>> Announcer: Live, from Las Vegas, it's the Cube. Covering I.B.M. Think 2018. Brought to you by I.B.M. >> Welcome back to the Cube. We are live at I.B.M. Think 2018, our inaugural event. I'm Lisa Martin with Dave Vellante. We're joined by another Vegas veteran, as we all are. First time guest to the Cube, Bret Greenstein, the V.P. of Watson I.o.T. Offerings. Bret, welcome to the Cube. >> Thank you very much, exciting to be here. >> This is the inaugural Think 2018 event. >> Yes. >> 40,000 plus attendees, expected over 10 keynotes, lots of cool stuff. Speaking of cool stuff, I.o.T. What is happening in I.o.T. this year? >> Yeah, so we've been here in Vegas several times over the last several years talking about the Internet of Things, but what's really pivoted, what's really changed, is people talking about applied I.o.T. How are they using it to get business outcomes. Something different happening. And I think when we all started with the Internet of Things we talked a lot about, connecting stuff and devices. But really, it was always about the data and the effect that data had on changing business, changing user engagement, changing outcomes. And so here, on stage, you're going to see people talking about how their businesses have been changed, how their customers are changing as a result of I.o.T. >> Yeah so, I've always felt like I.o.T. is the intersection of devices, data, and machine intelligence. >> Bret: Yeah. >> How are those sort of three things coming together and what's the data model look like? >> Data model is every type of data. I think what people really didn't expect was it wasn't just machine data coming off sensors, temperatures, vibrations. It's all this unstructured data coming in from connected things that are everywhere in our lives. So sensors with cameras for example, being able to see. It's not just recorded images, but it's information. Tons of information that you need A.I. systems and other systems to interpret. So we're able to take all that data, structured data, numeric stuff coming off of devices and sensors, but images and sound and vibration. Even emotional content in people's dialogue. All of that is relevant to the Internet of Things. >> What's the conversation like with customers? For example, when we say, what physical assets do we have that we can instrument. >> Bret: Right. >> Parking meters or whatever, okay. >> Bret: Right. >> What physical assets don't we have that we should have? How can we leverage our existing data? What's the conversation like in terms of transformations that are going on? >> I think the conversations have shifted a lot. Over the couple years people were talking about we want to connect our thing, whatever the thing is, whether it's an elevator or car or whatever. We want to connect it, what does that mean? And that's shifted very quickly to customers who are coming in talking about information data and insights and they want to know, what should I do to get more of those insights? So I'm seeing customers now with Chief Data Officers or heads of digital transformation. Totally new roles that didn't exist before. And they're coming in with a data centric view. They're saying, we're going to be a digital business. We need to understand all of these live data about our customers and our things and our business process. Help us do that. And so that's much more than just instrumenting the individual devices now. And I find that conversation is really, really focused on the value of the data. >> What about the industry impact in this context? Do you see, does I.B.M.'s perspective, is I.o.T., it's certainly transformative. >> Bret: Right. >> But is it disruptive or it is sort of the guys with infrastructure are going to evolve to it? Is it more evolutionary, is it more disruptive? How do you see it? >> I think there's room for both. Obviously traditional players are going to instrument their business process. They're bringing in connected cars and all that. But you could also look at those same industries and say there's new players emerging who are coming in with software defined products that are digital by design. And they can come in and suddenly become leaders in their field. I don't think people would've expected companies like Tesla to be so disruptive in automotive, but coming in as electric changes the game without having to build on a hundred years of mechanical design. You're building on some new principles. And now we see some new players coming in to automotive who've never built cars at all before. Like Dyson for example, that recently announced they were working on electric cars. So I think a digital platform, a digital way of thinking, also creates opportunities for new entrance in every market. >> I think automobiles is a great example because it's an industry that hasn't been largely disrupted. But then you use an example of Tesla which is extremely innovative, you could actually pretend disruptions coming out. And you see whole ecosystems form around that. >> Right, right. And I think what was so powerful about the effect they had was it's a software defined product. The software in it is upgraded constantly. Sometimes you buy the car, the next day you get a new feature you didn't even expect. And this is the way we've come to appreciate, experience through mobile and everything else. Software that continues to improve products that get more valuable over time. Not less valuable over time. >> So let's talk about Watson and I.o.T. I'd also love to maybe take a slice on how I.B.M. is helping customers that maybe have been around maybe the flip side of a Tesla. They've been around a long time. How are they leveraging Watson and I.o.T. to transform their businesses? So kind of start with, what's new with Watson and I.o.T. >> Sure, so I mentioned before that there's a whole part of many data types now that previously were very hard to interpret through traditional analytics. But A.I. and machine learning give you the ability to absorb and consume some of that data. Unstructured sound, images, video, vibration, all of that stuff is now able to become part of a business process. So even traditional companies that have been around a long time can start to look at the data coming off of cameras, visual inspection in manufacturing, sound and voice for example. We work with Jefferson Hospital where they brought Watson into patients rooms so you could ask questions like visiting hours, or set the temperature. Put the patients in control of their experience in a hospital. That takes a traditional experience, like a hospital recovery room, and turns it into something A.I. driven, I.o.T. powered and puts the patient at the center. So very big changes can occur when you do that. >> How far do you see us being able to take A.I. in this whole world of I.o.T.? How far should we take it? >> I think we have to start become more appreciative of the power of machine learning to drive outcomes that are not as easily prescribed with code. So all of us, all of our business processes, all of our businesses will be enhanced with A.I. And we shouldn't look at that in any other way as a better tool to understand data in a way that's different than the way you interpret data. And so it wasn't long ago when big data just meant writing an algorithm across large volumes of data. And now we literally have algorithms whose job is to find patterns. Whose job is to understand data from training. And deliver an outcome that you couldn't have prescribed before. And so those type of problems, it just opens up a class of problems we can all solve now that we couldn't before. >> You're seeing a whole set of digital services emerge. The lingua franca is changing. It's sense, hear, see, respond. >> Bret: Right. >> Optimize. >> Right. >> Fix. (chuckles) >> And all that comes from comprehending. So having a system that can look. For example, I have a camera outside the window of my house and every once in a while I feed the images into Watson to see what it sees. When I first did it, it would say truck. But later, as we make Watson better, now it says FedEx truck or U.P.S. truck. It can read the writing, it can see the patterns. Every camera should know what it sees. Whether it's in a car or a home or somewhere else. Because it's much more valuable than just taking a picture and letting a human being interpret it later. So cameras should know what they see. Machines should know what they hear. Machines should tell us when they're about to break based on vibration or sound. And so this is possible with machine learning. >> So you're saying machines actually take on a whole new set of human-like activities. Digital twins is an example. >> Bret: Okay. >> What's your perspective on, let's start there, digital twins? >> Digital twins, for me, represents sort of the evolution of I.o.T. and that it's digitalizing things. And so, a thing that has no connectivity and very few sensors, is just a thing, it's just a box, it's a block. But as you start to put sensors on it and start to understand it's behavior, it's motion, it's vibration, it's location. Any of the mechanisms, the angels, all this stuff. Then you add a virtual representation of that thing. And if you can do that with all the things in your business, you can start to look for patterns. You can start to assess what's working and what's not working. So I think it just represents a true digitization of a business, of a class of objects in your business. >> Does I.o.T. make security a do-over in your opinion? >> No, but it certainly raises the bar. And so, when we all started connecting our computers to the internet, I remember everyone being panicked. It you put a disc in your machine, you might get a virus. Then we connected them to the internet, we all panicked, but the tools evolved and we start to get things that can help detect zero day problems. In the case of I.o.T. we've got these software defined products that are connected. That are inherently vulnerable cause they're in the real world. They can be touched by other things. So it raises the bar in the expectation of monitoring normal behavior for things. Monitoring all kinds of different threats and stuff, So companies like I.B.M. they focus so much on security and security services, we build that right into our platform so we can keep an eye on that. And also, when things occur, be able to push out new software that is protected. So for more updates, keeping the products live and current is a huge security protection. >> Bret, how would you describe the ecosystem. I.B.M.'s point of view on the ecosystem that you've got to form and catalog in order to succeed in I.o.T.? What does that look like? >> Yeah so, there are so many things for people to do in the world of I.o.T. That I.B.M. doesn't prescribe to do all of them, at all. There's certain things that we're really, really good at. We're certainly good at our cloud infrastructure and analytics and the platforms that enable this and deep industry knowledge. But the ability to apply that in businesses, to take on machine learning algorithms and make it work on the thousands of classes of machines in manufacturing, requires a huge partner ecosystem. So we work very openly on contributions to standards and open source. We certainly work with partners to build a lot of value around our stuff. So for example, on stage this week, we have several partners who are going to be up there. One of them is Harmen, who builds all kinds of things that's including info-tainment units in cars and the professional equipment that goes into hotels and buildings. So we work with them to build great innovative value together and they do things that they're experts in and we do what we're experts in. >> So, from an I.o.T. perspective, what are some of the cool things that are here at I.B.M. Think 2018, that those that are attending are going to get to see and feel and touch and smell? >> Well there are some things I can talk about, things that I can't. Tomorrow we have some very exciting announcements coming up. Going to talk a lot more about Watson and I.o.T. coming together, that's all I can say about that. You'll also see physical representations of things. There's a Jaguar Land Rover out here on the floor. To look at where we have contributed significantly to the engineering and the software development inside these kinds of products like J.L.R. So they're going to be up on stage talking about some of the things we're doing together. You'll hear A.B.B. here talking about some of the work we're doing around manufacturing techniques and helping manage wind turbines. So all kinds of really cool, industrial use cases. It's really exciting and I think working in I.o.T. is great because not only do you get to talk about the technology and the analytics and the data, but you actually get to see things. So it makes all of this feel very real when you walk up to and see a thing that's infused with I.o.T. and made better because of I.B.M. >> What inning are we in? >> What's that? >> What inning are we in? >> Oh it's still early, still early. Third inning still, mostly because so much of the market is still working to figure out how to take advantage of the data and the insights about this to transform their business. I think if you thought of the dot com era and how long it took for companies to emerge to be truly digital e-businesses, on demand businesses. The I.o.T. businesses, the A.I. driven businesses of the future, still very early. Some of them, you probably don't even know their names yet. But they're going to be the leaders that's coming. >> Do you think it'll happen faster because there is an internet? Or not so much because of the physical infrastructure that has to get built out? >> The infrastructure is actually not the gate at all. >> Dave: Okay. >> The real gate is the cultural difference of having people who are data driven, data thinkers. Having a leadership role in our clients. If you can think about it, mechanical things have dominated for a hundred years. Software engineers are still not even the most senior people in most of the companies that build physical things. But to have the data scientists, have the data leaders have a strong enough role to define business process. It's really the readiness and maturity of those data leaders. >> Yeah so the culture of a mechanical engineering culture that says "don't touch my things," >> Right. >> I'm not going to let a software engineer come in and mess with it because it works, it's secure, I trust it. >> Right. >> So that's the cultural one of the cultural dimensions. >> It's to look at what the data might mean. Just understand how your users use your things or if you want to understand what they're doing with those things somewhere else. Or even with the value of your insights of your users are and building entirely new ecosystems of the data of I.o.T. >> Alright, so we're in the third inning. We'll say the top of the third. >> Okay. >> But one of the things that you shared with us is that you're excited about is this is about applied I.o.T. To get business outcomes. >> Yes. >> Shared some examples that attendees of the event are going to hear from A.B.B., you mentioned, you mentioned the >> Bret: J.L.R. >> Land Rover that's here. Harman as well. And maybe some best practices for how to advise companies to get through some of those cultural hurdles, we'll say, to start embracing the opportunities that are within the I.o.T. space. >> I think the best thing people could do is to start to really, I'm going to say it again, put value on data science. It doesn't mean everyone has to be a data geek. But it does mean you have to have a certain value on the skills and the insights that come from a data driven business. What does it mean to make decisions in real time based on your customers? For a hundred years when companies shipped a washing machine it went into someone's house and sat for 10 years and they never heard from the person ever again until they bought another one 10 years later. But now when you ship a washing machine, you want people to connect it to the wi-fi. You want to know the features that are used. Suddenly as a manufacturer of things, you have to respect the data coming off those things because they inform you on how to design better. How to deliver better service and value. Which means those engineers who were the experts in washing machines, now have to be the experts in the data of washing machines and the data of their users. So, I would say, focus on the education, the recruitment, the enablement, the empowerment of people who are data centric by nature and who are looking for the transformation of a digital business from a physical business. >> Awesome, Bret thank you so much for stopping by the Cube and sharing your insights. >> You're very welcome. >> Good luck tomorrow with your presentations and we are going to be waiting on the edge of our seats for those lots of I.o.T. announcements. >> Very exciting. >> Very exciting. >> Okay. >> Alright you heard it here. >> Thank you so much. >> You can watch all of our good stuff on thecube.net live, of course, as we are now as well as the interviews that we've already done and those that we'll be doing for the next two days as our coverage continues of I.B.M. Think 2018. Also check out siliconangle.com our media site for all of your real time coverage of this event and others. For Dave Vellante and Bret, two Vegas Veterans, I'm Lisa Martin. Stick around, Dave and I are going to be right back after a short break. (upbeat music)