry is no longer Moore's Law, it's a combination of data. You just talked about data. Applying machine technology to that data, being able to scale it, across clouds, on-prem, wherever the data lives. So. >> Right. >> Having said that, you know, you've had a journey. You know, you started out kind of playing "Jeopardy!", if you will. It was a very narrow use case, and you're expanding that use case. I wonder if you could talk about that journey, specifically in the context of your vision. >> Yeah. So, let me step back and say for IBM Research AI, when I think about how we, what's our strategy and vision, we think of it as in two parts. One part is the evolution of the science and techniques behind AI. And you said it, right? From narrow, bespoke AI that all it can do is this one thing that it's really trained for, it takes a large amount of data, a lot of computing power. Two, how do you have the techniques and the innovation for AI to learn from one use case to the other? Be less data hungry, less resource hungry. Be more trustworthy and explainable. So, we call that the journey from narrow to broad AI. And one part of our strategy, as scientists and technologists, is the innovation to make that happen. So that's sort of one part. But, as you said, as people involved in making AI work in the enterprise, and IBM Research AI vision would be incomplete without the second part, which is, what are the challenges in scaling and operationalizing AI? It isn't sufficient that I can tell you AI can do this, how do I make AI do this so that you get the right ROI, the investment relative to the return makes sense and you can scale and operationalize. So, we took both of these imperatives. The AI narrow-to-broad journey, and the need to scale and operationalize. And what of the things that are making it hard? The things that make scaling and operationalizing harder: data challenges, we talked about that, skills challenges, and the fact that in enterprises, you have to govern and manage AI. And we took that together and we think of our AI agenda in three pieces: Advancing, trusting, and scaling AI. Advancing is the piece of pushing the boundary, making AI narrow to broad. Trusting is building AI which is trustworthy, is explainable, you can control and understand its behavior, make sense of it and all of the technology that goes with it. And scaling AI is when we address the problem of, how do I, you know, reduce the time and cost for data prep? How do I reduce the time for model tweaking and engineering? How do I make sure that a model that you build today, when something changes in the data, I can quickly allow for you to close the loop and improve the model? All of the things, think of day-two operations of AI. All of that is part of our scaling AI strategy. So advancing, trusting, scaling is sort of the three big mantras around which the way we think about our AI. >> Yeah, so I've been doing a little work in this around this notion of DataOps. Essentially, you know, DevOps applied to the data and the data pipeline, and I had a great conversation recently with Inderpal Bhandari, IBM's Global Chief Data Officer, and he explained to me how, first of all, customers will tell you, it's very hard to operationalize AIs. He and his team took that challenge on themselves and have had some great success. And, you know, we all know the problem. It's that, you know AI has to wait for the data. It has to wait for the data to be cleansed and wrangled. Can AI actually help with that part of the problem, compressing that? >> 100%. In fact, the way we think of the automation and scaling story is what we call the "AI For AI" story. So, AI in service of helping you build the AI that helps you make this with speed, right? So, and I think of it really in three parts. It's AI for data automation, our DataOps. AI used in better discovery, better cleansing, better configuration, faster linking, quality assessment, all of that. Using AI to do all of those data problems that you had to do. And I called it AI for data automation. The second part is using AI to automatically figure out the best model. And that's AI for data science automation, which is, feature engineering, hyperparameter optimization, having them all do work, why should a data scientist take weeks and months experimenting? If the AI can accelerate that from weeks to a matter of hours? That's data science automation. And then comes the important part, also, which is operations automation. Okay, I've put a data model into an application. How do I monitor its behavior? If the data that it's seeing is different from the data it was trained on, how do I quickly detect it? And a lot of the work from Research that was part of that Watson OpenScale offering is really addressing the operational side. So AI for data, AI for data science automation, and AI to help automate production of AI, is the way we break that problem up. >> So, I always like to ask folks that are deep into R&D, how they are ultimately are translating into commercial products and offerings? Because ultimately, you got to make money to fund more R&D. So, can you talk a little bit about how you do that, what your focus is there? >> Yeah, so that's a great question, and I'm going to use a few examples as well. But let me say at the outset, this is a very, very closed partnership. So when we, the Research part of AI and our portfolio, it's a closed partnership where we're constantly both drawing problem as well as building technology that goes into the offering. So, a lot of our work, much of our work in AI automation that we were talking about, is part of our Watson Studio, Watson Machine Learning, Watson OpenScale. In fact, OpenScale came out of Research working Trusted AI, and is now a centerpiece of our Watson project. Let me give a very different example. We have a very, very strong portfolio and focus in NLP, Natural Language Processing. And this directly goes into capabilities out of Watson Assistant, which is our system for conversational support and customer support, and Watson Discovery, which is about making enterprise understand unstructurally. And a great example of that is the Working Project Debater that you might have heard, which is a grand challenge in Research about building a machine that can do debate. Now, look, we weren't looking to go sell you a debating machine. But what did we build as part of doing that, is advances in NLP that are all making their way into assistant and discovery. And we actually just talked about earlier this year, announced a set of capabilities around better clustering, advanced summarization, deeper sentiment analysis. These made their way into Assistant and Discovery but are born out of research innovation and solving a grand problem like building a debating machine. That's just an example of how that journey from research to product happens. >> Yeah, the Debater documentary, I've seen some of that. It's actually quite astounding. I don't know what you're doing there. It sounds like you're taking natural language and turning it into complex queries with data science and AI, but it's quite amazing. >> Yes, and I would encourage you, you will see that documentary, by the way, on Channel 7, in the Think Event. And I would encourage you, actually the documentary around how Debater happened, sort of featuring back of the you know, backdoor interviews with the scientist who created it was actually featured last minute at Copenhagen International Documentary Festival. I'll invite viewers to go to Channel 7 and Data and AI Tech On-Demand to go take a look at that documentary. >> Yeah, you should take a look at it. It's actually quite astounding and amazing. Sriram, what are you working on these days? What kind of exciting projects or what's your focus area today? >> Look, I think there are three imperatives that we're really focused on, and one is very, you know, just really the project you're talking about, NLP. NLP in the enterprise, look, text is a language of business, right? Text is the way business is communicated. Within each other, with their partners, with the entire world. So, helping machines understand language, but in an enterprise context, recognizing that data and the enterprises live in complex documents, unstructured documents, in e-mail, they live in conversations with the customers. So, really pushing the boundary on how all our customers and clients can make sense of this vast volume of unstructured data by pushing the advances of NLP, that's one focus area. Second focus area, we talked about trust and how important that is. And we've done amazing work in monitoring and explainability. And we're really focused now on this emerging area of causality. Using causality to explain, right? The model makes this because the model believes this is what it wants, it's a beautiful way. And the third big focus continues to be on automation. So, NLP, trust, automation. Those are, like, three big focus areas for us. >> sriram, how far do you think we can take AI? I know it's a topic of conversation, but from your perspective, deep into the research, how far can it go? And maybe how far should it go? >> Look, I think we are, let me answer it this way. I think the arc of the possible is enormous. But I think we are at this inflection point in which I think the next wave of AI, the AI that's going to help us this narrow-to-broad journey we talked about, look, the narrow-to-broad journey's not like a one-week, one-year. We're talking about a decade of innovation. But I think we are at a point where we're going to see a wave of AI that we like to call "neuro-symbolic AI," which is AI that brings together two sort of fundamentally different approaches to building intelligence systems. One approach of building intelligence system is what we call "knowledge driven." Understand data, understand concept, logically, reasonable. We human beings do that. That was really the way AI was born. The more recent last couple of decades of AI was data driven, Machine learning. Give me vast volumes of data, I'll use neural techniques, deep learning, to to get value. We're at a point where we're going to bring both of them together. Cause you can't build trustworthy, explainable systems using only one, you can't get away from not using all of the data that you have to make them. So, neuro-symbolic AI is, I think, going to be the linchpin of how we advance AI and make it more powerful and trustworthy. >> So, are you, like, living your childhood dream here or what? >> Look, for me I'm fascinated. I've always been fascinated. And any time you can't find a technology person who hasn't dreamt of building an intelligent machine. To have a job where I can work across our worldwide set of 3,000 plus researchers and think and brainstorm on strategy with AI. And then, most importantly, not to forget, right? That you talked about being able to move it into our portfolios so it actually makes a difference for our clients. I think it's a dream job and a whole lot of fun. >> Well, Sriram, it was great having you on theCUBE. A lot of fun, interviewing folks like you. I feel a little bit smarter just talking to you. So thanks so much for coming on. >> Fantastic. It's been a pleasure to be here. >> And thank you for watching, everybody. You're watching theCUBE's coverage of IBM Think 2020. This is Dave Vellante. We'll be right back right after this short break. (upbeat music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome everyone to this special CUBE Conversation Around the CUBE segment, Unpacking AI, number two, sponsored by Juniper Networks. We've got a great lineup here to go around the CUBE and unpack AI. We have Ken Jennings, all-time Jeopardy champion with us. Celebrity, great story there, we'll dig into that. John Hinson, director of AI at Evotek and Charna Parkey, who's the applied scientist at Textio. Thanks for joining us here for Around the CUBE Unpacking AI, appreciate it. First question I want to get to, Ken, you're notable for being beaten by a machine on Jeopardy. Everyone knows that story, but it really brings out the question of AI and the role AI is playing in society around obsolescence. We've been hearing gloom and doom around AI replacing people's jobs, and it's not really that way. What's your take on AI and replacing people's jobs? >> You know, I'm not an economist, so I can't speak to how easy it's going to be to retrain and re-skill tens of millions of people once these clerical and food prep and driving and whatever jobs go away, but I can definitely speak to the personal feeling of being in that situation, kind of watching the machine take your job on the assembly line and realizing that the thing you thought made you special no longer exists. If IBM throws enough money at it, your skill essentially is now obsolete. And it was kind of a disconcerting feeling. I think that what people need is to feel like they matter, and that went away for me very quickly when I realized that a black rectangle can now beat me at a game show. >> Okay John, what's your take on AI replacing jobs? What's your view on this? >> I think, look, we're all going to have to adapt. There's a lot of changes coming. There's changes coming socially, economically, politically. I think it's a disservice to us all to get to too indulgent around the idea that these things are going to change. We have to absorb these things, we have to be really smart about how we approach them. We have to be very open-minded about how these things are going to actually change us all. But ultimately, I think it's going to be positive at the end of the day. It's definitely going to be a little rough for a couple of years as we make all these adjustments, but I think what AI brings to the table is heads above kind of where we are today. >> Charna, your take around this, because the role of humans versus machines are pretty significant, they help each other. But is AI going to dominate over humans? >> Yeah, absolutely. I think there's a thing that we see over and over again in every bubble and collapse where, you know, in the automotive industry we certainly saw a bunch of jobs were lost, but a bunch of jobs were gained. And so we're just now actually getting into the phase where people are realizing that AI isn't just replacement, it has to be augmentation, right? We can't simply use images to replace recognition of people, we can't just use black box to give our FICO credit scores, it has to be inspectable. So there's a new field coming up now called explainable AI that actually is where we're moving towards and it's actually going to help society and create jobs. >> All right so let's stay on that next point for the next round, explainable AI. This points to a golden age. There's a debate around are we in a bubble or a golden age. A lot of people are negative right now on tech. You can see all the tech backlash. Amazon, the big tech companies like Apple and Facebook, there's a huge backlash around this so-called tech for society. Is this an indicator of a golden age coming? >> I think so, absolutely. We can take two examples of this. One would be where, you remember when Amazon built a hiring algorithm based upon their own resume data and they found that it was discriminating against women because they had only had men apply for it. Now with Textio we're building augmented writing across the audience and not from a single company and so companies like Johnson and Johnson are increasing the pipeline by more than nine percent which converts to 90,000 more women applying for their jobs. And so part of the difference there is one is explainable, one isn't, and one is using the right data set representing the audience that is consuming it and not a single company's hiring. So I think we're absolutely headed into more of a golden age, and I think these are some of the signs that people are starting to use it in the right way. >> John, what's your take? Obviously golden age doesn't look that to us right now. You see Facebook approving lies as ads, Twitter banning political ads. AI was supposed to solve all these problems. Is there light at the end of this dark tunnel we're on? >> Yeah, golden age for sure. I'm definitely a big believer in that. I think there's a new era amongst us on how we handle data in general. I think the most important thing we have here though is education around what this stuff is, how it works, how it's affecting our lives individually and at the corporate level. This is a new era of informing and augmenting literally everything we do. I see nothing but positives coming out of this. We have to be obviously very careful with our approaching all the biases that already exist today that are only going to be magnified with these types of algorithms at mass scale. But ultimately if we can get over that hurdle, which I believe collectively we all need to do together, I think we'd live in much better, less wasteful world just by approaching the data that's already at hand. >> Ken, what's your take on this? It's like a daily double question. Is it going to be a golden age? >> Laughs >> It's going to come sooner or later. We have to have catastrophe before, we have to have reality hit us in the face before we realize that tech is good, and shaping it? It's pretty ugly right now in some of the situations out there, especially in the political scene with the election in the US. You're seeing some negative things happening. What's your take on this? >> I'm much more skeptical than John and Charna. I feel like that kind of just blinkered, it's going to be great, is something you have to actually be in the tech industry and hearing all day to actually believe. I remember seeing kind of lay-person's exposure to Watson when Watson was on Jeopardy and hearing the questions reporters would ask and seeing the memes that would appear, and everyone's immediate reaction just to something as innocuous as a AI algorithm playing on a game show was to ask, is this Skynet from Terminator 2? Is this the computer from The Matrix? Is this HAL pushing us out of the airlock? Everybody immediately first goes to the tech is going to kill us. That's like everybody's first reaction, and it's weird. I don't know, you might say it's just because Hollywood has trained us to expect that plot development, but I almost think it's the other way around. Like that's a story we tell because we're deeply worried about our own meaning and obsolescence when we see how little these skills might be valued in 10, 20, 30 years. >> I can't tell you how much, by the way, Star Trek, Star Wars and Terminators probably affected the nomenclature of the technology. Everyone references Skynet. Oh my God, we're going to be taken over and killed by aliens and machines. This is a real fear. I thinks it's an initial reaction. You felt that Ken, so I've got to ask you, where do you think the crossover point is for people to internalize the benefits of say, AI for instance? Because people will say hey, look back at life before the iPhone, look at life before these tools were out there. Some will say society's gotten better, but yet there's this surveillance culture, things... And on and on. So what do you guys think the crossover point is for the reaction to change from oh my God, it's Skynet, gloom and doom to this actually could be good? >> It's incredibly tricky because as we've seen, the perception of AI both in and out of the industry changes as AI advances. As soon as machine learning can actually do a task, there's a tendency to say there's this no true Scotsman problem where we say well, that clearly can't be AI because I see how the trick worked. And yeah, humans lose at chess now. So when these small advances happen, the reaction is often oh, that's not really AI. And by the same token, it's not a game-changer when your email client can start to auto-complete your emails. That's a minor convenience to you. But you don't think oh, maybe Skynet is good. I really do think it's going to have to be, maybe the inflection point is when it starts to become so disruptive that actually public policy has to change. So we get serious about >> And public policy has started changing. >> whatever their reactions are. >> Charna, your thoughts. >> The public policy has started changing though. We just saw, I think it was in September, where California banned the use of AI in the body cameras, both real-time and after the fact. So I think that's part of the pivot point that we're actually seeing is that public policy is changing.` The state of Washington currently has a task force for AI who's making a set of recommendations for policy starting in December. But I think part of what we're missing is that we don't have enough digital natives in office to even attempt to, to your point Ken, predict what we're even going to be able to do with it, right? There is this fear because of misunderstanding, but we also don't have a respect of our political climate right now by a lot of our digital natives, and they need to be there to be making this policy. >> John, weigh in on this because you're director of AI, you're seeing positive, you have to deal with the uncertainty as well, the growth of machine learning. And just this week Google announced more TensorFlow for everybody. You're seeing Open Source. So there's a tech push, almost a democratization, going on with AI. So I think this crossover point might be sooner in front of us than people think. What's your thoughts? >> Yeah it's here right now. All these things can be essentially put into an environment. You can see these into products, or making business decisions or political decisions. These are all available right now. They're available today and its within 10 to 15 lines of code. It's all about the data sets, so you have to be really good stewards of the data that you're using to train your models. But I think the most important thing, back to the Skynet and all this science-fiction side, we have to collectively start telling the right stories. We need better stories than just this robots are going to take us over and destroy all of our jobs. I think more interesting stories really revolve around, what about public defenders who can have this informant augmentation algorithm that's going to help them get their job done? What about tailor-made medicine that's going to tell me exactly what the conditions are based off of a particular treatment plan instead of guessing? What about tailored education that's going to look at all of my strengths and weaknesses and present a plan for me? These are things that AI can do. Charna's exactly right, where if we don't get this into the right political atmosphere that's helping balance the capitalist side with the social side, we're going to be in trouble. So that's got to be embedded in every layer of enterprise as well as society in general. It's here, it's now, and it's real. >> Ken, before we move on to the ethics question, I want to get your thoughts on this because we have an Alexa at home. We had an Alexa at home; my wife made me get rid of it. We had an Apple device, what they're called... the Home pods, that's gone. I bought a Portal from Facebook because I always buy the earliest stuff, that's gone. We don't want listening devices in our house because in order to get that AI, you have to give up listening, and this has been an issue. What do you have to give to get? This has been a big question. What's your thoughts on all this? >> I was at an Amazon event where they were trumpeting how no technology had ever caught on faster than these personal digital assistants, and yet every time I'm in a use case, a household that's trying to use them, something goes terribly wrong. My friend had to rename his because the neighbor kids kept telling Alexa to do awful things. He renamed it computer, and now every time we use the word computer, the wall tells us something we don't want to know. >> (laughs) >> This is just anecdata, but maybe it speaks to something deeper, the fact that we don't necessarily like the feeling of being surveilled. IBM was always trying to push Watson as the star Trek computer that helpfully tells you exactly what you need to know in the right moment, but that's got downsides too. I feel like we're going to, if nothing else, we may start to value individual learning and knowledge less when we feel like a voice from the ceiling can deliver unto us the fact that we need. I think decision-making might suffer in that kind of a world. >> All right, this brings up ethics because I bring up the Amazon and the voice stuff because this is the new interface people want to have with machines. I didn't mention phones, Androids and Apple, they need to listen in order to make decisions. This brings up the ethics question around who sets the laws, what society should do about this, because we want the benefits of AI. John, you point out some of them. You got to give to get. Where are we on ethics? What's the opinion, what's the current view on this? John, we'll start with you on your ethics view on what needs to change now to move the ball faster. >> Data is gold. Data is gold at an exponential rate when you're talking about AI. There should be no situation where these companies get to collect data at no cost or no benefit to the end consumer. So ultimately we should have the option to opt out of any of these products and any of this type of surveillance wherever we can. Public safety is a little bit different situation, but on the commercial side, there is a lot of more expensive and even more difficult ways to train these models with a data set that isn't just basically grabbing everything our of your personal lives. I think that should be an option for consumers and that's one of those ethical check-marks. Again, ethics in general, the way that data's trained, the way that data's handled, the way models actually work, it has to be a primary reason for and approach of how you actually go about developing and delivering AI. That said, we cannot get over-indulgent in the fact that we can't do it because we're so fearful of the ethical outcomes. We have to find some middle ground and we have to find it quickly and collectively. >> Charna, what's your take on this? Ethics is super important to set the agenda for society to take advantage of all this. >> Yeah. I think we've got three ethical components here. We certainly have, as John mentioned, the data sets. However, it's also what behavior we're trying to change. So I believe the industry could benefit from a lot more behavioral science, so that we can understand whether or not the algorithms that we're building are changing behaviors that we actually want to change, right? And if we aren't, that's unethical. There is an entire field of ethics that needs to start getting put into our companies. We need an ethics board internally. A few companies are doing this already actually. I know a lot of the military companies do. I used to be in the defense industry, and so they've got a board of ethics before you can do things. The challenge is also though that as we're democratizing the algorithms themselves, people don't understand that you can't just get a set of data that represents the population. So this is true of image processing, where if we only used 100 images of a black woman, and we used 1,000 images of a white man because that was the distribution in our population, and then the algorithm could not detect the difference between skin tones for people of color, then we end up with situations where we end up in a police state where you put in an image of one black woman and it looks like ten of them and you can't distinguish between them. And yet, the confidence rate for the humans are actually higher, because they now have a machine backing their decision. And so they stop questioning, to your point, Ken, about what is the decision I'm making, they're like I'm so confident, this data told me so. And so there's a little bit of you need some expert in the loop and you also can't just have experts, because then you end up with Cambridge Analytica and all of the political things that happened there, not just in the US, but across 200 different elections and 30 different countries. And we are upset because it happened in the US, but this has been happening for years. So its just this ethical challenge of behavior change. It's not even AI and we do it all the time. Its why the cigarette industry is regulated (laughs). >> So Ken, what's your take on this? Obviously because society needs to have ethics. Who runs that? Companies? The law-makers? Someone's got to be responsible. >> I'm honestly a little pessimistic the general public will even demand this the way we're maybe hoping that they will. When I think about an example like Facebook, people just being able to, being willing to give away insane amounts of data through social media companies for the smallest of benefits: keeping in touch with people from high school they don't like. I mean, it really shows how little we value not being a product in this kind of situation. But I would like to see this kind of ethical decisions being made at the company-level. I feel like Google kind of surreptitiously moved away from it's little don't be evil mantra with the subtext that eh, maybe we'll be a little evil now. It just reminds me of Manhattan Project era thinking, where you could've gone to any of these nuclear scientists and said you're working on a real interesting puzzle here, it might advance the field, but like 200,000 civilians might die this summer. And I feel like they would've just looked at you and thought that's not really my bailiwick. I'm just trying to solve the fission problem. I would like to see these 10 companies actually having that kind of thinking internally. Not being so busy thinking if they can do something that they don't wonder if they should. >> That's a great point. This brings up the point of who is responsible. Almost as if who is less evil than the other person? Google, they don't do evil, but they're less evil than Amazon and Facebook and others. Who is responsible? The companies or the law-makers? Because if you look up some of the hearings in Washington, D.C., some of the law-makers we see up there, they don't know how the internet works, and it's pretty obvious that this is a problem. >> Yeah, well that's why Jack Dorsey of Twitter posted yesterday that he banned not just political ads, but also issue ads. This isn't something that they're making him do, but he understands that when you're using AI to target people, that it's not okay. At some point, while Mark is sitting on (laughs) this committee and giving his testimony, he's essentially asking to be regulated because he can't regulate himself. He's like well, everyone's doing it, so I'm going to do it too. That's not an okay excuse. We see this in the labor market though actually, where there's existing laws that prevent discrimination. It's actually the company's responsibility to make sure that the products that they purchase from any vendor isn't introducing discrimination into that process. So its not even the vendor that's held responsible, it's the company and their use of it. We saw in the NYPD actually that one of those image recognition systems came up and someone said well, he looked like, I forget the name of what the actor was, but some actor's name is what the perpetrator looked like and so they used an image of the actor to try and find the person who actually assaulted someone else. And that's, it's also the user problem that I'm super concerned about. >> So John, what's your take on this? Because these are companies are in business to make money, for profit, they're not the government. And who's the role, what should the government do? AI has to move forward. >> Yeah, we're all responsible. The companies are responsible. The companies that we work with, I have yet to interact with customers, or with our customers here, that have some insidious goal, that they're trying to outsmart their customers. They're not. Everyone's looking to do the best and deliver the most relevant products in the marketplace. The government, they absolutely... The political structure we have, it has to be really intelligent and it's got to get up-skilled in this space and it needs to do it quickly, both at the economy level, as well as for our defense. But the individuals, all of us as individuals, we are already subjected to this type of artificial intelligence in our everyday lives. Look at streaming, streaming media. Right now every single one of us goes out through a streaming source, and we're getting recommendations on what we should watch next. And we're already adapting to these things, I am. I'm like stop showing me all the stuff you know I want to watch, that's not interesting to me. I want to find something I don't know I want to watch, right? So we all have to get educated, we're all responsible for these things. And again, I see a much more positive side of this. I'm not trying to get into the fear-mongering side of all the things that could go wrong, I want to focus on the good stories, the positive stories. If I'm in a courtroom and I lose a court case because I couldn't afford the best attorney and I have the bias of a judge, I would certainly like artificial intelligence to make a determination that allows me to drive an appeal, as one example. Things like that are really creative in the world that we need to do. Tampering down this wild speculation we have on the markets. I mean, we are all victims of really bad data decisions right now, almost the worst data decisions. For me, I see this as a way to actually improve all those things. Fraud fees will be reduced. That helps everybody, right? Less speculation and these wild swings, these are all helpful things. >> Well Ken, John and Charna, thank- (audio feedback) >> Go ahead, finish. Get that word in. >> Sorry. I think that point you were making though John, is we are still a capitalist society, but we're no longer a shareholder capitalist society, we are a stakeholder capitalist society and the stakeholder is the society itself. It is us, it what we want to see. And so yes, I still want money. Obviously there are things that I want to buy, but I also care about well-being. I think it's that little shift that we're seeing that is actually you and I holding our own teams accountable for what they do. >> Yeah, culture first is a whole new shift going on in these companies that's a for-profit, mission-based. Ken, John, Charna, thanks for coming on Around the CUBE, Unpacking AI. Let's go around the CUBE Ken, John and Charna in that order, and just real quickly, unpacking AI, what's your final word? >> (laughs) I really... I'm interested in John's take that there's a democratization coming provided these tools will be available to everyone. I would certainly love to believe that. It seems like in the past, we've seen no, that access to these kind of powerful, paradigm-changing tools tend to be concentrated among a very small group of people and the benefits accrue to a very small group of people. But I hope that doesn't happen here. You know, I'm optimistic as well. I like the utopian side where we all have this amazing access to information and so many new problems can get solved with amazing amounts of data that we never could've touched before. Though you know, I think about that. I try to let that help me sleep at night, and not the fact that, you know... every public figure I see on TV is kind of out of touch about technology and only one candidate suggests the universal basic income, and it's kind of a crackpot idea. Those are the kind of things that keep me up at night. >> All right, John, final word. >> I think it's beautiful, AI's beautiful. We're on the cusp of a whole new world, it's nothing but positivity I see. We have to be careful. We're all nervous about it. None of us know how to approach these things, but as human beings, we've been here before. We're here all the time. And I believe that we can all collectively get a better lives for ourselves, for the environment, for everything that's out there. It's here, it's now, it's definitely real. I encourage everyone to hurry up on their own education. Every company, every layer of government to start really embracing these things and start paying attention. It's catching us all a little bit by surprise, but once you see it in production, you see it real, you'll be impressed. >> Okay, Charna, final word. >> I think one thing I want to leave people with is what we incentivize is what we end up optimizing for. This is the same for human behavior. You're training a new employee, you put incentives on the way that they sell, and that's, they game the system. AI's specifically find the optimum route, that is their job. So if we don't understand more complex cost functions, more complex representative ways of training, we're going to end up in a space, before we know it, that we can't get out of. And especially if we're using uninspectable AI. We really need to move towards augmentation. There are some companies that are implementing this now that you may not even know. Zillow, for example, is using AI to give you a cost for your home just by the photos and the words that you describe it, but they're also purchasing houses without a human in the loop in certain markets, based upon an inspection later by a human. And so there are these big bets that we're making within these massive corporations, but if you're going to do it as an individual, take a Coursera class on AI and take a Coursera class on ethics so that you can understand what the pitfalls are going to be, because that cost function is incredibly important. >> Okay, that's a wrap. Looks like we have a winner here. Charna, you got 18, John 16. Ken came in with 12, beaten again! (both laugh) Okay, Ken, seriously, great to have you guys on, a pleasure to meet everyone. Thanks for sharing on Around the CUBE Unpacking AI, panel number two. Thank you. >> Thanks a lot. >> Thank you. >> Thanks. I've been defeated by artificial intelligence again! (all laugh) (upbeat music)

(upbeat music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Hello everyone, welcome to theCUBE studio here in Palo Alto. I'm John Furrier your host of theCUBE. We're here introducing a new format for CUBE panel discussions, it's called Around theCUBE and we have a special segment here called Get Smart: Unpacking AI with some great with some great guests in the industry. Gene Santos, Professor of Engineering in College of Engineering Dartmouth College. Bob Friday, Vice President CTO at Mist at Juniper Company. And Ed Henry, Senior Scientist and Distinguished Member of the Technical Staff for Machine Learning at Dell EMC. Guys this is a format, we're going to keep score and we're going to throw out some interesting conversations around Unpacking AI. Thanks for joining us here, appreciate your time. >> Yeah, glad to be here. >> Okay, first question, as we all know AI is on the rise, we're seeing AI everywhere. You can't go to a show or see marketing literature from any company, whether it's consumer or tech company around, they all have AI, AI something. So AI is on the rise. The question is, is it real AI, is AI relevant from a reality standpoint, what really is going on with AI, Gene, is AI real? >> I think a good chunk of AI is real there. It depends on what you apply it to. If it's making some sort of decisions for you, that is AI that's blowing into play. But there's also a lot of AI left out there potentially is just simply a script. So, you know, one of the challenges that you'll always have is that, if it were scripted, is it scripted because, somebody's already developed the AI and now just pulled out all the answers and just using the answers straight? Or is it active learning and changing on its own? I would tend to say that anything that's learning and changing on its own, that's where you're having the evolving AI and that's where you get the most power from. >> Bob what's your take on this, AI real? >> Yeah, if you look at Google, What you see is AI really became real in 2014. That's when the AI and ML really became a thing in the industry and when you look why did it become a thing in 2014? It's really back when we actually saw TensorFlow, open source technology really become available. It's all that Amazon Compute story. You know, you look what we're doing here at Mist, I really don't have to worry about compute storage, except for the Amazon bill I get every month now. So I think you're really seeing AI become real, because of some key turning points in the industry. >> Ed, your take, AI real? >> Yeah, so it depends on what lens you want to kind of look at it through. The notion of intelligence is something that's kind of ill defined and depending how how you want to interpret that will kind of guide whether or not you think it's real. I tend to all things AI if it has a notion of agency. So if it can navigate its problem space without human intervention. So, really it depends on, again, what lens you kind of want to look at it through? It's a set of moving goalposts, right? If you take your smartphone back to Turing When he was coming up with the Turing test and asked them if this intelligent, or some value intelligent device was AI, would that be AI, to him probably back then. So really it depends on how you kind of want to look at it. >> Is AI the same as it was in 1988? Or has it changed, what's the change point with AI because some are saying, AI's been around for a while but there's more AI now than ever before, Ed we'll start with you, what's different with AI now versus say in the late 80s, early 90s? >> See what's funny is some of the methods that we're using aren't different, I think the big push that happened in the last decade or so has been the ability to store as much data as we can along with the ability to have as much compute readily disposable as we have today. Some of the methodologies I mean there was a great Wired article that was published and somebody referenced called, method called Eigenvector Decomposition they said it was from quantum mechanic, that came out in 1888 right? So it really a lot of the methodologies that we're using aren't much different, it's the amount of data that we have available to us that represents reality and the amount of compute that we have. >> Bob. >> Yeah so for me back in the 80s when I did my masters I actually did a masters on neural networks so yeah it's been around for a while but when I started Mist what really changed was a couple things. One is this modern cloud stack right so if you're going to have to build an AI solution really have to have all the pieces ingest tons of data and process it in real time so that is one big thing that's changed that we didn't have 20 years ago. The other big thing is we had access to all this open source TensorFlow stuff right now. People like Google and Facebook have made it so easy for the average person to actually do an AI project right? You know anyone here, anyone in the audience here could actually train a machine learning model over the weekend right now, you just have to go to Google, you have to find kind of the, you know they have the data sets you want to basically build a model to recognize letters and numbers, those data sets are on the internet right now and you personally yourself could go become a data scientist over the weekend. >> Gene, your take. >> Yeah I think also on top of that because of all that availability on the open software anybody can come in and start playing with AI, it's also building a really large experience base of what works and what doesn't work and because they have that now you can actually better define the problem you're shooting for and when you do that you increase you know what's going to work, what's not going to work and people can also tell you that on the part that's not going to work, how's it going to expand but I think overall though this comes back to the question of when people ask what is AI, and a lot of that is just being focused on machine learning and if it's just machine learning that's kind of a little limited use in terms of what you're classifying or not. Back in the early 80s AI back then is really what people are trying to call artificial general intelligence nowadays but it's that all encompassing piece. All the things that you know us humans can do, us humans can reason about, all the decision sequences that we make and so you know that's the part that we haven't quite gotten to but there is all the things that's why the applications that the AI with machine learning classification has gotten us this far. >> Okay machine learning is certainly relevant, it's been one of the most hottest, the hottest topic I think in computer science and with AI becoming much more democratized you guys mentioned TensorFlow, a variety of other open source initiatives been a great wave of innovation and again motivation, younger generations is easier to code now than ever before but machine learning seems to be at the heart of AI and there's really two schools of thought in the machine learning world, is it just math or is there more of a cognition learning machine kind of a thing going on? This has been a big debate in the industry, I want to get your guys' take on this, Gene is machine learning just math and running algorithms or is there more to it like cognition, where do you guys fall on this, what's real? >> If I look at the applications and look what people are using it for it's mostly just algorithms it's mostly that you know you've managed to do the pattern recognition, you've managed to compute out the things and find something interesting from it but then on the other side of it the folks working in say neurosciences, the first people working in cogno-sciences. You know I have the interest in that when we look at that, that machine learning does it correspond to what we're doing as human beings, now because the reason I fall more on the algorithm side is that a lot of those algorithms they don't match what we're often thinking so if they're not matching that it's like okay something else is coming up but then what do we do with it, you know you can get an answer and work from it but then if we want to build true human intelligence how does that all stack together to get to the human intelligence and I think that's the challenge at this point. >> Bob, machine learning, math, cognition is there more to do there, what's your take? >> Yeah I think right now you look at machine learning, machine learning are the algorithms we use, I mean I think the big thing that happened to machine learning is the neural network and deep learning, that was kind of a mild stepping stone where we got through and actually building kind of these AI behavior things. You know when you look what's really happening out there you look at the self driving car, what we don't realize is like it's kind of scary right now, you go to Vegas you can actually get on a driving bus now, you know so this AI machine learning stuff is starting to happen right before our eyes, you know when you go to the health care now and you get your diagnosis for cancer right, we're starting to see AI in image recognition really start to change how we get our diagnosis. And that's really starting to affect people's lives. So those are cases where we're starting to see this AI machine learning stuff is starting to make a difference. When we think about the AI singularity discussion right when are we finally going to build something that really has human behavior. I mean right now we're building AI that can actually play Jeopardy right, and that was kind of one of the inspirations for my company Mist was hey, if they can build something to play Jeopardy we should be able to build something answer questions on par with network domain experts. So I think we're seeing people build solutions now that do a lot of behaviors that mimic humans. I do think we're probably on the path to building something that is truly going to be on par with human thinking right, you know whether it's 50 years or a thousand years I think it's inevitable on how man is progressing right now if you look at the technologically exponential growth we're seeing in human evolution. >> Well we're going to get to that in the next question so you're jumping ahead, hold that thought. Ed, machine learning just math, pattern recognition or is there more cognition there to be had? Where do fall in this? >> Right now it's, I mean it's all math, so we collect something some data set about the world and then we use algorithms and some representation of mathematics to find some pattern, which is new and interesting, don't get me wrong, when you say cognition though we have to understand that we have a fundamentally flawed perspective on how maybe the one guiding light that we have on what intelligence could be would be ourselves right. Computers don't work like brains, brains are what we determine embody our intelligence right, computers, our brains don't have a clock, there's no state that's actually between different clock cycles that light up in the brain so when you start using words like cognition we end up trying to measure ourselves or use ourselves as a ruler and most of the methodologies that we have today don't necessarily head down that path. So yeah that's kind of how I view it. >> Yeah I mean stateless those are API kind of mindsets, you can't run Kubernetes in the brain. Maybe we will in the future, stateful applications are always harder than stateless as we all know but again when I'm sleeping, I'm still dreaming. So cognition in the question of human replacement. This has been a huge conversation. This is one, the singularity conversation you know the fear of most average people and then some technical people as well on the job front, will AI replace my job will it take over the world is there going to be a Skynet Terminator moment? This is a big conversation point because it just teases out what could be and tech for good tech for bad. Some say tech is neutral but it can be shaped. So the question is will AI replace humans and where does that line come from. We'll start with Ed on this one. What do you see this singularity discussion where humans are going to be replaced with AI? >> So replace is an interesting term, so there I mean we look at the last kind of Industrial Revolution that happened and people I think are most worried about the potential of job loss and when you look at what happened during the Industrial Revolution this concept of creative destruction kind of came about and the idea is that yes technology has taken some jobs out of the market in some way shape or form but more jobs were created because of that technology, that's kind of our one again lighthouse that we have with respect to measuring that singularity in and of itself. Again the ill defined definition, or the ill defined notion of intelligence that we have today, I mean when you go back and you read some of the early papers from psychologists from the early 1900s the experiment specifically who came up with this idea of intelligence he uses the term general intelligence as kind of the first time that all of civilization has tried to assign a definition to what is intelligent right? And it's only been roughly 100 years or so or maybe a little longer since we have had this understanding that's been normalized at least within western culture of what this notion of intelligence is so singularity this idea of the singularity is interesting because we just don't understand enough about the one measure ruler or yardstick that we have that we consider intelligence ourselves to be able to go and then embed that inside of a thing. >> Gene what's your thoughts on this, reasoning is a big part of your research you're doing a lot of research around intent and contextual, all these cool behavioral things you know this is where machines are there to augment or replace, this is the conversation, your view on this? >> I think one of the things with this is that that's where the downs still lie, if we have bad intentions, if we can actually start communicating then we can start getting the general intelligence yeah I mean sort of like what Ed was referring to how people have been trying to define this but I think one of the problems that comes up is that computers and stuff like that don't really capture that at this time, the intentions that they have are still at a low level, but if we start tying it to you know the question of the terminator moment to the singularity, one of the things is that autonomy, you know how much autonomy that we give to the algorithm, how much does the algorithm have access to? Now there could be you know just to be on an extreme there could be a disaster situation where you know we weren't very careful and we provided an API that gives full autonomy to whatever AI we have to run it and so you can start seeing elements of Skynet that can come from that but I also tend to come to analysis that hey even with APIs, while it's not AI, APIs a lot of that also we have the intentions of what you're going to give us to control. Then you have the AI itself where if you've defined the intentions of what it is supposed to do then you can avoid that terminator moment in terms of that's more of an act. So I'm seeing it at this point. And so overall singularity I still think we're a ways off and you know when people worry about job loss probably the closest thing that I think that can match that in recent history is the whole thing on automation, I grew up at the time in Ohio when the steel industry was collapsing and that was a trade off between automation and what the current jobs are and if you have something like that okay that's one thing that we go forward dealing with and I think that this is something that state governments, our national government something we should be considering. If you're going to have that job loss you know what better study, what better form can you do from that and I've heard different proposals from different people like, well if we need to retrain people where do you get the resources from it could be something even like AI job pack. And so there's a lot of things to discuss, we're not there yet but I do believe the lower, repetitive jobs out there, I should say the things where we can easily define, those can be replaceable but that's still close to the automation side. >> Yeah and there's a lot of opportunities there. Bob, you mentioned in the last segment the singularity, cognition learning machines, you mentioned deep learning, as the machines learn this needs more data, data informs. If it's biased data or real data how do you become cognitive, how do you become human if you don't have the data or the algorithms? The data's the-- >> I mean and I think that's one of the big ethical debates going on right now right you know are we basically going to basically take our human biases and train them into our next generation of AI devices right. But I think from my point of view I think it's inevitable that we will build something as complex as the brain eventually, don't know if it's 50 years or 500 years from now but if you look at kind of the evolution of man where we've been over the last hundred thousand years or so, you kind of see this exponential rise in technology right from, you know for thousands of years our technology was relatively flat. So in the last 200 years where we've seen this exponential growth in technology that's taking off and you know what's amazing is when you look at quantum computing what's scary is, I always thought of quantum computing as being a research lab thing but when you start to see VC's and investing in quantum computing startups you know we're going from university research discussions to I guess we're starting to commercialize quantum computing, you know when you look at the complexity of what a brain does it's inevitable that we will build something that has basic complexity of a neuron and I think you know if you look how people neural science looks at the brain, we really don't understand how it encodes, but it's clear that it does encode memories which is very similar to what we're doing right now with our AI machine right? We're building things that takes data and memories and encodes in some certain way. So yeah I'm convinced that we will start to see more AI cognizance and it starts to really happen as we start with the next hundred years going forward. >> Guys, this has been a great conversation, AI is real based upon this around theCUBE conversation. Look at I mean you've seen the evidence there you guys pointed it out and I think cloud computing has been a real accelerant with the combination of machine learning and open source so you guys have illustrated and so that brings up kind of the final question I'd love to get each of you's thought on this because Bob just brought up quantum computing which as the race to quantum supremacy goes on around the world this becomes maybe that next step function, kind of what cloud computing did for revitalizing or creating a renaissance in AI. What does quantum do? So that begs the question, five ten years out if machine learning is the beginning of it and it starts to solve some of these problems as quantum comes in, more compute, unlimited resource applied with software, where does that go, five ten years? We'll go start with Gene, Bob, then Ed. Let's wrap this up. >> Yeah I think if quantum becomes a reality that you know when you have the exponential growth this is going to be exponential and exponential. Quantum is going to address a lot of the harder AI problems that were from complexity you know when you talk about this regular search regular approaches of looking up stuff quantum is the one that allows you now to potentially take something that was exponential and make it quantum. And so that's going to be a big driver. That'll be a big enabler where you know a lot of the problems I look at trying to do intentions is that I have an exponential number of intentions that might be possible if I'm going to choose it as an explanation. But, quantum will allow me to narrow it down to one if that technology can work out and of course the real challenge if I can rephrase it into say a quantum program while doing it. But that's I think the advance is just beyond the step function. >> Beyond a step function you see. Okay Bob your take on this 'cause you brought it up, quantum step function revolution what's your view on this? >> I mean your quantum computing changes the whole paradigm right because it kind of goes from a paradigm of what we know, this binary if this then that type of computing. So I think quantum computing is more than just a step function, I think it's going to take a whole paradigm shift of you know and it's going to be another decade or two before we actually get all the tools we need to actually start leveraging quantum computing but I think that is going to be one of those step functions that basically takes our AI efforts into a whole different realm right? Let us solve another whole set of classic problems and that's why they're doing it right now because it starts to let you be able to crack all the encryption codes right? You know where you have millions of billions of choices and you have to basically find that one needle in the haystack so quantum computing's going to basically open that piece of the puzzle up and when you look at these AI solutions it's really a collection of different things going underneath the hood. It's not this one algorithm that you're doing and trying to mimic human behavior, so quantum computing's going to be yet one more tool in the AI toolbox that's going to move the whole industry forward. >> Ed, you're up, quantum. >> Cool, yeah so I think it'll, like Gene and Bob had alluded to fundamentally change the way we approach these problems and the reason is combinatorial problems that everybody's talking about so if I want to evaluate the state space of anything using modern binary based computers we have to kind of iteratively make that search over that search space where quantum computing allows you to kind of evaluate the entire search space at once. When you talk about games like AlphaGo, you talk about having more moves on a blank 19 by 19 AlphaGo board than you have if you put 1,000 universes on every proton of our universe. So the state space is absolutely massive so searching that is impossible. Using today's binary based computers but quantum computing allows you to evaluate kind of search spaces like that in one big chunk to really simplify the aspect but I think it will kind of change how we approach these problems to Bob and Gene's point with respect to how we approach, the technology once we crack that quantum nut I don't think will look anything like what we have today. >> Okay thank you guys, looks like we have a winner. Bob you're up by one point, we had a tie for second but Ed and Gene of course I'm the arbiter but I've decided Bob you nailed this one so since you're the winner, Gene you guys did a great job coming in second place, Ed good job, Bob you get the last word. Unpacking AI, what's the summary from your perspective as the winner of Around theCUBE. >> Yeah no I think you know from a societal point of view I think AI's going to be on par with kind of the internet. It's going to be one of these next big technology things. I think it'll start to impact our lives and people when you look around it it's kind of sneaking up on us, whether it's the self driving car the healthcare cancer, the self driving bus, so I think it's here, I think we're just at the beginnings of it. I think it's going to be one of these technologies that's going to basically impact our whole lives or our next one or two decades. Next 10, 20 years is just going to be exponentially growing everywhere in all our segments. >> Thanks so much for playing guys really appreciate it we have an inventor entrepreneur, Gene doing great research at Dartmouth check him out, Gene Santos at Dartmouth Computer Science. And Ed, technical genius at Dell, figuring out how to make those machines smarter and with the software abstractions growing you guys are doing some good work over there as well. Gentlemen thank you for joining us on this inaugural Around theCUBE unpacking AI Get Smart series, thanks for joining us. >> Thank you. >> Thank you. >> Okay, that's a wrap everyone this is theCUBE in Palo Alto, I'm John Furrier thanks for watching. (upbeat funk music)

>> Narrator: Live from Miami, Florida. It's theCUBE. Covering IBM's data and AI forum. Brought to you by IBM. >> Welcome back to the port of Miami everybody. This is theCube, the leader in live tech coverage. We're here covering the IBM AI and data forum. Of course, the centerpiece of IBM's AI platform is Watson. Beth Smith is here, she's the GM of IBM Watson. Beth, good to see you again. >> You too. Always good to be with theCUBE. >> So, awesome. Love it. So give us the update on Watson. You know, it's beyond Jeopardy. >> Yeah, yeah. >> Oh, wow. >> That was a long time ago now. (laughs) >> Right, but that's what a lot of people think of, when they think of Watson. What, how should we think about Watson today? >> So first of all, focus Watson on being ready for business. And then, a lot of people ask me, "So what is it?" And I often describe it as a set of tools, to help you do your own AI and ML. A set of applications that are AI applications. Where we have prebuilt it for you, around a use case. And there is examples where it gets embedded in a different application or system that may have existed already. In all of those cases, Watson is here, tuned to business enterprise, how to help people operational-wise, AI. So they can get the full benefit, because at the end of the day it's about those business outcomes. >> Okay, so the tools are for the super geeks, (Beth laughs) who actually want to go in and build the real AI. >> (laughs) That's right, that's right. >> The APPS are, okay. It's prebuilt, right? Go ahead and apply it. >> That's right. >> And the embedded is, we don't even know we're using it, right? >> That's right, or you may. Like, QRadar with Watson has an example of using Watson inside of it. Or, OpenPages with Watson. So sometimes you know you're using it. Sometimes you don't. >> So, how's the mix? I mean, in terms of the adoption of Watson? Are there enough like, super techies out there, who are absorbing this stuff? Or is it mostly packaged APPS? Is it a mix? >> So it is a mix, but we know that data science skills are limited. I mean, they're coveted, right? And so those are the geeks, as you say, that are using the tool chain as a part of it. And we see that in a lot of customers and a lot of industries around the world. And then from a packaged APP standpoint, the biggest use case of adoption is really around customer care, customer service, customer engagement. That kind of thing. And we see that as well. All around the world, all different industries. Lots of great adoption. Watson Assistant is our flagship in that. >> So, in terms of, if you think about these digital initiatives, we talked about digital transformation, >> Yup. >> Last few years, we kind of started in 2016 in earnest, it's real when you talk to customers. And there was a ton of experimentation going on. It was almost like spaghetti. Throw against the wall and see what sticks. Are you seeing people starting to place their bets on AI, Narrowing their scope, and really driving you know, specific business value now? >> Beth: Yeah. >> Or is it still kind of all over the place? >> Well, there's a lot of studies that says about 51% or so still stuck in experimentation. But I would tell you in most of those cases even, they have a nice pilot that's in production, that's doing a part of the business. So, 'cause people understand while they may be interested in the sexiness of the technology, they really want to be able to get the business outcomes. So yes, I would tell 'ya that things have kind of been guided, focused towards the use cases and patterns that are the most common. You know, and we see that. Like I mentioned, customer care. We see it in, how do you help knowledge workers? So you think of all those business documents, and papers and everything that exists. How do you assist those knowledge workers? Whether or not it's an attorney or an engineer, or a mortgage loan advisor. So you see that kind of use case, and then you see customers that are building their own. Focused in on, you know, how do they optimize or automate, or predict something in a particular line of business? >> So you mentioned Watson Assistant. So tell us more about Watson Assistant, and how has that affected adoption? >> So Watson Assistant as I said, it is our flagship around customer care. And just to give you a little bit of a data point, Watson Assistant now, through our public cloud, SaaS version, converses with 82 million end users a month. So it's great adoption. And this is, this is enabling customers. Customers of our customers, to be able to get self-service help in what they're doing. And Watson Assistant, you know, a lot of people want to talk about it being a chat bot. And you can do simple chat bots with it. But it's to sophisticated assistance as well. 'Cause it shows up to do work. It's there to do a task. It's to help you deal with your bank account, or whatever it is you're trying to do, and whatever company you're interacting with. >> So chat bots is kind of a, (laughs) bit of a pejorative. But you're talking about digital systems, it's like a super chat bot, right? >> Beth: Yeah. I saw a stat the other day that there's going to be, by I don't know, 2025, whatever. There's going to be more money spent on chat bot development, or digital assistance, than there is on mobile development. And I don't know if that's true or not, >> Beth: Mhm, wow. But it's kind of an interesting thing. So what are you seeing there? I mean, again I think chat bots, people think, oh, I got to talk into a bot. But a lot of times you don't know you're, >> Beth: That's right. >> so they're getting, they're getting better. I liken it to fraud detection. You know, 10 years ago fraud detection was like, six months later you'll, >> Right. >> you'll get a call. >> Exactly. >> And so chat bots are just going to get better and better and better, and now there's this super category that maybe we can define here. >> That's right. >> What is that all about? >> That's right. And actually I would tell you, they kind of, they can become the brain behind something that's happening. So just earlier today I was, I was with a customer and talking about their email CRM system, and Watson Assistant is behind that. So chat bots aren't just about what you may see in a little window. They're really about understanding user intent, guiding the user through what they're trying to either find out or do, and taking the action as a part of it. And that's why we talk about it being more than chat bots. 'Cause it's more than a FAQ interchange. >> Yes, okay. So it's software, >> Beth: Yes. >> that actually does, performs tasks. >> Beth: Yes. >> Probably could call other software, >> Beth: Absolutely. >> to actually take action. >> That's right. >> I mean, I see. We think of this as systems of agency, actually. Making, sort of, >> That's right. >> decisions and then I guess, the third piece of that is, having some kind of human interaction, where appropriate, right? >> That's right. >> What do you see in terms of, you know, infusing humans into the equation? >> So, well a couple of things. So one of the things that Watson Assistant will do, is if it realizes that it's not the expert on whatever it is, then it will pass over to an expert. And think of that expert as a human agent. And while it's doing that, so you may be in the queue, because that human person is tied up, you can continue to do other things with it, while you're waiting to actually talk to the person. So that's a way that the human is in the loop. I would tell you there's also examples of how the agents are being assisted in the background. So they have the interaction directly with the user, but Watson Assistant is helping them, be able to get to more information quicker, and narrow in on what the topic is. >> So you guys talk about the AI ladder, >> Beth: Mhm. >> Sort of, Rob talked about that this morning. My first version of the AI ladder was building blocks. It was like data and AI analytics, ML, and then AI on top of that. >> Beth: Yup. >> I said AI. Data and IA. >> Beth: Yup. >> Information Architecture. Now you use verbs. Sort of, to describe it. >> Beth: Yup. Which is actually more powerful. Collect, organize, analyze and infuse. Now infuse is like the Holy Grail, right? 'Cause that's operationalizing and being able to scale AI. >> Beth: That's right. >> What can you tell us about how successful companies are infusing AI, and what is IBM doing to help them? >> So, I'm glad you picked up first of all, that these are verbs and it's about action. And action leads to outcome, which is, I think, critical. And I would also tell you yes, infuse is, you know, the Holy Grail of the whole thing. Because that's about injecting it into business processes, into workflows, into how things are done. So you can then see examples of how attorneys may be able to get through their legal prep process in just a few minutes, versus 10, 15 hours on certain things. You can see conversion rates of, from a sales standpoint, improve significantly. A number of different things. We've also got it as a part of supply chain optimization, understanding a little bit more about both inventory, but also where the goods are along the way. And particularly when you think about a very complicated thing, there could be a lot of different goods in various points of transit. >> You know, I was sort of joking. Not joking, but mentioning Jeopardy at first. 'Cause a lot of people associate Watson with Jeopardy. >> Beth: Right. >> I can't remember the first time I saw that. It had to be the mid part of the last decade. What was it? >> Beth: February of 2011. >> 2011, okay I thought I even saw demos before that. I'm actually sure I did. Like in, back in some lab in IBM. And of course, the potential like, blew your mind. >> Right. >> I suspect you guys didn't even know what you had at the time. You were like, "Okay, we're going to go change the world." And you know, when you drive up and down 101 in Silicone Valley, it's like, "Oh, Watson this, Watson that." You know, you get the consumer guys, doing facial recognition, ad serving. You know, serving up fake news, you know. All kinds of applications. But IBM started to do something different. You're trying to really change business. Did you have any clue as to what you had at the time? And then how much of a challenge you were taking on, and then bring us to where we are now, and what do you see as a potential for the next 10 years? >> So, of course we had a clue. So let me start there. (Dave laughs) But with that, I think the possibilities of it weren't completely understood. There's no question in my mind about that. And what the early days were, were understanding, okay, what is that business application? What's the pattern that's going to come about as a part of it? And I think we made tremendous progress on that along the way. I would tell you now, you mentioned operationalizing stuff, and you know, now it's about, how do we help companies have it more throughout their company? Through different lines of business, how does it tie to various things that are important to us? And so that brings in things like trust, explainablity, the ethics of what it's doing. Bias detection and mitigation. And I actually believe a lot of that, and the operationalizing it within the processes, is where we're going to head, going forward. Of course there'll continue to be advancements on the features and the capabilities, but it's going to be about that. >> Alright, I'm going to ask you the it's depends question. (Beth laughs) So I know that's your answer, but at the macro, can machines make better diagnosis than doctors today, and if not, when will they be able to, in your view? >> So I would actually tell you that today they cannot, but what they can do is help the doctor make a better diagnosis than she would have done by herself. And because it comes back to this point of, you know, how the machine can process so much information, and help the expert, in this case the doctor's the expert, it could be an attorney, it could be an engineer, whatever. Help that expert be able to augment the knowledge that he or she has as a part of it. So, and that's where I think it is. And I think that's where it will be for my lifetime. >> So, there's no question in your mind that machines today, AI today, is helping make better diagnosis, it's just within augmented or attended type of approach. >> Absolutely. >> And I want to talk about Watson Anywhere. >> Beth: Okay, great. >> So we saw some discussion in the key notes and some demos. My understanding is, you could bring Watson Anywhere, to the data. >> That's right. >> You don't have to move the data around. Why is that important? Give us the update on Watson Anywhere. >> So first of all, this is the biggest requirement I had since I joined the Watson team, three and a half years ago. Was please can I have Watson on-prem, can I have Watson in my company data center, etcetera. And you know, we needed to instead, really focus in on what these patterns and use cases were, and we needed some help in the platform. And so thanks to Cloud Pak for data, and the underlying Red Hat OpenShift and container platform, we now are enabled to truly take Watson anywhere. So you can have it on premise, you can have it on the other public clouds, and this is important, because like you said, it's important because of where your data is. But it's also important because the workloads of today and tomorrow are very complex. And what's on cloud today, may be on premise tomorrow, may be in a different cloud. And as that moves around, you also want to protect the investment of what you're doing, as you have Watson customize for what your business needs are. >> Do you think you timed it right? I mean, you kind of did. All this talk about multicloud now. You really didn't hear much about it four or five years ago. For awhile I thought you were trying to juice your cloud business. Saying, "You want, if you want Watson, you got to go to the IBM cloud." Was there some of that, or was it really just, "Hey, now the timing's right." Where clients are demanding it, and hybrid and multicloud and on-prem situations? >> Well look, we know that cloud and AI go hand in hand. So there was a lot of positive with that. But it really was this technology point, because had I taken it anywhere three and a half years ago, what would've happened is, every deployment would've been a unique environment, a unique stack. We needed to get to a point that was a modern day, you know, infrastructure, if you will. And that's what we get now, with a container based platform. >> So you're able to scale it, such that every instance isn't a snowflake, >> That's right. >> that requires customization. >> That's right. So then I can invest in the enhancements to the actual capabilities it is there to do, not supporting multiple platform instantiations, under the covers. >> Well, okay. So you guys are making that transparent to the customer. How much of an engineering challenge is that? Can you share that with us? You got to run on this cloud, on that cloud, or on forever? >> Well, now because of Cloud Pak for data, and then what we have with OpenShift and Kubernetes and containers, it becomes, well, you know, there's still some technical work, my engineering team would tell you it was a lie. But it's simple now, it's straightforward. It's a lot of portability and flexibility. In the past, it would've been every combination of whatever people were trying to do, and we would not have had the benefit of what that now gives you. >> And what's the technical enable there? Is it sort of open API's? Architecture that allows for the interconnectivity? >> So, but inside of Watson? Or the overall platform? >> The overall platform. >> So I would say, it's been, at it's, at it's core it's what containers bring. >> Okay, really. So it's that, it's that. It's the marriage of your tech, >> Yeah. >> with the container wave. >> That's right. That's right. Which is why the timing was critical now, right? So you go back, yes they existed, but it really hadn't matured to a point of broad adoption. And that's where we are now. >> Yeah, the adoption of containers, Kubernetes, you know, micro services. >> Right, exactly. Now it's on a very steep curve. >> Exactly. >> Alright, give your last word on, big take away, from this event. What do you hearing, you know, what are you, some of the things you're most excited about? >> So first of all, that we have all of these clients and partners here, and all the buzz that you see. And that we've gotten. And then the other thing that I would tell you is, the great client examples. And what they're bragging on, because they are getting business outcomes. And they're getting better outcomes than they thought they would achieve. >> IBM knows how to throw an event. (Beth laughs) Beth, thanks so much for coming to theCUBE. >> Thank you, good to >> Appreciate it. >> see you again. >> Alright, great to see you. Keep it right there everybody, we'll be back. This is theCUBE live, from the IBM Data Forum in Miami, we'll be right back. (upbeat instrumental music)

>> fly from San Francisco. It's the Cube covering Google Club next nineteen Rock Tio by Google Cloud and its ecosystem Partners. >> Okay, welcome back, everyone. We're here live in San Francisco for cubes coverage of Google next twenty nineteen. I'm suffering my coast, David. Want to many men also doing interviews out, getting, reporting and collecting all the data. And we're gonna bring it back on the Q R. Next to gas mark in l. A. Who's a senior technical account manager? AccuWeather at enough was the director product manager. Google Cloud Platform. Now welcome back to the Cube and >> thank you for >> coming on. Thank you. >> You got a customer. Big customer focus here this year. Step function of just logo's growth. New announcements. Technical. Really good stuff. Yeah. What's going on? Give us the update. AP economies here, full throttle. >> I mean, you know, the great thing is it's a pea eye's on all fronts. So what you saw this morning was about standardizing the AP eyes that cloud infrastructure is based on. You saw, You know, how do we build applications with AP eyes at a finer grained level? Micro services, you know, And we've had a lot of great customer examples of people using, and that's what you know with AC. You weather here talking about how do you use a P ice to service and build business models reached developer ecosystems. So you know. So I look at everything today. It's every aspect of it brings it back home tape. Yas. >> It's just things that's so exciting because we think about the service model of cloud and on premise. And now cloud, it's integration and AP Eyes or Ki ki and all and only getting more functional. Talk about your implementation. Aki weather. What do you guys do with Apogee? Google clouds just chair. What >> would implementation is so accurate? There's been running an AP I service for the past ten years, and we have lots of enterprise clients, but we started to realize we're missing a whole business opportunity. So we partnered with Apogee, and we created a new self survey P developer portal that allows developers to go in there, sign up on their own and get started. And it's been great for us as far as like basically unlocking new revenue opportunities with the FBI's because, as he said, everything is a p i cz. We also say everything is impacted by the weather. So why not have everyone used ac you other empty eyes to fulfill their weather needs? >> It wasn't like early on when you guys were making this call, was it more like experimenting? Did men even have a clue where they're like You's a p I I was gonna start grass Roots >> Way knew right >> away like we were working very heavily with the enterprise clients. But we wanted to really cater to the small business Is the individual developers to weather enthusiasts are students. Even so, we wanted to have this easy interface that instead of talking to a sales rep, you could just go through this portal and sign upon your own. It get started and we knew right away there is money to be left or money to be had money left on the table. So we knew right away with by working with apogee and creating this portal, it would run itself. Everyone uses a P eyes and everyone needs to weather, so to make it easier to find and use >> and what was it like? Now let's see how >> it we've been using it now for about two years, and it's been very successful. We've we've seen great, rather revenue growth. And more importantly, it's worked as a great sales channel for us because now, instead of just going directly to an enterprise agreement and talking about legal terms and contracts, you can go through this incremental steps of signed up on your own. Do a free trial. Then you could buy a package. You can potentially increase your package, and we can then monitor that. Let them do it on their own, and it allows us ability to reach out to them and see could just be a new partner that we want to work with, or is there a greater opportunity there? So it's been great for us as faras elite generator in the sales channel to really more revenue, more opportunities and just more aware these'LL process a whole new business model. It's amore awareness, actually replies. Instead, people were trying to find us. Now it's out there and people see great Now it Khun, use it, Get started >> Admission in the back end. The National Weather Service, obviously the government's putting up balloons taking data and presumably and input to your models. How are they connecting in to the AP eyes? Maybe described that whole process. Yeah. Tilak, You other works >> of multiple weather providers and government agencies from around the world. It's actually one of our strengths because we are a global company, and we have those agreements with all kinds of countries around the world. So we ingest all of that data into our back and database, and then we surface it through our story and users. >> Okay, so they're not directly sort of plugging into that ap economy yet? Not yet. So we have to be right there. Well, I >> mean, for now we have the direct data feeds that were ingesting that data, and we make it available through the AC you other service, and we kind of unjust that data with some of our own. Augur those to kind of create our own AccuWeather forecast to >> That's actually a barrier to entry for you guys. The fact that you've built those pipelines from the back end and then you expose it at the front end and that's your business model. So okay, >> tell about that. We're where it goes from here because this is a great example of how silly the old way papering legal contracts. Now you go. It was supposed to maybe eyes exposing the data. Where does it go from here? Because now you've got, as were close, get more complex. This is part of the whole announcement of the new rebranding. The new capabilities around Antos, which is around Hey, you know, you could move complex work clothes. Certainly the service piece. We saw great news around that because it gets more complex with sap. Ichi, go from here. How did these guys go? The next level. >> So, you know, I think that the interesting thing is you look at some of the themes here that we've talked about. It's been about unlocking innovation. It's about providing ways that developers in a self service way Khun, get at the data. The resource is that they need ask. They need them to build these types of new types of applications and vacuum weather experience and their journey on. That's a great example of it. Look, you know, moving from from a set of enterprise customers that they were serving very well to the fact that really ah, whole ecosystem of applications need act needs access to weather data, and they knew that if they could just unlock that, that would be an incredibly powerful things. So we see a lot of variants of that. And in fact, a lot of what you see it's on announcements this morning with Google Cloud is part of that. You know, Google Cloud is very much about taking these resource is that Google is built that were available to a select few and unlocking those in a self service fashion, but in a standard way that developers anywhere and now with andthe oh, switches hybrid a multi cloud wherever they are being able to unlock those capabilities. So why've you? This is a continuation of a P. I promise. You know, we're very excited about this because what we're seeing is more and more applications that are being built across using AP eyes and more more environments. The great thing for Apogee is that any time people are trying to consume AP eyes in a self service fashion agile way, we're able to add value. >> So Allison Wagner earlier was we asked her about the brand promise, and she said, We want our customers, customers they're not help them innovate all the way down our customers customers level. So I'm thinking about whether whether it gets a bad rap, right? I mean, >> look at it >> for years and we make make jokes about the weather. But the weather has been looked uncannily accurate. These they used to be art. Now it's becoming more silent. So in the spirit of innovation, talk about what's happening just in terms of predicting whether it's, you know, big events, hurricanes, tornadoes and some of the innovation that's occurring on that end. >> Well, I mean, look at from a broader standpoint to weather impacts everything. I mean, as we say, you look at all the different products out there in the marketplace that use whether to enhance that. So there's things you can do for actionable decisions, too. It's not just what is the weather, it is. How can whether impact what I'm doing next, what I'm doing, where I go, what I wear, how I feel even said every day you make a conscious and subconscious decision based on the weather. So when you can put that into products and tools and services that help make those actionable decisions for the users. That makes it a very, very powerful products. That's why a lot of people are always seeking out whether data to use it to enhance their product. >> Give us an example. >> What So a famous story I even told Justin my session earlier. Connected Inhaler Company named co hero they use are FBI's by calling our current conditions every time a user had a respiratory attack over time, it started to build a database as the user is using your inhaler. Then use machine learning to kind of find potential weather triggers and learn pattern recognition to find in the future. Based on our forecast, a p I When white might that user have another attack? So buy this. It's a connected health product that's helping them monitor their own health and keep them safe and keep them prepared as opposed to being reactive. >> The inhaler is instrumented. Yeah, and he stated that the cloud >> and that's just that's just one product. I mean, there's all kinds of things connected, thermostats and connect that >> talks about the creativity of the application developer. I think this highlights to me what Deva is all about and what cloud and FBI's all about because you're exposing your resource products. You don't have to have a deaf guy going. Hey, let's car get the pollen application, Martin. Well, what the hell does that mean? You put the creativity of the in the edge, data gets integrated to the application. This kind of kind of hits on the core cloud value problems, which is let the data drive the value from the APP developer. Without your data, that APP doesn't have the value right. And there's multiple instances of weird what it could mean the most viable in golf Africa and Lightning. Abbott could be whatever. Exactly. So this is kind of the the notion of cloud productivity. >> Well, it's a notion of club activity. It's also this idea of a digital value change. So, you know, Data's products and AP Icer products. And and so now we see the emergence of a P I product managers. You know, you know this idea that we're going to go and build a whole ecosystem of products and applications, that meat, the whole set of customer needs that you might not even initially or ever imagine. I'm sure you folks see all the time new applications, new use cases. The idea is, you know, can I I take this capability or can I take this set of data, package it up us an a p I that any developer can use in anyway that they want to innovate on DH, build new functionality around, and it's a very exciting time in makes developers way more productive than they could have been in >> this talks about the C I C pipeline and and programmable bramble AP eyes. But you said something interesting. I wanna unpack real quick talk about this rise of a pipe product managers because, yes, this is really I think, a statement that not only is the FBI's around for a long time to stay, but this is instrumental value. Yes. What is it? A byproduct. Men and okay, what they do. >> So it's a new concept that has Well, I should say a totally new concept. If you talk to companies that have provided a P eyes, you go back to the the early days of you know, folks like eBay or flicker. All of these idea was that you can completely reinvent your business in the way that you partner with other companies by using AP eyes to tie these businesses together. And what you've now seen has been really, I'd say, over the last five years become a mainstream thing. You've got thousands of people out there and enterprises and Internet companies and all sorts of industries that are a P I product managers who are going in looking at how doe I packet a package up, the capabilities the business processes, the data that my business has built and enable other companies, other developers, to go on, package these and embed them in the products and services that they're building. And, uh, that's the job of a P A. Product measures just like a product manager that you would have for any other product. But what they're thinking about is how do they make their A P? I success >> had to Mark's point there. He saw money being left on the table. Small little tweak now opens up a new product line at an economic model. The constructor that's it's pretty *** good. >> It's shifting to this idea platform business models, and it's a super exciting thing that we're seeing the companies that successfully do it, they see huge growth way. Think that every business is goingto have to transition into this AP I product model eventually. >> Mark, what's the what's the role of the data scientist? Obviously very important in your organization and the relationship between the data scientists and the developers. And it specifically What is Google doing, Tio? Help them coordinate, Collaborate better instead of wrangling data all day. Yeah, I mean, >> so far, a data scientists when we actually have multiple areas. Obviously, we're studying the weather data itself. But then we're studying the use case of data how they're actually ingesting it itself, but incorporating that into our products and services. I mean I mean, that's kind of >> mean date is every where the key is the applications have the data built in. This is to your point about >> unnecessarily incorporating it in, but to collaborate on creating products, right? I mean, you're doing a lot of data science. You got application developers. All right? You're talking about tooling, right? R, are they just sort of separate silos or they >> I mean, we obviously have to have an understanding of what day it is going to be successful. What's gonna be adjusted and the easiest way to adjust it a swell so way obviously are analyzing it from that sense is, >> I say step back for a second. Thiss Google Next mark. What's your impression of the show this year? What's the vibe? What's this day? One storyline in your mind. Yet a session you were in earlier. What's been some of the feedback? What's what's it like >> for me personally? It's that AP eyes, power, everything. So that's obviously what we've been very focused on, and that's what the messaging I've been hearing. But yeah, I mean, divide has been incredible here. Obviously be around so many different great minds and the creativity. It's it's definitely >> talk. What was the session that you did? What was the talk about? Outside? Maybe I was some >> of the feedback. Yeah, I mean, so the session I gave was how wacky weather unlock new business opportunities with the FBI's on way. Got great feedback was a full house, had lots of questions afterwards that followed me out to the hallway. It's was actually running here, being held up, but lots people are interested in learning about this. How can they unlock their own opportunity? How can they take what they have existing on and bring it to a new audience? For >> some of the questions that that was kind of the thematic kind of weaken stack rank, the categorical questions were mean point. The >> biggest thing was like trying to make decisions about how for us, for example, having an enterprise model already transitioning that toe a self serve model that actually worked before we're kind of engaging clients directly. So having something that users could look at and on their own, immediately engage with and connect with and find ways that they can utilize it for their own business models and purposes. >> And you gotta be psychic, FBI as a business model, You got FBI product managers, you got you got the cloud and those spanning now multiple domain spaces on Prem Hybrid Multi. >> Well, that last points are very exciting to us. So, you know, if you look at it, you know, it was about two and a half years ago that apogee became part of Google and G C P got into hybrid of multi cloud with aptitude that we were, you know, the definitive AP I infrastructure for AP eyes. Wherever they live. And what we saw this morning was DCP doubling down in a very big way on hybrid of multi clap. And so this is fantastic four. This message of AP eyes everywhere. Apogee is going to be able Teo sit on top of Antos and really, wherever people are looking at either producing are consuming AP eyes. We'LL be able to sit on top of that and make it a lot easier to do. Capture that data and build new business models. On top of it, >> we'LL make a prediction here in the Cube. That happens. He's going to be the center. The value proposition. As those abs get built, people go to the business model. Connecting them under the covers is going to be a very interesting opportunity with you guys. It's >> a very exciting, very exciting for us to >> get hurt here first in the queue, of course. The cubes looking for product manager a p I to handle our cube database. So if you're interested, we're always looking for a product manager. FBI economies here I'm Jeopardy Volante here The Cube Day one coverage Google Next stay with us for more of this short break

(cheerful music) >> Live from San Francisco. It's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome to theCUBE, I'm Lisa Martin. We are at day one of IBM Think 2019, I'm with Dave Vellante. Hey Dave! Hey Lisa, good to see you. The new improved Moscone. >> Exactly, and Stu Miniman, yeah. >> Shiny. >> Yeah, this is the new, it is shiny, The carpets smells new. This is the second annual IBM Think, gentleman where there's this conglomeration of five to six previous events. Doesn't really kick off yet today. I think Partner World starts today but here we are in San Francisco. Moscone North, I think south, and west they have here expecting about 25,000 people. No news yet today, Dave, so let's kind of talk about where IBM is right now with the early part of Q1 of 2019. Red Hat acquisition just approved by shareholders last month. What are your thoughts on the status of Big Blue? >> Well, I think you're right, Lisa, that the Red Hat news is the big news for IBM. We're now entering the next chapter but if you look back for the last five years IBM had to go out and pay two billion dollars for a soft layer to get into the cloud business. That was precipitated by the big, high profile loss of the CIA deal against Amazon. So that was a wake up call for IBM. So they got into the public cloud game. So that's the good news. The bad news is the public cloud's not easy when you're going up against the likes of Google and Microsoft and of course, Amazon. But the linchpin of IBM's cloud strategy is it's SAS portfolio. Over the last 20 years Steve Mills and his organization built a very large software business which they now have migrated into their cloud and so they've got that advantage much like Oracle. They're not a big, dominant cloud infrastructure as a service player but they have a platform where they can put things like Cognitive Solutions and Watson and offer those SAS services to clients. So you'll check on that but when you'll peel through the numbers IBM beat it's numbers last quarter. Stock was up. You know, when it announced the Red Hat acquisition the stock actually got crushed because when you spend 34 billion dollars on a company, you know the shareholders don't necessarily love that but we'll talk about the merits of that move. But they beat in the fourth quarter. They beat on the strength of services. So IBM remains largely a services company, about 60% plus of it's revenues comes from services. It's a somewhat lower margin business, even though IBM margins have been ticking up. As I say, you go back the last five, six years IBM Genesys did Mike's it's microelectronics business, which was a, you know, lost business. It got rid of it's x86 business which is a x86 server business, which is a low margin business. So again, like Oracle, it's focusing on high margin software and services and now we enter the era, Stu, of hybrid cloud with the Red Hat acquisition. A lot of money to pay, but it gets IBM into the next generation of multi cloud. >> Yeah, Dave, the knock I've had against IBM is in many ways they always try to be all things to all people and of course we know you can be good at some things but, you know, it's really tough to be great at everything. And, you know, you talked about cloud, Dave, you know, the SoftLayer acquisition to kind of get into public cloud but, you know, IBM is not one of the big players in public cloud. It's easy. It's Amazon and then followed by you know, Azure, Google, and let's talk Alibaba if we're talking globally. In a multi cloud world IBM has a strong play. As you said, they've got a lot of application assets, they have public cloud, they partner with a lot of the different cloud players out there and with Red Hat they get a key asset to be able to play across all of these multi cloud environments whether we're talking public cloud, private cloud, across all these environments. IBM's been pushing hard into the Kubernetes space, doing a lot with Istio. You know, where they play there, in Red Hat is a key piece of this puzzle. Red Hat running at about three billion dollars of revenue and paying 34 billion dollars but, you know, this is a linchpin as to say how does IBM stay relevant in this cloud world going forward? It's really a you know, a key moment for IBM as to what this means. A lot of discussion as to you know, it's not just the revenue piece but what will Red Hat do to the culture of IBM? IBM has a strong history in open source but you know, you got to, you have a large bench of Red Hat's strong executive team. We're going to see some of them here at the show. We're even going to have one Red Hat executive on our program here and so what will happen once this deal finally closes, which is expected later this year, probably October if you read, you know everything right. But what will it look like as to how will, you know, relatively small Red Hat impact the larger IBM going forward? >> Well, I think it's a big lever, right? I mean we were, Lisa, we were at Cisco Live in Barcelona last week kind of laying out the horses on the track for this multi cloud. Cisco doesn't own it's own public cloud. VMware and Dell don't own it's own public cloud. They both tried to get into the public cloud in the early days and IBM does own it's own public cloud as does Oracle but they're also going hard after this notion of multi cloud as is Cisco, as is VMware. So it sort of sets up the sort of Cisco, IBM Red Hat, VMware, Dell, sort of competing to get after that multi cloud revenue and then HPE fits in there somewhere. We can talk about that. >> So I saw a stat the other day that said in 2018, 80% of companies moved data or apps from public cloud. Reasons being security, control, cost, performance. So to some of the things I've read, Dave, that you've covered recently, if IBM isn't able to really go head to head against the Azures and the AWS, what is their differentiator in this new, hybrid multi cloud world? Is it being able to bring AI, Watson, Cognitive Solutions, better than their competitors in that space that you just mentioned? >> Yeah, IBM does complicate it. You know and cloud and hybrid cloud is complicated and so that's IBM's wheelhouse. And so it tends not to do commodity. So if it's complicated and sophisticated and requires a lot of services and a lot of business processing happening and things like that, IBM tends to excel. So, you know, if you do the SWOT analysis it's big opportunity is to be that multi-cloud provider for it's largest customers. And the larger customers are running, you know, transaction systems on mainframe. They're running cognitive systems on things like power. They've got a giant portfolio, at IBM that is, and they can cobble things together with their services and solve problems and that's kind of how IBM approaches the marketplace. Much different than say, Stu, Cisco or VMware. >> Yeah, Dave, you're absolutely right. You know one of the things I look at is you know, in this multi-cloud space we've see the SI's that are very important there. Companies like Accenture and KPMG and the like. IBM partners with them but IBM also has a large services business. So, you know who's going to be able to help customers get in there and figure out this rather complicated environment. So we are definitely one of the things I want to dig into this week is understand where IBM is at the Cisco Show, Dave. We've talked about their messaging was the bridge to you know what's possible. You know meet the customers where they are, show them how to reach into the future and from Cisco's standpoint, it's strong partnerships with AWS and Google at the forefront. So IBM has just one of the broadest portfolios in the industry. They absolutely play in every single piece but you know customers need good consulting as to Okay, what's going to be the fit for my business. How do I modernize, how do I go forward? And IBM's been down this trip for a number of years. >> Well the in the legacy of Ginni Rometty, in my opinion is going to be determined by the pace at which it can integrate Red Hat and use Red Hat as a lever. Ginni Rometty, when she was doing the roadshow with Jim Whitehurst kept saying it's not a backend loaded deal, and the reason it's not a backend loaded deal is because IBM is a 20 plus billion dollar outsourcing business and they're going to plug Red Hat right into that business to modernize applications. So there's a captive revenue source for IBM. In my view they have to really move fast, faster than typically IBM moves. We've been hearing about strategic initiatives and cloud, and Watson and it's been moving too slow in my opinion. The Red Hat acquisition has to move very very quickly. It's got to move at the speed of cloud and that's going to determine in my opinion-- >> So, actually, so a couple of weeks after the acquisition Red Hat had brought in an analyst to hear what was going on, and while the discussion is Red Hat will stay a distinct brand, there's going to be no lay offs were >> Yeah absolutely. >> Going to keep them separate, what they will get is IBM can really help them scale so >> Yep. Red Hat is getting into some new environments, you know that whole services organization, Red Hat doesn't have that. So IBM absolutely can plug in there and we think really accelerate, the old goal for Red Hat was okay how do we get from that three billion dollars to five billion dollars in the next couple of years. IBM thinks that they can accelerate that even faster. >> And Lisa I think the good news is IBM has always had an affinity toward open source. IBM was really the first, really to make a big investment you know they poured a billion dollars into Linux as a means of competing with Microsoft back in the day, and so they've got open source chops. So for those large IBM customers that might not want to go it alone on open source and you know Red Hat's kind of the cool kid on the block. But at the same time, you know there's some risks there. Now IBM can take that big blue blanket wrap it around it's largest customers and say okay, we've got you covered in open source, we've got the Red Hat asset, and we've got the services organization to help you modernize your application portfolio. >> One of the things too that Stu, you brought up a couple minutes ago is culture. And so looking at what, Red Hat estimates that it's got about eight million developers world wide using their technologies and this is an area that IBM had historically not been really focused on. What are some of the things that you're expecting to hear this week or see this week with respect to the developer community embracing IMB? >> Yeah and Lisa it's not like IBM hasn't been trying to get into the developer community. I remember back at some of the previous shows Edge and Pulse and the like, they would have you know Dev at and try to do a nice little piece of it but it really didn't gain as much traction as you might like. Compare and contrast that with cisco, we've been watching over the last five years the DevNet community. They've got over half a million developers on that platform. So you know, developer engagement usually requires that ground level activity where I've seen good work from IBM has been getting into that cloud native space. So absolutely seen them at the Kubernetes shows working in the container space very heavily and of course that's an area that Red Hat exceeds. So the Linux developers are absolutely there. Now you mentioned how many developers Red Hat has and in that multi cloud, cloud native space, you know Red Hat one of the leaders if not kind of the leader in that space and therefore it should help super charge what IBM is doing, give them some credibility. I'd love to see how many developers we see at this show, you know, you've been to this show Dave and you've been to this show before, it looked more enterprisey to me from the outside-- >> Well, I'm glad you brought up developers because that is the lynch pin of the Red Hat acquisition. If you look at the companies that actually have in the cloud that have a strong developer affinity obviously Microsoft does and always had AWS clearly does Google has you know it's developer community. Stu you mentioned Sisco. Sisco came at it from a networking standpoint and opened up it's network for infrastructure's code. One of the few legacy hardware companies that's done a good job there. VMware, you know not so much. Right? Not really a big developer world and IBM has tried as you pointed out. When they announced Bluemix but that really didn't take off in the developer world. Now with Red Hat IBM, it's your point eight million developers. That is a huge asset for IBM and one that as I said before it absolutely has to leverage and leverage fast. >> And what are you expectations in terms of any sort of industry deeper penetration? There's been some big cloud deals, cloud wins that IBM has made is recent history. One of them being really big in the energy sector. Are you guys kind of expecting to see any sort of industry deeper penetration as a result of what the Red Hat Acquisition will bring? >> Well thats IBM's strength. Stu you pointed out before, it's Accenture, you know Ernie Young, to a lesser extend maybe KPNG but those big SI's and IBM. When IBM bought PWC Gerstner transformed the company and it became a global leader with deep deep industry expertise. That is IBM's you know, savior frankly over these past many many years. So it can compete with virtually anybody on that front and so yes absolutely every industry is being transformed because of digital transformation. IBM understands this as well as anybody. It's a boon for services, it's a good margin business and so that's their competitive advantage. >> Yeah I mean it ties back into their services. I think back when I lived on the vendor side I learned a lot of the industry off of watching IBM. I see how many companies are talking about smarter cities. IBM had you know a long history of working In those environment's. Energy, industrial, IBM is very good at digging into the needed requirements of specific industries and driving that forward. >> So we're going to be here for four days as we mentioned, today is day one. We're going to be talking a lot about this hybrid multi-cloud world. But some of the double clicks we're going to do is talking about data protection, modern data protection, you know a lot of the statistics say that there's eighty percent of the worlds data isn't searchable yet. We all hear every event we do guys, data is the new oil. If companies can actually harness that, extract insights faster than their competition. Create new business models, new services, new products. What are your expectations about how, I hear a lot get your data AI ready. As a marketer I go, what does that mean? What are your thoughts Stu on, and we're sitting in a lot of signage here. How is IBM going to help companies get AI, Data rather AI ready and what does that actually mean? >> So IBM really educated a lot of the world and the broader world as to what some of this AI is. I mean I know we all watched many years ago when Watson was on Jeopardy and we kind of hit through the past the peak and have been trying to sort out okay well how can IBM monetize this? They're taking Watson and getting it into healthcare, they're getting it into all these other environments. So IBM is well known in the AI space. Really well known in the data space but there's a lot of competition and we're still relatively early in the sorting how this new machine learning and AI are going to fit in there. You know we spent a lot of time looking at things like RPA was kind of the gateway drug of AI if you will robotic process automation. And I'm not sure where IBM fit's into that environment. So once again IBM has always had a broad portfolio they do a lot of acquisitions in the space. So you know how can they take all those pieces, pull them together, get after the multicloud world, enable developers to be able to really leverage data even more that's possible and as you said you know more than eighty percent of data today isn't used, you know from an infrastructure stand point I'm looking at how do things like edge computing all get pulled into this environment and lot of questions still. >> IBM is going after hard problems like I said before. You don't expect IBM to be doing things like ad serving with Alexa. You know that's not IBM's game, they're not going to appropriate to sell ad's they're going to take really hard complex problems and charge a lot of money for big services engagements to transform companies. That's their game and that's a data game for sure. >> It's a data game and one of the pieces too that I'm excited to learn about this week is what they're doing about security. We all know you can throw a ton of technology at security and infrastructure but there's the people piece. So we're going to be having a lot of conversations about that as well. Alright guys looking forward to a full week with you and with John joining us at IBM Think I'm Lisa Martin for Dave Vellante and Stu Miniman. You're watching theCUBE live day one IBM Think 2019. Stick around we'll be right back with our next guest. (energetic electronic 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)

>> Announcer: Live from Las Vegas it's the CUBE covering IBM Think 2018. Brought to you by IBM. (upbeat music) >> Welcome back to the CUBE we are live at Day one of IBM Think 2018, I'm Lisa Martin with Dave Vellante we're at Mandalay Bay in Las Vegas, 40 plus thousand people at this event, we're excited to welcome to the CUBE Ashok Reddy the Group GM of DevOps at CA, welcome to the CUBE. >> Great to be here. >> So you were at IBM, you're now at CA, you came over a couple of years ago. Digital business transformation, a buzzword, we talk about it a lot on the CUBE, I want to kind of kick things off with you about what is CA seeing with respect to helping businesses evolve to a digital enterprise? What is a digital enterprise and where does trust come into that as a key enabler? >> Yeah I think that, you know, when you look at the enterprise all businesses today are becoming technology-based businesses, so it doesn't matter what industry you are in, whether you are financial services, garment retail, so every industry's innovation is coming from technology and software. So in that context, if somebody, if I'm a bank today and people used to walk into my, you know use the ATM or they will come into my store, but you may deal with a few hundred or few million people, but now when you become a digital enterprise, you're scaling from a few hundred, a few million to almost a billion people or more who could be accessing your services all the web, all the mobile, as well as now AI as a channel. So how do you actually work, scale the business from just dealing with people who you had prior relationships with to people who have to deal with now billions of people, could be devices and bots, in a very digital world where you don't have prior trust and relationships established. So that's where I think about digital enterprise as who is moving from a traditional way of doing business to now you're scaling to five to six billion people, devices, and everything else, but then the trust comes in because how do you trust whether a user is actually a real user or if I'm a user how do I trust a enterprise because I'm dealing with them virtually. So this really becomes a two-way thing, so it's really that trust becomes much more important. >> I want to come to back to trust in a minute but this whole notion of digital, everybody talks about digital transformation, different things to different people, everybody uses the uberization example, but everybody's trying to get digital right, all the customers that we talk to, the companies that are organizing around it. Do you see that in the marketplace and what is your advice to customers who are tryin' to get it right? >> Yeah I mean, I think it's a great question, I think part of the being digital is really about are you, sometimes people are what I call digital washing things, you basically adopt a few things, but in a true sense of digital, it's all about how do I actually understand the user needs and experimentation? It starts with really, you have a hypothesis and how do I actually go about acquiring new customers and not just a few but where you're trying to acquire millions of them and in a digital world, you are establishing things where we call, you know people talk about there are different channels, right, digital sales and others. Most of the enterprises typically have been dealing with direct relationship, so if I want to now create awareness of my services and products as a company, it's not about direct sales anymore, people are using different means to understand about the products and services themselves. So it becomes more about in that context your sales will have to change in the first place, your marketing has to be about, you know, how do I acquire digital in a channel perspective. So you're to change your processes such that feedback becomes much more important, it's not about just selling, people actually use your product and now you're getting feedback and that needs to be very much faster than what it used to be, because it's all about experience and people are going to change, you know if you don't get a response on your mobile app for a few seconds they're going to switch, it's not that way in a traditional way, right. So the type of things what people do in a digital is quite different than in a traditional way. >> I want to follow that up with an observation, and get CA's point of view, or maybe even your personal point of view. When you think about mobile, social, cloud, SAS, big data, these aren't really discrete industries anymore, they've sort of all come together, and it seems like digital is about these sets of digital services that are built on top of all those things. I mean when you think about even, logging in with LinkedIn, Twitter, or Facebook, I mean those are digital services that we can all access and it seems like disruption is coming from companies who are able to form new businesses leveraging those digital services that are a part of this new fabric that's emerging. So is that a reasonable premise? And where does CA fit in that fabric? >> Yeah, you know I think that's a, you know basically what has happened, right, if I look at more from a applications perspective we have gone from traditional line-server, desktop applications to web to mobile, but now the latest thing is AI, right? It's more like AI first applications, so when I look at a process perspective, the disruptions you are talking about is people used to do waterfall and then it was fast waterfall then agile came in and people are saying, "Okay let me develop," the development become agile, but you need to bring the rest of the organization and that where DevOps came in. But now you're looking at, if I am looking at an application and I want to build an application and get feedback, people who build cloud-first models that's what it work, like whether it's an Amazon or LinkedIn, examples you are giving, but now if the application itself is changing, right? I look at it as like a thermostat, a digital thermostat verses a Nest, when I develop and deploy something quickly, it was still predefined, it's a deterministic application but now with the AI type application where everybody is going towards the application itself starts changing, it starts learning and now it starts going to make decisions so how do you actually develop and deploy, test something which you don't know what it's going to do based on the data and that's really the next paradigm, right? Because the cloud itself is making everybody equal because if everybody uses SAS applications on the cloud so what's a differentiation for a company? So that's where I think we look at it as you really still need to understand your customers, your domain, and being able to understand and learn from them and the specific algorithms or whatever you apply, you train that and how what action you're going to take is where we think CA, when we say transform it's not just about transforming how do you do development and how do you do infrastructure and moving cloud, but just because you become a cloud, everybody can go to cloud and infrastructure, people are providing AWS and Azure and IBM Cloud, then what happens to the companies, right? To me that's where the transformation is the secret sauce of what industry you're in, how you understand your customers and what you're going to do with it. >> Okay so I would agree, cloud, let's call it, let's say cloud is table stakes, right? I mean, it's there, sets of services that anybody can use. Then there's data, that's a little harder, putting data at the core of your enterprise is nontrivial and then applying machine intelligence or AI to that data, to learn, to improve, and to have a culture of speed and DevOps, that's the hard part. So that's where CA fits, is that right? >> Ashok: Yeah you know I think so. >> Maybe you could add some color. >> Exactly right so from a data perspective part of it comes from, you know when you are collecting all this information from different companies and people, privacy becomes a big issue, right? So how do you make sure that the data somebody gives you is private and you're not sharing it with somebody else? And people are sharing personal information here, including the IP addresses, where you are located, if you think about all of us, there is so much information about us available. But how do you make sure that that's private? And it's almost like I use an example of somebody gives you a credit card information on the digital world, it's almost like you are going, it's like somebody giving you a wallet and you're looking at their entire wallet just because they gave you a credit card, right? So we need to make sure that we actually are focusing on the privacy, so we actually help customers around making sure that data what's private, and whether it's data in rest or in data in motion, and there's lots of laws like GDPR and others where, in Europe for example, right, you can't have the data leave a particular country or a data center, so how do you make sure that happens? And the second part is, in machine learning there's so much bias in the data, the machine learning is nothing but computational statistics and it's going to come up with a signal based on the data you provide. What if there is data is biased? There's a lot of bias in the data and now how do you know whether you can prevent the, you know the bias in the data? And then you know we have a lot of other things, but it's about the speed and agility, but also how do I test things? And make sure that, you know, the data itself is one aspect but the services are available to you 24 by seven anytime anywhere. >> With respect to some of the announcements that have come out already from IBM, related to cloud, related to AI, you mentioned security, what excites you in your role as the group GM for DevOps in terms of the directions that they're going and especially where AI is concerned? >> No, you know I think IBM you know when I look at through all the way when the first tunnels to the Jeopardy and the Watson, I think there has been so much of innovations and a lot, and of course there has been a lot of hype also, but I think now you are getting to a point where there is significant progress in both machine learning as well as applying deep learning and some of the things that are solving real-world problems like healthcare, right? You're trying to solve, people talk about AI well it's going to take away jobs, but I think it's not taking away jobs it's the tasks people won't do, most people have a lot of tasks, mundane tasks, but if you are able to solve the world's biggest problems some healthcare related, you're finding people who are disabilities and a lot of things around, if you look at the automation which is creating really new opportunities for many people to focus on the higher value things. So I think the IBM has brought together industry focus, which is great, it's not just about technology but let me go and look at healthcare industry, the supply-chain with blockchain, right? I think the combination of blockchain with AI and machine learning is also changing the whole aspects of what we knew, the trust comes back into this because you know IBM is announcing with the hyperledger, something around zero knowledge proof which is really around, if you are somebody who is let's say you're under 21, you look like you're somebody who is under 21. >> Lisa: Thank you. >> And if somebody has to check your proof of age on a, somebody not knowing you on the web or on the digital world, how do they verify that? Without you giving too much information. So that's something where, it's like zero knowledge proof, so that's being built into the blockchain, so things like that, combining blockchain and now with the mainframe that is a z14, which has highest levels of encryption. So you can really start providing a true system of trust and a digital trust for, both from a user's perspective as well as enterprises. >> Yeah the whole KYC, know your customer, is just exploding in terms of interest and importance and you guys are obviously, it sounds like you're participating there directly. >> Yeah we are launching a bunch of this mainframe as a service for that to help customers because we also have problems with people retiring on the mainframe and they don't understand so what we're doing is to bring a notion of how do I use the tools I already know, like opensource tools or whatever, so we have an initiative for Brightside which is really help developers use the things that they already know but underneath the covers they're actually building things for the mainframe. So that solves the problem of knowing the mainframe but don't make mainframe different. >> So on, kind of closing things out, from an innovation perspective, one of the things that you talked about with automation, and we heard this earlier Dave from KPMG, is that machine learning and AI are actually going to be enablers of a lot of things including new opportunities, new careers paths, et cetera, rather than looking at it as oh there's going to take away humans for jobs, I thought that was interesting that you brought that up. Talk to us about the innovation, the culture of innovation, at CA, how is the culture enabling you to do your job better and really work with customers in a symbiotic way to really understand what problems need to be solved? What's the innovation culture like? >> Yeah I mean that's actually you know we actually have a, we've created a more like a incubation program, a startup within CA, and one of the things we have done is if anybody has a really good idea to solve a customer outcome, we kind of go through this whole like a mini while process and they actually come in and pitch the idea, then we actually fund those as a separate start ups within the company, we have more than 15 startups, some of them are graduated, either they, we buy them internally from a different business unit or we can even we take them public to other companies, right? So we have done that, which is energized a lot of the people like they can become their own founders and they can bring the innovations. But within even the product development teams we do a lot of hackathons and being able to use AI and machine learning and blockchain, we are basically build up machine learning first culture, so everybody from people who have been there for 30 years to people who are just come in are all learn this and they're looking at what are the type of things I can apply AI for my data to improve how I can look at patterns and improve the automation. But there is also, as applications become AI first, how can kind of help customers around that? So I think it's a exciting time for everybody and we are seeing that are already in terms of the numbers of innovations and patterns we're finding. >> Well Ashok, thanks so much for sharing your insights and what's going on with IBM in CA, we thank you so much for your time. >> Oh great, thank you for having me on. >> We want to thank you for watching the CUBE. We are live at Day one of IBM's inaugural Think event, I'm Lisa Martin with Dave Vellante, stick around Dave and I are going to be right back with our final guest and then we'll do a wrap of the exciting things that we've heard today. We'll be right back. (upbeat music)

>> Narrator: Live from New York, it's the Cube. Covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. >> And welcome back here to New York City. We're at IBM's Machine Learning Everywhere: Build Your Ladder to AI, along with Dave Vellante, John Walls, and we're now joined by Sam Lightstone, who is an IBM fellow in analytics. And Sam, good morning. Thanks for joining us here once again on the Cube. >> Yeah, thanks a lot. Great to be back. >> Yeah, great. Yeah, good to have you here on kind of a moldy New York day here in late February. So we're talking, obviously data is the new norm, is what certainly, have heard a lot about here today and of late here from IBM. Talk to me about, in your terms, of just when you look at data and evolution and to where it's now become so central to what every enterprise is doing and must do. I mean, how do you do it? Give me a 30,000-foot level right now from your prism. >> Sure, I mean, from a super, if you just stand back, like way far back, and look at what data means to us today, it's really the thing that is separating companies one from the other. How much data do they have and can they make excellent use of it to achieve competitive advantage? And so many companies today are about data and only data. I mean, I'll give you some like really striking, disruptive examples of companies that are tremendously successful household names and it's all about the data. So the world's largest transportation company, or personal taxi, can't call it taxi, but (laughs) but, you know, Uber-- >> Yeah, right. >> Owns no cars, right? The world's largest accommodation company, Airbnb, owns no hotels, right? The world's largest distributor of motion pictures owns no movie theaters. So these companies are disrupting because they're focused on data, not on the material stuff. Material stuff is important, obviously. Somebody needs to own a car, somebody needs to own a way to view a motion picture, and so on. But data is what differentiates companies more than anything else today. And can they tap into the data, can they make sense of it for competitive advantage? And that's not only true for companies that are, you know, cloud companies. That's true for every company, whether you're a bricks and mortars organization or not. Now, one level of that data is to simply look at the data and ask questions of the data, the kinds of data that you already have in your mind. Generating reports, understanding who your customers are, and so on. That's sort of a fundamental level. But the deeper level, the exciting transformation that's going on right now, is the transformation from reporting and what we'll call business intelligence, the ability to take those reports and that insight on data and to visualize it in the way that human beings can understand it, and go much deeper into machine learning and AI, cognitive computing where we can start to learn from this data and learn at the pace of machines, and to drill into the data in a way that a human being cannot because we can't look at bajillions of bytes of data on our own, but machines can do that and they're very good at doing that. So it is a huge, that's one level. The other level is, there's so much more data now than there ever was because there's so many more devices that are now collecting data. And all of us, you know, every one of our phones is collecting data right now. Your cars are collecting data. I think there's something like 60 sensors on every car that rolls of the manufacturing line today. 60. So it's just a wild time and a very exciting time because there's so much untapped potential. And that's what we're here about today, you know. Machine learning, tapping into that unbelievable potential that's there in that data. >> So you're absolutely right on. I mean the data is foundational, or must be foundational in order to succeed in this sort of data-driven world. But it's not necessarily the center of the universe for a lot of companies. I mean, it is for the big data, you know, guys that we all know. You know, the top market cap companies. But so many organizations, they're sort of, human expertise is at the center of their universe, and data is sort of, oh yeah, bolt on, and like you say, reporting. >> Right. >> So how do they deal with that? Do they get one big giant DB2 instance and stuff all the data in there, and infuse it with MI? Is that even practical? How do they solve this problem? >> Yeah, that's a great question. And there's, again, there's a multi-layered answer to that. But let me start with the most, you know, one of the big changes, one of the massive shifts that's been going on over the last decade is the shift to cloud. And people think of the shift to cloud as, well, I don't have to own the server. Someone else will own the server. That's actually not the right way to look at it. I mean, that is one element of cloud computing, but it's not, for me, the most transformative. The big thing about the cloud is the introduction of fully-managed services. It's not just you don't own the server. You don't have to install, configure, or tune anything. Now that's directly related to the topic that you just raised, because people have expertise, domains of expertise in their business. Maybe you're a manufacturer and you have expertise in manufacturing. If you're a bank, you have expertise in banking. You may not be a high-tech expert. You may not have deep skills in tech. So one of the great elements of the cloud is that now you can use these fully managed services and you don't have to be a database expert anymore. You don't have to be an expert in tuning SQL or JSON, or yadda yadda. Someone else takes care of that for you, and that's the elegance of a fully managed service, not just that someone else has got the hardware, but they're taking care of all the complexity. And that's huge. The other thing that I would say is, you know, the companies that are really like the big data houses, they got lots of data, they've spent the last 20 years working so hard to converge their data into larger and larger data lakes. And some have been more successful than others. But everybody has found that that's quite hard to do. Data is coming in many places, in many different repositories, and trying to consolidate, you know, rip the data out, constantly ripping it out and replicating into some data lake where you, or data warehouse where you can do your analytics, is complicated. And it means in some ways you're multiplying your costs because you have the data in its original location and now you're copying it into yet another location. You've got to pay for that, too. So you're multiplying costs. So one of the things I'm very excited about at IBM is we've been working on this new technology that we've now branded it as IBM Queryplex. And that gives us the ability to query data across all of these myriad sources as if they are in one place. As if they are a single consolidated data lake, and make it all look like (snaps) one repository. And not only to the application appear as one repository, but actually tap into the processing power of every one of those data sources. So if you have 1,000 of them, we'll bring to bear the power 1,000 data sources and all that computing and all that memory on these analytics problems. >> Well, give me an example why that matters, of what would be a real-world application of that. >> Oh, sure, so there, you know, there's a couple of examples. I'll give you two extremes, two different extremes. One extreme would be what I'll call enterprise, enterprise data consolidation or virtualization, where you're a large institution and you have several of these repositories. Maybe you got some IBM repositories like DB2. Maybe you've got a little bit of Oracle and a little bit of SQL Server. Maybe you've got some open source stuff like Postgres or MySQL. You got a bunch of these and different departments use different things, and it develops over decades and to some extent you can't even control it, (laughs) right? And now you just want to get analytics on that. You just, what's this data telling me? And as long as all that data is sitting in these, you know, dozens or hundreds of different repositories, you can't tell, unless you copy it all out into a big data lake, which is expensive and complicated. So Queryplex will solve that problem. >> So it's sort of a virtual data store. >> Yeah, and one of the terms, many different terms that are used, but one of the terms that's used in the industry is data virtualization. So that would be a suitable terminology here as well. To make all that data in hundreds, thousands, even millions of possible data sources, appear as one thing, it has to tap into the processing power of all of them at once. Now, that's one extreme. Let's take another extreme, which is even more extreme, which is the IoT scenario, Internet of Things, right? Internet of Things. Imagine you've, have devices, you know, shipping containers and smart meters on buildings. You could literally have 100,000 of these or a million of these things. They're usually small; they don't usually have a lot of data on them. But they can store, usually, couple of months of data. And what's fascinating about that is that most analytics today are really on the most recent you know, 48 hours or four weeks, maybe. And that time is getting shorter and shorter, because people are doing analytics more regularly and they're interested in, just tell me what's going on recently. >> I got to geek out here, for a second. >> Please, well thanks for the warning. (laughs) >> And I know you know things, but I'm not a, I'm not a technical person, but I've been a molt. I've been around a long time. A lot of questions on data virtualization, but let me start with Queryplex. The name is really interesting to me. When I, and you're a database expert, so I'm going to tap your expertise. When I read the Google Spanner paper, I called up my colleague David Floyer, who's an ex-IBM, I said, "This is like global Sysplex. "It's a global distributed thing," And he goes, "Yeah, kind of." And I got very excited. And then my eyes started bleeding when I read the paper, but the name, Queryplex, is it a play on Sysplex? Is there-- >> It's actually, there's a long story. I don't think I can say the story on-air, but we, suffice it to say we wanted to get a name that was legally usable and also descriptive. >> Dave: Okay. >> And we went through literally hundreds and hundreds of permutations of words and we finally landed on Queryplex. But, you know, you mentioned Google Spanner. I probably should spend a moment to differentiate how what we're doing is-- >> Great, if you would. >> A different kind of thing. You know, on Google Spanner, you put data into Google Spanner. With Queryplex, you don't put data into it. >> Dave: Don't have to move it. >> You don't have to move it. You leave it where it is. You can have your data in DB2, you can have it in Oracle, you can have it in a flat file, you can have an Excel spreadsheet, and you know, think about that. An Excel spreadsheet, a collection of text files, comma delimited text files, SQL Server, Oracle, DB2, Netezza, all these things suddenly appear as one database. So that's the transformation. It's not about we'll take your data and copy it into our system, this is about leave your data where it is, and we're going to tap into your (snaps) existing systems for you and help you see them in a unified way. So it's a very different paradigm than what others have done. Part of the reason why we're so excited about it is we're, as far as we know, nobody else is really doing anything quite like this. >> And is that what gets people to the 21st century, basically, is that they have all these legacy systems and yet the conversion is much simpler, much more economical for them? >> Yeah, exactly. It's economical, it's fast. (snaps) You can deploy this in, you know, a very small amount of time. And we're here today talking about machine learning and it's a very good segue to point out in order to get to high-quality AI, you need to have a really strong foundation of an information architecture. And for the industry to show up, as some have done over the past decade, and keep telling people to re-architect their data infrastructure, keep modifying their databases and creating new databases and data lakes and warehouses, you know, it's just not realistic. And so we want to provide a different path. A path that says we're going to make it possible for you to have superb machine learning, cognitive computing, artificial intelligence, and you don't have to rebuild your information architecture. We're going to make it possible for you to leverage what you have and do something special. >> This is exciting. I wasn't aware of this capability. And we were talking earlier about the cloud and the managed service component of that as a major driver of lowering cost and complexity. There's another factor here, which is, we talked about moving data-- >> Right. >> And that's one of the most expensive components of any infrastructure. If I got to move data and the transmission costs and the latency, it's virtually impossible. Speed of light's still up. I know you guys are working on speed of light, but (Sam laughs) you'll eventually get there. >> Right. >> Maybe. But the other thing about cloud economics, and this relates to sort of Queryplex. There's this API economy. You've got virtually zero marginal costs. When you were talking, I was writing these down. You got global scale, it's never down, you've got this network effect working for you. Are you able to, are the standards there? Are you able to replicate those sort of cloud economics the APIs, the standards, that scale, even though you're not in control of this, there's not a single point of control? Can you explain sort of how that magic works? >> Yeah, well I think the API economy is for real and it's very important for us. And it's very important that, you know, we talk about API standards. There's a beautiful quote I once heard. The beautiful thing about standards is there's so many to choose from. (All laugh) And the reality is that, you know, you have standards that are official standards, and then you have the de facto standards because something just catches on and nobody blessed it. It just got popular. So that's a big part of what we're doing at IBM is being at the forefront of adopting the standards that matter. We made a big, a big investment in being Spark compatible, and, in fact, even with Queryplex. You can issue Spark SQL against Queryplex even though it's not a Spark engine, per se, but we make it look and feel like it can be Spark SQL. Another critical point here, when we talk about the API economy, and the speed of light, and movement to the cloud, and these topics you just raised, the friction of the Internet is an unbelievable friction. (John laughs) It's unbelievable. I mean, you know, when you go and watch a movie over the Internet, your home connection is just barely keeping up. I mean, you're pushing it, man. So a gigabyte, you know, a gigabyte an hour or something like that, right? Okay, and if you're a big company, maybe you have a fatter pipe. But not a lot fatter. I mean, not orders of, you're talking incredible friction. And what that means is that it is difficult for people, for companies, to en masse, move everything to the cloud. It's just not happening overnight. And, again, in the interest of doing the best possible service to our customers, that's why we've made it a fundamental element of our strategy in IBM to be a hybrid, what we call hybrid data management company, so that the APIs that we use on the cloud, they are compatible with the APIs that we use on premises. And whether that's software or private cloud. You've got software, you've got private cloud, you've got public cloud. And our APIs are going to be consistent across, and applications that you code for one will run on the other. And you can, that makes it a lot easier to migrate at your leisure when you're ready. >> Makes a lot of sense. That way you can bring cloud economics and the cloud operating model to your data, wherever the data exists. Listening to you speak, Sam, it reminds me, do you remember when Bob Metcalfe who I used to work with at IDG, predicted the collapse of the Internet? He predicted that year after year after year, in speech after speech, that it was so fragile, and you're bringing back that point of, guys, it's still, you know, a lot of friction. So that's very interesting, (laughs) as an architect. >> You think Bob's going to be happy that you brought up that he predicted the Internet was going to be its own demise? (Sam laughs) >> Well, he did it in-- >> I'm just saying. >> I'm staying out of it, man. >> He did it as a lightning rod. >> As a talking-- >> To get the industry to respond, and he had a big enough voice so he could do that. >> That it worked, right. But so I want to get back to Queryplex and the secret sauce. Somehow you're creating this data virtualization capability. What's the secret sauce behind it? >> Yeah, so I think, we're not the first to try, by the way. Actually this problem-- >> Hard problem. >> Of all these data sources all over the place, you try to make them look like one thing. People have been trying to figure out how to do that since like the '70s, okay, so, but-- >> Dave: Really hasn't worked. >> And it hasn't worked. And really, the reason why it hasn't worked is that there's been two fundamental strategies. One strategy is, you have a central coordinator that tries to speak to each of these data sources. So I've got, let's say, 10,000 data sources. I want to have one coordinator tap into each of them and have a dialogue. And what happens is that that coordinator, a server, an agent somewhere, becomes a network bottleneck. You were talking about the friction of the Internet. This is a great example of friction. One coordinator trying to speak to, you know, and collaborators becomes a point of friction. And it also becomes a point of friction not only in the Internet, but also in the computation, because he ends up doing too much of the work. There's too many things that cannot be done at the, at these edge repositories, aggregations, and joins, and so on. So all the aggregations and joins get done by this one sucker who can't keep up. >> Dave: The queue. >> Yeah, so there's a big queue, right. So that's one strategy that didn't work. The other strategy that people tried was sort of an end squared topology where every data source tries to speak to every other data source. And that doesn't scale as well. So what we've done in Queryplex is something that we think is unique and much more organic where we try to organize the universe or constellation of these data sources so that every data source speaks to a small number of peers but not a large number of peers. And that way no single source is a bottleneck, either in network or in computation. That's one trick. And the second trick is we've designed algorithms that can truly be distributed. So you can do joins in a distributed manner. You can do aggregation in a distributed manner. These are things, you know, when I say aggregation, I'm talking about simple things like a sum or an average or a median. These are super popular in, in analytic queries. Everybody wants to do a sum or an average or a median, right? But in the past, those things were hard to do in a distributed manner, getting all the participants in this universe to do some small incremental piece of the computation. So it's really these two things. Number one, this organic, dynamically forming constellation of devices. Dynamically forming a way that is latency aware. So if I'm a, if I represent a data source that's joining this universe or constellation, I'm going to try to find peers who I have a fast connection with. If all the universe of peers were out there, I'll try to find ones that are fast. And the second is having algorithms that we can all collaborate on. Those two things change the game. >> We're getting the two minute sign, and this is fascinating stuff. But so, how do you deal with the data consistency problem? You hear about eventual consistency and people using atomic clocks and-- Right, so Queryplex, you know, there's a reason we call it Queryplex not Dataplex. Queryplex is really a read-only operation. >> Dave: Oh, there you go. >> You've got all these-- >> Problem solved. (laughs) >> Problem solved. You've got all these data sources. They're already doing their, they already have data's coming in how it's coming in. >> Dave: Simple and brilliant. >> Right, and we're not changing any of that. All we're saying is, if you want to query them as one, you can query them as one. I should say a few words about the machine learning that we're doing here at the conference. We've talked about the importance of an information architecture and how that lays a foundation for machine learning. But one of the things that we're showing and demonstrating at the conference today, or at the showcase today, is how we're actually putting machine learning into the database. Create databases that learn and improve over time, learn from experience. In 1952, Arthur Samuel was a researcher at IBM who first, had one of the most fundamental breakthroughs in machine learning when he created a machine learning algorithm that will play checkers. And he programmed this checker playing game of his so it would learn over time. And then he had a great idea. He programmed it so it would play itself, thousands and thousands and thousands of times over, so it would actually learn from its own mistakes. And, you know, the evolution since then. Deep Blue playing chess and so on. The Watson Jeopardy game. We've seen tremendous potential in machine learning. We're putting into the database so databases can be smarter, faster, more consistent, and really just out of the box (snaps) performing. >> I'm glad you brought that up. I was going to ask you, because the legend Steve Mills once said to me, I had asked him a question about in-memory databases. He said ever databases have been around, in-memory databases have been around. But ML-infused databases are new. >> Sam: That's right, something totally new. >> Dave: Yeah, great. >> Well, you mentioned Deep Blue. Looking forward to having Garry Kasparov on a little bit later on here. And I know he's speaking as well. But fascinating stuff that you've covered here, Sam. We appreciate the time here. >> Thank you, thanks for having me. >> And wish you continued success, as well. >> Thank you very much. >> Sam Lightstone, IBM fellow joining us here live on the Cube. We're back with more here from New York City right after this. (electronic music)

>> Announcer: Live from New York City, it's The Cube, covering IBM data science for all. Brought to you by IBM. >> Welcome back to data science for all. This is IBM's event here on the west side of Manhattan, here on The Cube. We're live, we'll be here all day, along with Dave Vallente, I'm John Walls Poor Dave had to put up with all that howling music at this hotel last night, kept him up 'til, all hours. >> Lots of fun here in the city. >> Yeah, yeah. >> All the crazies out last night. >> Yeah, but the headphones, they worked for ya. Glad to hear that. >> People are already dressed for Halloween, you know what I mean? >> John: Yes. >> In New York, you know what I mean? >> John: All year. >> All the time. >> John: All year. >> 365. >> Yeah. We have with us now the head of data science, and the VP at Galvanize, Nir Kaldero, and Nir, good to see you, sir. Thanks for being with us. We appreciate the time. >> Well of course, my pleasure. >> Tell us about Galvanize. I know you're heavily involved in education in terms of the tech community, but you've got corporate clients, you've got academic clients. You cover the waterfront, and I know data science is your baby. >> Nir: Right. >> But tell us a little bit about Galvanize and your mission there. >> Sure, so Galvanize is the learning community for technology. We provide the training in data science, data engineering, and also modern software engineering. We recently built a very large, fast growing enterprise corporate training department, where we basically help companies become digital, become nimble, and also very data driven, so they can actually go through this digital transformation, and survive in this fourth industrial revolution. We do it across all layers of the business, from the executives, to managers, to data scientists, and data analysts, and kind of transform and upscale all current skills to be modern, to be digital, so companies can actually go through this transformation. >> Hit on one of those items you talked about, data driven. >> Nir: Right. >> It seems like a no-brainer, right? That the more information you give me, the more analysis I can apply to it, the more I can put it in my business practice, the more money I make, the more my customers are happy. It's a lay up, right? >> Nir: It is. >> What is a data driven organization, then? Do you have to convince people that this is where they need to be today? >> Sometimes I need to convince them, but (laughs) anyway, so let's back up a little bit. We are in the midst of the fourth industrial revolution, and in order to survive in this fourth industrial revolution, companies need to become nimble, as I said, become agile, but most importantly become data driven, so the organization can actually best respond to all the predictions that are coming from this very sophisticated machine intelligence models. If the organization immediately can best respond to all of that, companies will be able to enhance the user experience, get insight about their customers, enhance performances, and et cetera, and we know that the winners in this revolution, in this era, will be companies who are very digital, that master the skills of becoming a data driven organization, and you know, we can talk more about the transformation, and what it consisted of. Do you want me to? >> John: Sure. >> Can I just ask you a question? This fourth wave, this is what, the cognitive machine wave? Or how would you describe it? >> Some people call it artificial intelligence. I think artificial intelligence is like big data, kind of like a buzz word. I think more appropriately, we should call it machine intelligence industrial revolution. >> Okay. I've got a lot of questions, but carry on. >> So hitting on that, so you see that as being a major era. >> Nir: It's a game changer. >> If you will, not just a chapter, but a major game changer. >> Nir: Yup. >> Why so? >> So, okay, I'll jump in again. Machines have always replaced man, people. >> John: The automation, right. >> Nir: To some extent. >> But certain machines have replaced certain human tasks, let's say that. >> Nir: Correct. >> But for the first time in history, this fourth era, machine's are replacing humans with cognitive tasks, and that scares a lot of people, because you look at the United States, the median income of the U.S. worker has dropped since 1999, from $55,000 to $52,000, and a lot of people believe it's sort of the hollowing out of that factor that we just mentioned. Education many believe is the answer. You know, Galvanize is an organization that plays a critical role in helping deal with that problem, does it not? >> So, as Mark Zuckerberg says, there is a lot of hate love relationship with A.I. People love it on one side, because they're excited about all the opportunities that can come from this utilization of machine intelligence, but many people actually are afraid from it. I read a survey a few weeks ago that says that 36% of the population thinks that A.I. will destroy humanity, and will conquer the world. That's a fact that's what people think. If I think it's going to happen? I don't think so. I highly believe that education is one of the pillars that can address this fear for machine intelligence, and you spoke a lot about jobs I talk about it forever, but just my belief is that machines can actually replace some of our responsibilities, right? Not necessarily take and replace the entire job. Let's talk about lawyers, right? Lawyers currently spend between 40% to 60% of the time writing contracts, or looking at previous cases. The machine can write a contract in two minutes, or look up millions of data points of previous cases in zero time. Why a lawyer today needs to spend 40% to 60% of the time on that? >> Billable hours, that's why. >> It is, so I don't think the machine will replace the job of the lawyer. I think in the future, the machine replaces some of the responsibilities, like auditing, or writing contracts, or looking at previous cases. >> Menial labor, if you will. >> Yes, but you know, for example, the machine is not that great right now with negotiations skills. So maybe in the future, the job of the lawyer will be mostly around negotiation skills, rather than writing contracts, et cetera, but yeah, you're absolutely right. There is a big fear in the market right now among executives, among people in the public. I think we should educate people about what is the true implications of machine intelligence in this fourth industrial revolution and era, and education is definitely one of those. >> Well, one of my favorite stories, when people bring up this topic, is when Gary Kasparov lost to the IBM super computer, Blue Jean, or whatever it's called. >> Nir: Yup. >> Instead of giving up, what he said is he started a competition, where he proved that humans and machines could beat the IBM super computer. So to this day has a competition where the best chess player in the world is a combination between humans and machines, and so it's that creativity. >> Nir: Imagination. >> Imagination, right, combinatorial effects of different technologies that education, hopefully, can help keep those either way. >> Look, I'm a big fan of neuroscience. I wish I did my PhD in neuroscience, but we are very, very far away from understanding how our brain works. Now to try to imitate the brain when we don't know how the brain works? We are very far away from being in a place where a machine can actually replicate, and really best respond like a human. We don't know how our brain works yet. So we need to do a lot of research on that before we actually really write a very strong, powerful machine intelligence model that can actually replace us as humans, and outbid us. We can speak about Jeopardy, and what's on, and we can speak about AlphaGo, it's a Google company that kind of outperformed the world champion. These are very specific tasks, right? Again, like the lawyer, the machines can write beautiful contracts with NLP, machines can look at millions and trillions of data and figure out what's the conclusion there, right? Or summarize text very fast, but not necessarily good in negotiation yet. >> So when you think about a digital business, to us a digital business is a business that uses data to differentiate, and serve customers, and maintain customers. So when you talk about data driven, it strikes me that when everybody's saying digital business, digital transformation, it's about a data transformation, how well they utilize data, and if you look at the bell curve of organizations, most are not. Everybody wants to be data driven, many say they are data driven. >> Right. >> Dave: Would you agree most are not? >> I will agree that most companies say that they are data driven, but actually they're not. I work with a lot of Fortune 500 companies on a daily basis. I meet their executives and functional leaders, and actually see their data, and business problems that they have. Most of them do tend to say that they are data driven, but truly just ask them if they put data and decisions in the same place, every time they have to make a decision, they don't do it. It's a habit that they don't yet have. Companies need to start investing in building what we say healthy data culture in order to enable and become data driven. Part of it is democratization of data, right? Currently what I see if lots of organizations actually open the data just for the analyst, or the marketers, people who kind of make decisions, that need to make decisions with data, but not throughout the entire organization. I know I always say that everyone in the organization makes decisions on a daily basis, from the barista, to the CEO, right? And the entirety of becoming data driven is that data can actually help us make better decisions on a daily basis, so how about democratizing the data to everyone? So everyone, from the barista, to the CEO, can actually make better decisions on a daily basis, and companies don't excel yet in doing it. Not every company is as digital as Amazon. Amazon, I think, is actually one of the most digital companies in the world, if you look at the digital index. Not everyone is Google or Facebook. Most companies want to be there, most companies understand that they will not be able to survive in this era if they will not become data driven, so it's a big problem. We try at Galvanize to address this problem from executive type of education, where we actually meet with the C-level executives in companies, and actually guide them through how to write their data strategy, how to think about prioritizing data investment, to actual implementation of that, and so far we are highly successful. We were able to make a big transformation in very large, important organizations. So I'm actually very proud of it. >> How long are these eras? Is it a century, or more? >> This fourth industrial? >> Yeah. >> Well it's hard to predict that, and I'm not a machine, or what's on it. (laughs) >> But certainly more than 50 years, would you say? Or maybe not, I don't know. >> I actually don't think so. I think it's going to be fast, and we're going to move to the next one pretty soon that will be even more, with more intelligence, with more data. >> So the reason I ask, is there was an article I saw and linked, and I haven't had time to read it, but it talked about the Four Horsemen, Amazon, Google, Facebook, and Apple, and it said they will all be out of business in 50 years. Now, I don't know, I think Apple probably has 50 years of cash flow in the bank, but then they said, the one, the author said, if I had to predict one that would survive, it would be Amazon, to your point, because they are so data driven. The premise, again I didn't read the whole thing, was that some new data driven, digital upstart will disrupt them. >> Yeah, and you know, companies like Amazon, and Alibaba lately, that try kind of like in a competition with Amazon about who is becoming more data driven, utilizing more machine intelligence, are the ones that invested in these capabilities many, many years ago. It's no that they started investing in it last year, or five years ago. We speak about 15 and 20 years ago. So companies who were really a pioneer, and invested very early on, will predict actually to survive in the future, and you know, very much align. >> Yeah, I'm going to touch on something. It might be a bridge too far, I don't know, but you talk about, Dave brought it up, about replacing human capital, right? Because of artificial intelligence. >> Nir: Yup. >> Is there a reluctance, perhaps, on behalf of executives to embrace that, because they are concerned about their own price? >> Nir: You should be in the room with me. (laughing) >> You provide data, but you also provide that capability to analyze, and make the best informed decision, and therefore, eliminate the human element of a C-suite executive that maybe they're not as necessary today, or tomorrow, as they were two years ago. >> So it is absolutely true, and there is a lot of fear in the room, especially when I show them robots, they freak out typically, (John and Dave laugh) but the fact is well known. Leaders who will not embrace these skills, and understanding, and will help the organization to become agile, nimble, and data driven, will not survive. They will be replaced. So on the one hand, they're afraid from it. On the other side, they see that if they will not actually do something, and take an action today, they might be replaced in the future. >> Where should organizations start? Hey, I want to be data driven. Where do I start? >> That's a good question. So data science, machine learning, is a top down initiative. It requires a lot of funding. It requires a change in culture and habits. So it has to start from the top. The journey has to start from executive, from educating and executive about what is data science, what is machine learning, how to prioritize investments in this field, how to build data driven culture, right? When we spoke about data driven, we mainly speaks about the culture aspect here, not specifically about the technical side of it. So it has to come from the top, leaders have to incorporate it in the organization, the have to give authority and power for people, they have to put the funding at first, and then, this is how it's beautiful, that you actually see it trickles down to the organization when they have a very powerful CEO that makes a decision, and moves the organization quickly to become data driven, make executives look at data every time they make a decision, get them into the habit. When people look up to executives, they try to do the same, and if my boss is an example for me, someone who is looking at data every time he is making a decision, ask the right questions, know how to prioritize, set the right goals for me, this helps me, and helps the organization better perform. >> Follow the leader, right? >> Yup. >> Follow the leader. >> Yup, follow the leader. >> Thanks for being with us. >> Nir: Of course, it's my pleasure. >> Pinned this interesting love hate thing that we have going on. >> We should address that. >> Right, right. That's the next segment, how about that? >> Nir Kaldero from Galvanize joining us here live on The Cube. Back with more from New York in just a bit.

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