 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 and we've heard a lot, seen a lot, we've talked to a lot of folks today. You, first off, tell me, AI, we've heard some optimistic outlooks, I wouldn't say pessimistic, but some folks saying, hold off, not as daunting as some might think. So just your take on the artificial intelligence conversation we've heard so far today. Yeah, I think generally, John, that people don't realize what's coming. I think the industry in general, our industry, technology industry, sorry, 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 sort of 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. I mean, as having said that, you know, 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, that 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. I mean, 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 in, 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. But think about the fact that they can do that as a function of that they are digital first company that 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, you know, is not so great. Alexa's become her 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's and Elon Musk say, hey, we should be concerned. Maybe they're right, not the next 10 years. But, and that's what, well, we mentioned Jennifer, we were talking about her in the influencer panel, where, you know, and we've heard from others as well. It's a combination of human intelligence and artificial intelligence. That combination is 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, you know, maybe not the case, maybe not necessary is what you're thinking. Yeah, I mean, you know, I guess at the end of the day, the question is, is the world going to be a better place with all this AI? And are we going to be more prosperous, more productive, healthier, safer on the roads? I am an optimist. I come down to the side of, yes, I would not want to go back to the days where I didn't have GPS. Can you imagine, right? If you did that now, you go back five years, just five years, you know, from where we are now, back to where we were. I mean, Waze was nowhere. All the downside of these things, I feel is offset by that. And I do think it's coming upon the industry to try to, you know, deal with the problem, especially with young people, you know, the blue light problem. The addictive issue. Yeah, 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, you know, humans and machines are going to continue to coexist, at least in the near to mid to 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 that, the answer to that changes every year. It's like I said earlier, not too long ago, machines couldn't climb stairs, right? They can now, robots can climb stairs. Can they negotiate? Can they, well, look, can they identify cats? Yeah, who would have imagined that all these cats on the internet would have 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 understanding multidisciplinary, 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 sort of two-tool star that I think has got 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, health care. I mean, can't we solve the HIPAA challenge? We can't get access to our own health care information. Well, things like artificial intelligence and blockchain, we're talking off the camera about blockchain, those things I think can help solve the challenge of maybe I can carry around my health profile, my medical records. You know, I don't have access to them. I mean, it's hard to get them, right? So can things like artificial intelligence improve our lives? I think, you know, there's no question about it. What about, well, 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, a lot of, 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? Yeah, I think you have to, there's no question that's gonna happen. And it has to be managed. But even if you could stop it, I would say you shouldn't because the benefits are gonna 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? Now we're not even close to the should we go yet. We're still in the how far can we go? You know, Jennifer was pointing out is, you know, I can't get my password reset because I gotta 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. More so. There's still 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 a credit card fraud and things of that nature. Autonomous vehicles are just fascinating, right? I mean, both culturally, we talked about that. You know, we learned how to drive a stick shift. Right, so phone story. Not gonna worry about that anymore. But it was an exciting time in our lives. So there's a cultural downside of that. But I don't know what the highway death toll number is, but it's enormous if cell phones cause that many deaths, we wouldn't be using them. So that's a problem that I think, you know, things like artificial intelligence and machine intelligence can solve. And then the big other thing, 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? Google, 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, you know, et cetera, et cetera. So 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 sort of 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. It was very important. So there's a giant skills gap in existing companies. There's data silos. The other thing we touched on this was 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, you know, 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 Michelev, 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 you're the disruptor or the disruptee, it's not just, I mean, we talked about this at the open, cloud, SaaS, mobile, social, big data. It kind of yesterday's news, all right? It's now, new artificial intelligence, machine intelligence, deep learning, machine learning, cognitive, we're still trying to figure out the parlance. So it's, you can feel the changes coming. I think there's, 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 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 is in cybersecurity are just coming together, right? 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. All right, so let's just switch over to kind of the currency of AI data that and we've talked to like Sam Lightstone today was talking about the database querying that he's able 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 as human expertise with data sort of bolted on or data in silos. What was interesting about query Plex I think they called it is essentially virtualizes the data, 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. The data is the core. Now what I don't know, and I don't know and I've just heard about it today and I missed that announcement, I think it was the announcement a year ago. Is it, he mentioned DB2, he mentioned Neteza. Most of the world is not on DB2 and Neteza even though IBM customers are. I think you 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 is the great thing about standards. There are a lot of them. There's always another one that you can pick if this one fails, right about that. But so that was very interesting. And so this is, Ben, the question, can traditional companies close that machine learning, machine intelligence AI gap? 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 Airbnb's and the Uber's, right? Again, blockchain comes in and you say, well, why should I, why do I need a trusted third party called Uber, right? Why can't I do this on the blockchain? I predict you're gonna 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 open source 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. And is it possible that open source software, AI, cryptography, all this activity could challenge the status quo? I've been in this business as long as I have, things never stay the same. Leaders come, leaders go. Right, let's say never, say never, right, you don't know. So 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. I think he was thinking, I was saying, you're going to get disrupted by all these little guys. IBM has been getting disrupted for years. They know how to sort of reinvent and a lot of people criticize IBM, how many quarters they haven't had growth, blah, blah, blah. But IBM has made some big, big bets on the future of 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 are the best AI, IBM is not there with them. But think about what IBM is trying to do versus what Google's doing. It was very consumer oriented, solving consumer problems. Consumers have really led the last, the consumerization of IT, that's true. But none of those guys are trying to solve cancer, right? So IBM is talking about some big, hairy, audacious goals. And I'm not as pessimistic as some others as you've seen in the trade press, it's popular to do. But so bringing it back to IBM, I saw IBM is trying to disrupt itself. The challenge IBM has is it's got, it's just got a lot of legacy software products that it 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 the leader, they 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. See, 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's going to be really interesting to see what happens. I mean, I don't know, you're down in DC. Jeff Bezos spent a lot of time there. We just want the headquarters. That's all we want. We just want the headquarters. Well, to the point. I mean, if you've got such a growing company and monopoly, maybe you should set up an HQ2 in DC. There's no, yeah. He said something the other day. Three of the 20, right? Yeah, he was saying the other day that maybe we should think about enhancing, he didn't call it social security, but sort of 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? Right. So that, if I'm a customer of Amazons, 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, Ginny Rometti 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. Yeah, I think. Three days. Third week in March, Monday, Tuesday, Wednesday, the Cube's going to be there. This week we're at the, in the Bahamas this week, actually. Not as a group taking vacation, actually a working expedition. No, we just got a blockchain conference, actually it was this week. What am I saying next week? Although I'm happy to volunteer to grip on that. It should, by the way. Just flying out tomorrow. All right, good. It's happening fast. Well, enjoy this. Always good to spend time with you and to spend time with you as well. So you've been watching the Cube. Machine learning everywhere, build your ladder to AI. Brought to you by IBM. Have a good one.