 Live from New York, it's theCUBE, covering machine learning everywhere. Build your ladder to AI, brought to you by IBM. Well, good morning, welcome here on theCUBE. Along with Dave Vellante, I'm John Walls. We're in Midtown, New York for IBM's machine learning everywhere. Build your ladder to AI. Great lineup of guests we have for you today. Looking forward to bringing them to you, including world champion, chess master, Gary Kasparov, a little bit later on, it's gonna be fascinating. Dave, glad you're here. Dave, good to see you, sir. John, I was a pleasure. How you been? Up from DC, yeah, I was in your area last week doing some stuff with John Furrier, but I've been great. Stop by the White House, you drop in. You know, I didn't this time. No? No, my son, as you know, goes to school down there. So when I go to my hotel, I was walked by the White House, I waved. Just in case, right? I don't get, no reciprocity. Yeah, same deal, we're in the same boat. Let's talk about what we have coming up here today. We're talking about this digital transformation that's going on within multiple industries, but you kind of have an interesting take on it that it's a different wave, and it's a bigger wave, and it's an exciting wave right now that digital was creating. Well, look at me, I've been around for a long time. I think we're entering a new era. You know, the great thing about theCUBE is you go to all these events, you hear the innovations, and we started theCUBE in 2010, and the big data theme was just coming in, and it appeared, you know, everybody was very excited, still excited obviously about the data-driven concept, but we're now entering a new era, and it's like every 10 years, the parlance in our industry changes, right? It was cloud, big data, SaaS, mobile, social. It just feels like, okay, we're here. We're doing that now. That's sort of a daily ritual. We used to talk about how it's early innings. It's not anymore, it's late innings for those, and I think the industry is changing. I mean, the describers of what we're entering are autonomous, you know, pervasive, self-healing, intelligent. You know, when you infuse artificial intelligence, I'm not crazy about that name, but when you infuse that throughout the landscape, things start to change. Data is at the center of it, but I think, John, we're going to see the parlance change. IBM, for example, uses cognitive. People use artificial intelligence. I like machine intelligence. So we're trying to still figure out the names. To me, it's an indicator that things are changing. It's early innings now. And so what we're seeing is a whole new set of opportunities emerging. And if you think about it, it's based on this notion of digital services where data is at the center. So that's something that I want to poke at with the folks at IBM and our guests today. How are people going to build sort of new companies? You're certainly seeing it with the likes of Uber, Airbnb, Waze. It's built on these existing cloud and security, off the shelf, if you will, horizontal technologies. How are new companies going to be built? What industries are going to be disruptive? Hint, every industry? But really the key is how will existing companies keep pace? That's what I really want to understand today. Yeah, so you said every industry is going to be disrupted, which is certainly, I think, an exciting prospect in some respects, but a little scary to some too, right? Because they think, no, we're fat and happy and things are going well right now in our space and we know our space better than anybody. Some of those leaders might be thinking that. But as you point out, digital technology has transformed to the extent now that there's nobody safe because you just slap this application in, you put this technology in, and I'm going to change your business overnight. Yeah, so it's right. So digital means data. Data is at the center of this transformation. A colleague of mine, David Michela, has come up with this concept of the matrix. And what the matrix is is a set of horizontal technology services. Think about cloud or SaaS or security or mobile, social, all the way up the stack through data services. But when you look at the companies like Airbnb and Uber and certainly what Google is doing in Facebook and others, they're building services on top of this matrix. And the matrix is comprised of vertical slices by industry and horizontal slices of technology. And disruptors are cobbling together through software and data, new sets of services that are disrupting industries. The key to this, John, in my view anyway, is that historically within healthcare or financial services or insurance or manufacturing or education, those were very siloed. But digital and data allows companies and disruptors to traverse silos like never before. Think about it, Amazon buying Whole Foods, Apple getting into healthcare and financial services. So you're seeing these big giants disrupt all of these different industries and even smaller guys, there's certainly room for startups, but it's all around the data and the digital transformation. You spoke about traditional companies needing to convert, right? Needing to get caught up perhaps or to catch up with what's going on in that space. What do you do with your workforce in that case? You got a bunch of great, hardworking people, embedded legacy, you're filled good about where you are and now you're coming to that workforce and saying, here's a new hat. Yeah, I think it's a great question. I think the concern that one would have for traditional companies is data is not foundational for most companies, it's not at their core. The vast majority of companies at the core are the people, you hear it all the time, the people are our greatest asset. That, I hate to say it, but it's somewhat changing. If you look at the top five companies by market cap, their greatest asset is their data and the people are surrounding that data. They're very, very important because they know how to leverage that data, but if you look at most traditional companies, people are at their core, data is kind of, oh, we got this bolt on or it's in a bunch of different silos. So the big question is how do they close that gap? And you're absolutely right, the key is skill sets and the skills have to be, we talk about five tool baseball players, you're a baseball fan, as am I. Well, you need multi-tool players, those that understand not only the domain of whether it's marketing or sales or operational expertise or finance, but they also require digital expertise. They know, for example, if you're a marketing professional, they know how to do hyper-targeting, they know how to leverage social, they know how to do SEO, all these digital skills and they know how to get information that's relevant and messaging out into the marketplace and permeate that. And so we're entering, again, this whole new world that's highly scalable, highly intelligent, pervasive, autonomous. We're going to talk about that today with a lot of our guests that really are kind of futurist and have thought through, I think, the changes that are coming. Yes, you can't have a DH anymore, right? That's what you're saying. You need a guy that can play the field. Yeah, not only play the field, not only a utility player, but somebody who's a utility player and, but great, best of breed at all these different skill sets. Well, about machine learning, we haven't talked much about that. And another term, right, that certainly has different definitions, but certainly really specific applications to what's going on today. We'll talk a lot about ML today. Your thoughts about that and how that squares into the artificial intelligence picture and what we're doing with all those machines out there that are churning 24 seven. Yeah, so real quick, I don't want to turn on time here. So artificial intelligence, to me, is the umbrella. Machine learning is the application of math and algorithms to solve a particular problem or answer a particular question. And then there's deep learning, which is highly focused neural networks that go deeper and deeper and deeper and become autodidactic, self-learning in a manner. So those are some of this, a very quick and rudimentary description. Machine learning, to me, is the starting point. And that's really where organizations really want to start to learn and begin to close the gap. Well, a lot of ground to cover, and we're going to do that for you right here on theCUBE as we continue our coverage of machine learning everywhere, your ladder to AI coming up here, IBM, hosting us in Midtown New York back with more here on theCUBE in just a bit. All right, that's it. Let's get started.