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

>> 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)

hey welcome back here ready Jeff Rick here with the key we're at the chief data scientist USA conference in downtown San Francisco and we're really excited to have a representative from IBM Sam Lightstone distinguished engineer from IBM join us Sam great to se you thank you very much pleasure to be here absolutely so we cover a ton of IBM events we're at world of Watson world lots of developer conference big the big event in New York earlier this year around strata so you know we're big fans of all the things that IBM is doing and in Rob Thomas and the SPARC group so I could go on and on but we won't go there we'll talk about what you were talking about earlier today and kind of let the cat out of the bag which is always exciting breaking news or breaking Bay there I don't know exactly how we would describe it but you talked about something new IBM data confluence yeah you could share this what's that all about yeah so it's a it's a whole new idea a whole new paradigm that were that we were incubating right now inside of IBM and it's not yet available but we're hoping to start trials in January ish timeframe but it comes from a realization that so much data is about to come upon us from distributed data sources you know everybody's got not only your cell phone but increasingly data is coming from Internet of Things you're gonna have data coming from your car data come from your glasses some smart meters on your house and it's deluge of data and the way that people like to do data science on this data today is they pull this data from these devices and put it into a central repository which is which is a perfectly legitimate strategy but it means that you're creating copies of the data and there's a certain complexity of dragging that data through the internet into some central repository so the idea that we had with data confluence is to leave the data where it is and create and allow the data all these different data sources if you can imagine cars you can imagine cell phones or smart meters on buildings allow them to find one another and collaborate on data science problems like a computational mesh so that we can bring hundreds thousands millions of microprocessors to bear on the data where it lives without moving it around and our theory is not only is that simpler for everyone because the data doesn't have to move around but we can actually bring more computation to bear because every one of those data sources has compute and has persistence and you can multiply the the opportunities right and you took a chance you ran a live demo which is you know always risky business at any anything but but there was a really interesting because concepts that you highlighted kind of organically forming adapting constellation right of these of these sources and the example you use they were solar panels but for them to do this kind of automatically if you will as opposed to someone going in and scripting and building the structure because tomorrow as you demonstrated in your demo you might want to add more or add more so exactly that dynamic functions are pretty pretty interesting yeah and it's a very powerful concept and a very necessary concept and the reason it's so necessary is these devices could be anywhere right and you could have most your devices in New York but a few of them in the Yukon or Alaska or something and you don't want them to all be equally connected right so it's important to be sensitive to create this network that is sort of geospatially aware and connectivity aware not not just sort of hard-coded you know so that so one aspect of that is to be sensitive to network latency and topology that's one reason why it has to be automatic the other reason has to be automatic is if you really want this to scale to thousands of devices you can't have some programmer trying to figure out who connects to what right it's just too hard right so making it really adaptive and automatic is super important another thing that's really important for the Internet of Things is depending on the on the circumstance but if you can imagine cell phones for example you can have a network of thousands millions of phones but at any point in time somebody some of those funds are gonna be turned off so the network has to be adaptive to the possibility that devices go offline right are there intentionally like a phone perhaps unintentionally because they break you know if you have a device on a smart meter it may simply break and then that particular device is offline for a period of time right so the network has to be resilient to that and that's part of what we've been building in particular using technology that we incubated in our UK labs in Hursley so it's it's been a great collaboration across IBM this is not just you know one you know one set of people in one lab but actually a corporate collaboration and really our goal is to make this as you say automatic but I would I would say beyond automatic to make it resilient right there's got to be resilient and fault tolerant because the complexities that we could be dealing with are just too large for human being to deal with right and clearly and distributed right that's the big thing guys we're leveraging IBM bluemix cloud you know all this stuff doesn't happen with with cloud capabilities and the demo you did here you were here the data center was concerned San Jose and the actual data elements were in in Toronto so just you know Amazon and Microsoft and Google are always you know get talked about a lot it within the cloud space but really iBM is making major players and it if not in that top three certainly right there in the fourth position as a leader in cloud and then what this cloud enables and then really kind of with the whole cognitive push you know that's a priority for Ginni and the team to really bring more intelligence he's exactly right and what data confluence you know what we're hoping not only to tap in to data science on distributed systems for IOT and also for enterprise use cases as well but really to take it to the next level of hybrid cloud because these data sources could be in the cloud and they could be on-premises they could be anywhere in the world and you can mix and match and that's really a very powerful capability for our customers many companies now struggling as their data is now part cloud and part on-premises right and in the compute as well right you could deal shift exactly compute from the edge to the cloud you know a dynamic fashion based on what the kind of optimal solution is or as you said sometimes over the edges off lined and you can't do it there it's exactly right so kind of a cool story you said this came out of a out of something called blue unicorn what is blue you know fantastic so blue unicorn was an initiative that a few of us got together on inside of IBM you probably know some of these folks Rob Thomas so I think you've interviewed gears from Karachay Leah and myself and the three of us got together and we said you know we want to find a more effective way to tap in to the creative juices of our staff we got some of the greatest minds in the world working at IBM we hire brilliant people PhDs masters of the top schools all over the world and all too often we hire these people and we tell them what they should be working on that wouldn't it be better if we could find a repeatable process for them to come to us and say here's the next big innovation that IBM ssin should have and blue unicorn came out of that desire to tap into and and nurture this creative passion of of our staff and was really designed almost like an internal VC initiative so people would would come to us with proposals and we've got those proposals we start out with hundreds and feted it down to dozens that down to just a small few that we would fund from the ones that we funded you know that would go through periodic reviews until eventually we ended up with a very small set that are still being incubated and and did a confluence happen to have been one of those projects awesome so it's different than kind of the 10% thing this is actually almost like an internal you you put your proposal together you pitch it whereas if it was an internal VC you get funded and then you go do that with your team right one thing I would say is one of the you know as we're setting up we're trying to find ways to make it work make it efficient one of the best filtering factors that we came up with is that people had to show us running code before it was funded right right and that was amazing because that meant people had to work nights and weekends they had to have that level of passion and commitment for their idea to get to that level of vetting and that was incredible that that definitely filtered the people who were super passionate about what they were doing and the people just said yeah I'd like to tinker and that was tremendous okay and then you're here at the show melting a small show tight group kind of multi industry any good takeaway surprises from the last couple days here at the chief data science USA show you know it's been an amazing conference actually and some great speakers some great insights I think one of the most useful insights for me was was I was curious to hear from this audience what is the duration of data that is important to them do they need to see data from the last hour the last month the last year the last 10 years and of course it does vary from problem to problem but many people said you know for the work that I do I need about three months to build a model and then once I have a model I'm really looking at the last two to four weeks of data to gain data science insight and that was a very important point for me especially as we continue on our work on analytics data science and IBM it's very important for us to understand the range of data that that people are using shorter than you seem sure yeah it's shorter because I know certainly in the data warehousing space that I've been working a lot of my career in people do data analytics on you know six months a year or three years right so this is this is it definitely is somewhat of a shift and it tells us something about our society that things are moving faster and the idea that's older than six months is is usually not as interesting anymore yeah really shows kind of the dynamic real-time nature it's not this is analyzing just the old stuff is interesting but not nearly as interesting as being on top of where's the spark stream somebody's other thing is funny Beth Comstock kicked off the GU minds and machines event a couple days ago she said we even walk faster in cities they've done so everything is continuing to speed up right all right so you're from now you're back here what are we gonna be talking about Wow okay well you know we just launched a few months or a few weeks ago actually the the Watson Data Platform a huge event for us and it really is for us the foundation the data foundation of all the cognitive computing that we're that IBM is coming out with it's gonna bring together data science and data storage and collaboration across you know amongst analysts and data scientists together all all one platform for all your data needs I'm hoping that a year from now I'm going to speak to you about how data confluence is a core part of that of that platform and we're gonna be raeng analytics on millions of devices all over the world all right Sam well thanks for taking a few minutes I know you gotta go catch an airplane for stopping by and sharing your insight thank you all right Sam lights on I'm Jeff Creek you're watching the cube thanks for watching