>> [Narrator] Live from New York, it's theCube, covering Machine Learning Everywhere. Build your ladder to AI, brought to you by IBM. >> Welcome back here to New York City as we continue at IBM's Machine Learning Everywhere, build your ladder to AI, along with Dave Vellante, I'm John Walls. It is now a great honor of ours to have I think probably and arguably the greatest chess player of all time, Garry Kasparov now joins us. He's currently the chairman of the Human Rights Foundation, political activist in Russia as well some time ago. Thank you for joining us, we really appreciate the time, sir. >> Thank you for inviting me. >> We've been looking forward to this. Let's just, if you would, set the stage for us. Artificial Intelligence obviously quite a hot topic. The maybe not conflict, the complementary nature of human intelligence. There are people on both sides of the camp. But you see them as being very complementary to one another. >> I think that's natural development in this industry that will bring together humans and machines. Because this collaboration will produce the best results. Our abilities are complementary. The humans will bring creativity and intuition and other typical human qualities like human judgment and strategic vision while machines will add calculation, memory, and many other abilities that they have been acquiring quickly. >> So there's room for both, right? >> Yes, I think it's inevitable because no machine will ever reach 100% perfection. Machines will be coming closer and closer, 90%, 92, 94, 95. But there's still room for humans because at the end of the day even with this massive power you have guide it. You have to evaluate the results and at the end of the day the machine will never understand when it reaches the territory of diminishing returns. It's very important for humans actually to identify. So what is the task? I think it's a mistake that is made by many pundits that they automatically transfer the machine's expertise for the closed systems into the open-ended systems. Because in every closed system, whether it's the game of chess, the game of gall, video games like daughter, or anything else where humans already define the parameters of the problem, machines will perform phenomenally. But if it's an open-ended system then machine will never identify what is the sort of the right question to be asked. >> Don't hate me for this question, but it's been reported, now I don't know if it's true or not, that at one point you said that you would never lose to a machine. My question is how capable can we make machines? First of all, is that true? Did you maybe underestimate the power of computers? How capable to you think we can actually make machines? >> Look, in the 80s when the question was asked I was much more optimistic because we saw very little at that time from machines that could make me, world champion at the time, worry about machines' capability of defeating me in the real chess game. I underestimated the pace it was developing. I could see something was happening, was cooking, but I thought it would take longer for machines to catch up. As I said in my talk here is that we should simply recognize the fact that everything we do while knowing how we do that, machines will do better. Any particular task that human perform, machine will eventually surpass us. >> What I love about your story, I was telling you off-camera about when we had Erik Brynjolfsson and Andrew McAfee on, you're the opposite of Samuel P. Langley to me. You know who Samuel P. Langley is? >> No, please. >> Samuel P. Langley, do you know who Samuel P. Langley is? He was the gentleman that, you guys will love this, that the government paid. I think it was $50,000 at the time, to create a flying machine. But the Wright Brothers beat him to it, so what did Samuel P. Langley do after the Wright Brothers succeeded? He quit. But after you lost to the machine you said you know what? I can beat the machine with other humans, and created what is now the best chess player in the world, is my understanding. It's not a machine, but it's a combination of machines and humans. Is that accurate? >> Yes, in chess actually, we could demonstrate how the collaboration can work. Now in many areas people rely on the lessons that have been revealed, learned from what I call advanced chess. That in this team, human plus machine, the most important element of success is not the strengths of the human expert. It's not the speed of the machine, but it's a process. It's an interface, so how you actually make them work together. In the future I think that will be the key of success because we have very powerful machine, those AIs, intelligent algorithms. All of them will require very special treatment. That's why also I use this analogy with the right fuel for Ferrari. We will have expert operators, I call them the shepherds, that will have to know exactly what are the requirements of this machine or that machine, or that group of algorithms to guarantee that we'll be able by our human input to compensate for their deficiencies. Not the other way around. >> What let you to that response? Was it your competitiveness? Was it your vision of machines and humans working together? >> I thought I could last longer as the undefeated world champion. Ironically, 1997 when you just look at the game and the quality of the game and try to evaluate the Deep Blue real strengths, I think I was objective, I was stronger. Because today you can analyze these games with much more powerful computers. I mean any chess app on your laptop. I mean you cannot really compare with Deep Blue. That's natural progress. But as I said, it's not about solving the game, it's not about objective strengths. It's about your ability to actually perform at the board. I just realized while we could compete with machines for few more years, and that's great, it did take place. I played two more matches in 2003 with German program. Not as publicized as IBM match. Both ended as a tie and I think they were probably stronger than Deep Blue, but I knew it would just be over, maybe a decade. How can we make chess relevant? For me it was very natural. I could see this immense power of calculations, brute force. On the other side I could see us having qualities that machines will never acquire. How about bringing together and using chess as a laboratory to find the most productive ways for human-machine collaboration? >> What was the difference in, I guess, processing power basically, or processing capabilities? You played the match, this is 1997. You played the match on standard time controls which allow you or a player a certain amount of time. How much time did Deep Blue, did the machine take? Or did it take its full time to make considerations as opposed to what you exercised? >> Well it's the standard time control. I think you should explain to your audience at that time it was seven hours game. It's what we call classical chess. We have rapid chess that is under one hour. Then you have blitz chess which is five to ten minutes. That was a normal time control. It's worth mentioning that other computers they were beating human players, myself included, in blitz chess. In the very fast chess. We still thought that more time was more time we could have sort of a bigger comfort zone just to contemplate the machine's plans and actually to create real problems that machine would not be able to solve. Again, more time helps humans but at the end of the day it's still about your ability not to crack under pressure because there's so many things that could take you off your balance, and machine doesn't care about it. At the end of the day machine has a steady hand, and steady hand wins. >> Emotion doesn't come into play. >> It's not about apps and strength, but it's about guaranteeing that it will play at a certain level for the entire game. While human game maybe at one point it could go a bit higher. But at the end of the day when you look at average it's still lower. I played many world championship matches and I analyze the games, games played at the highest level. I can tell you that even the best games played by humans at the highest level, they include not necessarily big mistakes, but inaccuracies that are irrelevant when humans facing humans because I make a mistake, tiny mistake, then I can expect you to return the favor. Against the machine it's just that's it. Humans cannot play at the same level throughout the whole game. The concentration, the vigilance are now required when humans face humans. Psychologically when you have a strong machine, machine's good enough to play with a steady hand, the game's over. >> I want to point out too, just so we get the record straight for people who might not be intimately familiar with your record, you were ranked number one in the world from 1986 to 2005 for all but three months. Three months, that's three decades. >> Two decades. >> Well 80s, 90s, and naughts, I'll give you that. (laughing) That's unheard of, that's phenomenal. >> Just going back to your previous question about why I just look for some new form of chess. It's one of the key lessons I learned from my childhood thanks to my mother who spent her live just helping me to become who I am, who I was after my father died when I was seven. It's about always trying to make the difference. It's not just about winning, it's about making a difference. It led me to kind of a new motto in my professional life. That is it's all about my own quality of the game. As long as I'm challenging my own excellence I will never be short of opponents. For me the defeat was just a kick, a push. So let's come up with something new. Let's find a new challenge. Let's find a way to turn this defeat, the lessons from this defeat into something more practical. >> Love it, I mean I think in your book I think, was it John Henry, the famous example. (all men speaking at once) >> He won, but he lost. >> Motivation wasn't competition, it was advancing society and creativity, so I love it. Another thing I just want, a quick aside, you mentioned performing under pressure. I think it was in the 1980s, it might have been in the opening of your book. You talked about playing multiple computers. >> [Garry] Yeah, in 1985. >> In 1985 and you were winning all of them. There was one close match, but the computer's name was Kasparov and you said I've got to beat this one because people will think that it's rigged or I'm getting paid to do this. So well done. >> It's I always mention this exhibition I played in 1985 against 32 chess-playing computers because it's not the importance of this event was not just I won all the games, but nobody was surprised. I have to admit that the fact that I could win all the games against these 32 chess-playing computers they're only chess-playing machine so they did nothing else. Probably boosted my confidence that I would never be defeated even by more powerful machines. >> Well I love it, that's why I asked the question how far can we take machines? We don't know, like you said. >> Why should we bother? I see so many new challenges that we will be able to take and challenges that we abandoned like space exploration or deep ocean exploration because they were too risky. We couldn't actually calculate all the odds. Great, now we have AI. It's all about increasing our risk because we could actually measure against this phenomenal power of AI that will help us to find the right pass. >> I want to follow up on some other commentary. Brynjolfsson and McAfee basically put forth the premise, look machines have always replaced humans. But this is the first time in history that they have replaced humans in the terms of cognitive tasks. They also posited look, there's no question that it's affecting jobs. But they put forth the prescription which I think as an optimist you would agree with, that it's about finding new opportunities. It's about bringing creativity in, complementing the machines and creating new value. As an optimist, I presume you would agree with that. >> Absolutely, I'm always saying jobs do not disappear, they evolve. It's an inevitable part of the technological progress. We come up with new ideas and every disruptive technology destroys some industries but creates new jobs. So basically we see jobs shifting from one industry to another. Like from agriculture, manufacture, from manufacture to other sectors, cognitive tasks. But now there will be something else. I think the market will change, the job market will change quite dramatically. Again I believe that we will have to look for riskier jobs. We will have to start doing things that we abandoned 30, 40 years ago because we thought they were too risky. >> Back to the book you were talking about, deep thinking or machine learning, or machine intelligence ends and human intelligence begins, you talked about courage. We need fail safes in place, but you also need that human element of courage like you said, to accept risk and take risk. >> Now it probably will be easier, but also as I said the machine's wheel will force a lot of talent actually to move into other areas that were not as attractive because there were other opportunities. There's so many what I call raw cognitive tasks that are still financially attractive. I hope and I will close many loops. We'll see talent moving into areas where we just have to open new horizons. I think it's very important just to remember it's the technological progress especially when you're talking about disruptive technology. It's more about unintended consequences. The fly to the moon was just psychologically it's important, the Space Race, the Cold War. But it was about also GPS, about so many side effects that in the 60s were not yet appreciated but eventually created the world we have now. I don't know what the consequences of us flying to Mars. Maybe something will happen, one of the asteroids will just find sort of a new substance that will replace fossil fuel. What I know, it will happen because when you look at the human history there's all this great exploration. They ended up with unintended consequences as the main result. Not what was originally planned as the number one goal. >> We've been talking about where innovation comes from today. It's a combination of a by-product out there. A combination of data plus being able to apply artificial intelligence. And of course there's cloud economics as well. Essentially, well is that reasonable? I think about something you said, I believe, in the past that you didn't have the advantage of seeing Deep Blue's moves, but it had the advantage of studying your moves. You didn't have all the data, it had the data. How does data fit into the future? >> Data is vital, data is fuel. That's why I think we need to find some of the most effective ways of collaboration between humans and machines. Machines can mine the data. For instance, it's a breakthrough in instantly mining data and human language. Now we could see even more effective tools to help us to mine the data. But at the end of the day it's why are we doing that? What's the purpose? What does matter to us, so why do we want to mine this data? Why do we want to do here and not there? It seems at first sight that the human responsibilities are shrinking. I think it's the opposite. We don't have to move too much but by the tiny shift, just you know percentage of a degree of an angle could actually make huge difference when this bullet reaches the target. The same with AI. More power actually offers opportunities to start just making tiny adjustments that could have massive consequences. >> Open up a big, that's why you like augmented intelligence. >> I think artificial is sci-fi. >> What's artificial about it, I don't understand. >> Artificial, it's an easy sell because it's sci-fi. But augmented is what it is because our intelligent machines are making us smarter. Same way as the technology in the past made us stronger and faster. >> It's not artificial horsepower. >> It's created from something. >> Exactly, it's created from something. Even if the machines can adjust their own code, fine. It still will be confined within the parameters of the tasks. They cannot go beyond that because again they can only answer questions. They can only give you answers. We provide the questions so it's very important to recognize that it is we will be in the leading role. That's why I use the term shepherds. >> How do you spend your time these days? You're obviously writing, you're speaking. >> Writing, speaking, traveling around the world because I have to show up at many conferences. The AI now is a very hot topic. Also as you mentioned I'm the Chairman of Human Rights Foundation. My responsibilities to help people who are just dissidents around the world who are fighting for their principles and for freedom. Our organization runs the largest dissident gathering in the world. It's called the Freedom Forum. We have the tenth anniversary, tenth event this May. >> It has been a pleasure. Garry Kasparov, live on theCube. Back with more from New York City right after this. (lively instrumental music)

>>from Las Vegas. It's the Q covering. Next work. 2019 America's Do You buy Juniper Networks? Come back already. Jeffrey here with the Cube were in Las Vegas at Caesar's at the Juniper. Next work event. About 1000 people kind of going over a lot of new cool things. 400 gigs. Who knew that was coming out of new information for me? But that's not what we're here today. We're here for the fourth installment of around the Cube unpacking. I were happy to have all the winners of the three previous rounds here at the same place. We don't have to do it over the phone s so we're happy to have him. Let's jump into it. So winner of Round one was Bob Friday. He is the VP and CTO at Missed the Juniper Company. Bob, Great to see you. Good to be back. Absolutely. All the way from Seattle. Sharna Parky. She's a VP applied scientist at Tech CEO could see Sharna and, uh, from Google. We know a lot of a I happen to Google. Rajan's chef. He is the V p ay ay >>product management on Google. Welcome. Thank you, Christy. Here >>All right, so let's jump into it. So just warm everybody up and we'll start with you. Bob, What are some When you're talking to someone at a cocktail party Friday night talking to your mom And they say, What is a I What >>do you >>give him? A Zen examples of where a eyes of packing our lives today? >>Well, I think we all know the examples of the south driving car, you know? Aye, aye. Starting to help our health care industry being diagnosed cancer for me. Personally, I had kind of a weird experience last week at a retail technology event where basically had these new digital mirrors doing facial recognition. Right? And basically, you start to have little mirrors were gonna be a skeevy start guessing. Hey, you have a beard, you have some glasses, and they start calling >>me old. So this is kind >>of very personal. I have a something for >>you, Camille, but eh? I go walking >>down a mall with a bunch of mirrors, calling me old. >>That's a little Illinois. Did it bring you out like a cane or a walker? You know, you start getting some advertising's >>that were like Okay, you guys, this is a little bit over the top. >>Alright, Charlotte, what about you? What's your favorite example? Share with people? >>Yeah, E think one of my favorite examples of a I is, um, kind of accessible in on your phone where the photos you take on an iPhone. The photos you put in Google photos, they're automatically detecting the faces and their labeling them for you. They're like, Here's selfies. Here's your family. Here's your Children. And you know, that's the most successful one of the ones that I think people don't really think about a lot or things like getting loan applications right. We actually have a I deciding whether or not we get loans. And that one is is probably the most interesting one to be right now. >>Roger. So I think the father's example is probably my favorite as well. And what's interesting to me is that really a I is actually not about the Yeah, it's about the user experience that you can create as a result of a I. What's cool about Google photos is that and my entire family uses Google photos and they don't even know actually that the underlying in some of the most powerful a I in the world. But what they know is they confined every picture of our kids on the beach whenever they whenever they want to. Or, you know, we had a great example where we were with our kids. Every time they like something in the store, we take a picture of it, Um, and we can look up toy and actually find everything that they've taken picture. >>It's interesting because I think most people don't even know the power that they have. Because if you search for beach in your Google photos or you search for, uh, I was looking for an old bug picture from my high school there it came right up until you kind of explore. You know, it's pretty tricky, Raja, you know, I think a lot of conversation about A They always focus the general purpose general purpose, general purpose machines and robots and computers. But people don't really talk about the applied A that's happening all around. Why do you think that? >>So it's a good question. There's there's a lot more talk about kind of general purpose, but the reality of where this has an impact right now is, though, are those specific use cases. And so, for example, things like personalizing customer interaction or, ah, spotting trends that did that you wouldn't have spotted for turning unstructured data like documents into structure data. That's where a eyes actually having an impact right now. And I think it really boils down to getting to the right use cases where a I right? >>Sharon, I want ask you. You know, there's a lot of conversation. Always has A I replace people or is it an augmentation for people? And we had Gary Kasparov on a couple years ago, and he talked about, you know, it was the combination if he plus the computer made the best chess player, but that quickly went away. Now the computer is actually better than Garry Kasparov. Plus the computer. How should people think about a I as an augmentation tool versus a replacement tool? And is it just gonna be specific to the application? And how do you kind of think about those? >>Yeah, I would say >>that any application where you're making life and death decisions where you're making financial decisions that disadvantage people anything where you know you've got u A. V s and you're deciding whether or not to actually dropped the bomb like you need a human in the loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and a I doesn't know that, uh, you know, if it's always seen sheep in a field that when the sheep aren't there, that that isn't a sheep like it doesn't have that kind of recognition to it. So anything were we making decisions about parole or financial? Anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. >>Great. So shift gears. The team are Jeff Saunders. Okay, team, your mind may have been the liquid on my bell, so I'll be more active on the bell. Sorry about that. Everyone's even. We're starting a zero again, so I want to shift gears and talk about data sets. Um Bob, you're up on stage. Demo ing some some of your technology, the Miss Technology and really, you know, it's interesting combination of data sets A I and its current form needs a lot of data again. Kind of the classic Chihuahua on blue buried and photos. You got to run a lot of them through. How do you think about data sets? In terms of having the right data in a complete data set to drive an algorithm >>E. I think we all know data sets with one The tipping points for a I to become more real right along with cloud computing storage. But data is really one of the key points of making a I really write my example on stage was wine, right? Great wine starts a great grape street. Aye, aye. Starts a great data for us personally. L s t M is an example in our networking space where we have data for the last three months from our customers and rule using the last 30 days really trained these l s t m algorithms to really get that tsunami detection the point where we don't have false positives. >>How much of the training is done. Once you once you've gone through the data a couple times in a just versus when you first started, you're not really sure how it's gonna shake out in the algorithm. >>Yeah. So in our case right now, right, training happens every night. So every night, we're basically retraining those models, basically, to be able to predict if there's gonna be an anomaly or network, you know? And this is really an example. Where you looking all these other cat image thinks this is where these neural networks there really were one of the transformational things that really moved a I into the reality calling. And it's starting to impact all our different energy. Whether it's text imaging in the networking world is an example where even a I and deep learnings ruling starting to impact our networking customers. >>Sure, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of chihuahuas and blackberries, and I want to apply some machine intelligence to the problem. >>I mean, so you need to have the right data set. You know, Big is a relative term on, and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares, and then you're trying to detect some anomaly, right? If you train and I what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there, so you actually need to over sample. You have to create a population of data that allows you to detect images you can't say, Um oh, >>I'm going to reflect in my data set the percentage of black women >>in Seattle, which is something below 6% and say it's fair. It's not right. You have to be able thio over sample things that you need, and in some ways you can get this through surveys. You can get it through, um, actually going to different sources. But you have to boot, strap it in some way, and then you have to refresh it, because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time, and so you may have been able to find the one yesterday. But today it's a completely different ball game >>project to you, which comes first, the chicken or the egg. You start with the data, and I say this is a ripe opportunity to apply some. Aye, aye. Or do you have some May I objectives that you want to achieve? And I got to go out and find the >>data. So I actually think what starts where it starts is the business problem you're trying to solve. And then from there, you need to have the right data. What's interesting about this is that you can actually have starting points. And so, for example, there's techniques around transfer, learning where you're able to take an an algorithm that's already been trained on a bunch of data and training a little bit further with with your data on DSO, we've seen that such that people that may have, for example, only 100 images of something, but they could use a model that's trained on millions of images and only use those 100 thio create something that's actually quite accurate. >>So that's a great segue. Wait, give me a ring on now. And it's a great Segway into talking about applying on one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Is air you risking all kinds of interesting problems by taking that and applying it here, especially in light of when people are gonna go to outweigh the marketplace, is because I've got a date. A scientist. I couldn't go get one in the marketplace and apply to my data. How should people be careful not to make >>a bad decision based on that? So I think it really depends. And it depends on the type of machine learning that you're doing and what type of data you're talking about. So, for example, with images, they're they're they're well known techniques to be able to do this, but with other things, there aren't really and so it really depends. But then the other inter, the other really important thing is that no matter what at the end, you need to test and generate based on your based on your data sets and on based on sample data to see if it's accurate or not, and then that's gonna guide everything. Ultimately, >>Sharon has got to go to you. You brought up something in the preliminary rounds and about open A I and kind of this. We can't have this black box where stuff goes into the algorithm. That stuff comes out and we're not sure what the result was. Sounds really important. Is that Is that even plausible? Is it feasible? This is crazy statistics, Crazy math. You talked about the business objective that someone's trying to achieve. I go to the data scientist. Here's my data. You're telling this is the output. How kind of where's the line between the Lehman and the business person and the hard core data science to bring together the knowledge of Here's what's making the algorithm say this. >>Yeah, there's a lot of names for this, whether it's explainable. Aye, aye. Or interpret a belay. I are opening the black box. Things like that. Um, the algorithms that you use determine whether or not they're inspect herbal. Um, and the deeper your neural network gets, the harder it is to inspect, actually. Right. So, to your point, every time you take an aye aye and you use it in a different scenario than what it was built for. For example, um, there is a police precinct in New York that had a facial recognition software, and, uh, victim said, Oh, it looked like this actor. This person looked like Bill Cosby or something like that, and you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So the Russians point yes, like it. You can transfer learning to other a eyes, but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Um, even if you're a, I is explainable, and somebody tries to use it in a way that it was never intended to be used. The risk is much higher >>now. I think maybe I had, You know, if you look at Marvis kind of what we're building for the networking community Ah, good examples. When Marvis tries to do estimate your throughput right, your Internet throughput. That's what we usually call decision tree algorithm. And that's a very interpretive algorithm. and we predict low throughput. We know how we got to that answer, right? We know what features God, is there? No. But when we're doing something like a NAMI detection, that's a neural network. That black box it tells us yes, there's a problem. There's some anomaly, but that doesn't know what caused the anomaly. But that's a case where we actually used neural networks, actually find the anomie, and then we're using something else to find the root cause, eh? So it really depends on the use case and where the night you're going to use an interpreter of model or a neural network which is more of a black box model. T tell her you've got a cat or you've got a problem >>somewhere. So, Bob, that's really interested. So can you not unpacking? Neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the >>separate kind of process too. Get to the root cause. >>Yeah, assigned is always hard to say. Never. But inherently s neural networks are very complicated. Saito set of weights, right? It's basically usually a supervised training model, and we're feeding a bunch of data and trying to train it to detect a certain features, sir, an output. But that is where they're powerful, right? And that's why they basically doing such good, Because they are mimicking the brain, right? That neural network is a very complex thing. Can't like your brain, right? We really don't understand how your brain works right now when you have a problem, it's really trialling there. We try to figure out >>right going right. So I want to stay with you, bought for a minute. So what about when you change what you're optimizing? Four? So you just said you're optimizing for throughput of the network. You're looking for problems. Now, let's just say it's, uh, into the end of the quarter. Some other reason we're not. You're changing your changing what you're optimizing for, Can you? You have to write separate algorithm. Can you have dynamic movement inside that algorithm? How do you approach a problem? Because you're not always optimizing for the same things, depending on the market conditions. >>Yeah, I mean, I think a good example, you know, again, with Marvis is really with what we call reinforcement. Learning right in reinforcement. Learning is a model we use for, like, radio resource management. And there were really trying to optimize for the user experience in trying to balance the reward, the models trying to reward whether or not we have a good balance between the network and the user. Right, that reward could be changed. So that algorithm is basically reinforcement. You can finally change hell that Algren works by changing the reward you give the algorithm >>great. Um, Rajan back to you. A couple of huge things that have come into into play in the marketplace and get your take one is open source, you know, kind of. What's the impact of open source generally on the availability, desire and more applications and then to cloud and soon to be edge? You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of >>a I Yeah, I think open source is really important because I think one thing that's interesting about a I is that it's a very nascent field and the more that there's open source, the more that people could build on top of each other and be able to utilize what what others others have done. And it's similar to how we've seen open source impact operating systems, the Internet, things like things like that with Cloud. I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to t create these thes networks. And so the capacity for data and the capacity for compute is much higher. Edge is gonna be a very important thing, especially going into next few years. You're seeing Maur things incorporated on the edge and one exciting development is around Federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make EJ even more powerful. >>But it's got to be so dynamic, right? Because the fundamental problem used to always be the move, the computer, the data or the date of the computer. Well, now you've got on these edge devices. You've got Tanya data right sensor data all kinds of machining data. You've got potentially nasty hostile conditions. You're not in a nice, pristine data center where the environmental conditions are in the connective ity issues. So when you think about that problem yet, there's still great information. There you got latent issues. Some I might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break the break up? The problem to give you a step up? Well, we see a lot >>of customers do is they do a lot of training on the cloud, but then inference on the on the edge. And so that way they're able to create the model that they want. But then they get fast response time by moving the model to the edge. The other thing is that, like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud. But the other way to do is filter. And to try to figure out what data you want to send to the clouds that you can create the next days. >>Shawna, back to you let's shift gears into ethics. This pesky, pesky issue that's not not a technological issue at all, but right. We see it often, especially in tech. Just cause you should just cause you can doesn't mean that you should. Um so and this is not a stem issue, right? There's a lot of different things that happened. So how should people be thinking about ethics? How should they incorporate ethics? Um, how should they make sure that they've got kind of a, you know, a standard kind of overlooking kind of what they're doing? The decisions are being made. >>Yeah, One of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study, and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change of behavior. We need to ask questions like, What is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem, right? And this this can be true of the intention, goal gap or the intention action up. We can see when we regulated for cigarettes. What? We can't just make it look cool without telling them what the cigarettes are doing to them, right so we can apply the same principles moving forward. And they're pretty accessible without having to know. Oh, this philosopher and that philosopher in this ethicist said these things, it can be pretty human. The challenge with this is that most people building these algorithms are not. They're not trained in this way of thinking, and especially when you're working at a start up right, you don't have access to massive teams of people to guide you down this journey, so you need to build it in from the beginning, and you need to be open and based upon principles. Um, and it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem you need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to still that product for a wider audience, but it seems like >>they were on such a razor sharp edge. Right with Coca Cola wants you to buy Coca Cola and they show ads for Coca Cola, and they appeal to your let's all sing together on the hillside and be one right. But it feels like with a I that that is now you can cheat. Right now you can use behavioral biases that are hardwired into my brain is a biological creature against me. And so where is where is the fine line between just trying to get you to buy Coke? Which somewhat argues Probably Justus Bad is Jule cause you get diabetes and all these other issues, but that's acceptable. But cigarettes are not. And now we're seeing this stuff on Facebook with, you know, they're coming out. So >>we know that this is that and Coke isn't just selling Coke anymore. They're also selling vitamin water so they're they're play isn't to have a single product that you can purchase, but it is to have a suite of products that if you weren't that coke, you can buy it. But if you want that vitamin water you can have that >>shouldn't get vitamin water and a smile that only comes with the coat. Five. You want to jump in? >>I think we're going to see ethics really break into two different discussions, right? I mean, ethics is already, like human behavior that you're already doing right, doing bad behavior, like discriminatory hiring, training, that behavior. And today I is gonna be wrong. It's wrong in the human world is gonna be wrong in the eye world. I think the other component to this ethics discussion is really round privacy and data. It's like that mirror example, right? No. Who gave that mirror the right to basically tell me I'm old and actually do something with that data right now. Is that my data? Or is that the mirrors data that basically recognized me and basically did something with it? Right. You know, that's the Facebook. For example. When I get the email, tell me, look at that picture and someone's take me in the pictures Like, where was that? Where did that come from? Right? >>What? I'm curious about to fall upon that as social norms change. We talked about it a little bit for we turn the cameras on, right? It used to be okay. Toe have no black people drinking out of a fountain or coming in the side door of a restaurant. Not that long ago, right in the 60. So if someone had built an algorithm, then that would have incorporated probably that social norm. But social norms change. So how should we, you know, kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, That's no longer acceptable. We need to tweak this. I >>would have said in that example, that was wrong. 50 years ago. >>Okay, it was wrong. But if you ask somebody in Alabama, you know, at the University of Alabama, Matt Department who have been born Red born, bred in that culture as well, they probably would have not necessarily agreed. But so generally, though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying four things that are no longer the right thing to do? >>Well, I think I mean, as I said, I think you know what? What we know is wrong, you know is gonna be wrong in the eye world. I think the more subtle thing is when we start relying on these Aye. Aye. To make decisions like no shit in my car, hit the pedestrian or save my life. You know, those are tough decisions to let a machine take off or your balls decision. Right when we start letting the machines Or is it okay for Marvis to give this D I ps preference over other people, right? You know, those type of decisions are kind of the ethical decision, you know, whether right or wrong, the human world, I think the same thing will apply in the eye world. I do think it will start to see more regulation. Just like we see regulation happen in our hiring. No, that regulation is going to be applied into our A I >>right solutions. We're gonna come back to regulation a minute. But, Roger, I want to follow up with you in your earlier session. You you made an interesting comment. You said, you know, 10% is clearly, you know, good. 10% is clearly bad, but it's a soft, squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people, you know, kind of make judgments in this this big gray area in the middle? >>Yeah, and I think that is the toughest part. And so the approach that we've taken is to set us set out a set of AI ai principles on DDE. What we did is actually wrote down seven things that we will that we think I should do and four things that we should not do that we will not do. And we now have to actually look at everything that we're doing against those Aye aye principles. And so part of that is coming up with that governance process because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what what you should do and so that governments process is something we're doing. But I think it's something that every company is going to need to do. >>Sharon, I want to come back to you, so we'll shift gears to talk a little bit about about law. We've all seen Zuckerberg, unfortunately for him has been, you know, stuck in these congressional hearings over and over and over again. A little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. You know, he stumbled into some really big Harry nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen, unfortunately, a lot of those hearings is a lot of our elected officials are way, way, way behind there, still printing their e mails, right? So what is the role of the law? How should we think about it? What shall we What should we invite from fromthe law to help sort some of this stuff out? >>I think as an individual, right, I would like for each company not to make up their own set of principles. I would like to have a shared set of principles that were following the challenge. Right, is that with between governments, that's impossible. China is never gonna come up with same regulations that we will. They have a different privacy standards than we D'oh. Um, but we are seeing locally like the state of Washington has created a future of work task force. And they're coming into the private sector and asking companies like text you and like Google and Microsoft to actually advise them on what should we be regulating? We don't know. We're not the technologists, but they know how to regulate. And they know how to move policies through the government. What will find us if we don't advise regulators on what we should be regulating? They're going to regulate it in some way, just like they regulated the tobacco industry. Just like they regulated. Sort of, um, monopolies that tech is big enough. Now there is enough money in it now that it will be regularly. So we need to start advising them on what we should regulate because just like Mark, he said. While everyone else was doing it, my competitors were doing it. So if you >>don't want me to do it, make us all stop. What >>can I do? A negative bell and that would not for you, but for Mark's responsibly. That's crazy. So So bob old man at the mall. It's actually a little bit more codified right, There's GDP are which came through May of last year and now the newness to California Extra Gatorade, California Consumer Protection Act, which goes into effect January 1. And you know it's interesting is that the hardest part of the implementation of that I think I haven't implemented it is the right to be for gotten because, as we all know, computers, air, really good recording information and cloud. It's recorded everywhere. There's no there there. So when these types of regulations, how does that impact? Aye, aye, because if I've got an algorithm built on a data set in in person, you know, item number 472 decides they want to be forgotten How that too I deal with that. >>Well, I mean, I think with Facebook, I can see that as I think. I suspect Mark knows what's right and wrong. He's just kicking ball down tires like >>I want you guys. >>It's your problem, you know. Please tell me what to do. I see a ice kind of like any other new technology, you know, it could be abused and used in the wrong waste. I think legally we have a constitution that protects our rights. And I think we're going to see the lawyers treat a I just like any other constitutional things and people who are building products using a I just like me build medical products or other products and actually harmful people. You're gonna have to make sure that you're a I product does not harm people. You're a product does not include no promote discriminatory results. So I >>think we're going >>to see our constitutional thing is going applied A I just like we've seen other technologies work. >>And it's gonna create jobs because of that, right? Because >>it will be a whole new set of lawyers >>the holdings of lawyers and testers, even because otherwise of an individual company is saying. But we tested. It >>works. Trust us. Like, how are you gonna get the independent third party verification of that? So we're gonna start to see a whole terrorist proliferation of that type of fields that never had to exist before. >>Yeah, one of my favorite doctor room. A child. Grief from a center. If you don't follow her on Twitter Follower. She's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And Rahman up on the keynote this morning, talked about missed and and really, this kind of shifting workload of fixing things into an autonomous set up where the system now is, is finding problems, diagnosing problems, fixing problems up to, I think, he said, even generating return authorizations for broken gear, which is amazing. But autonomy opens up all kinds of crazy, scary things. Robert Gates, we interviewed said, You know, the only guns that are that are autonomous in the entire U. S. Military are the ones on the border of North Korea. Every single other one has to run through a person when you think about autonomy and when you can actually grant this this a I the autonomy of the agency toe act. What are some of the things to think about in the word of the things to keep from just doing something bad, really, really fast and efficiently? >>Yeah. I mean, I think that what we discussed, right? I mean, I think Pakal purposes we're far, you know, there is a tipping point. I think eventually we will get to the CP 30 Terminator day where we actually build something is on par with the human. But for the purposes right now, we're really looking at tools that we're going to help businesses, doctors, self driving cars and those tools are gonna be used by our customers to basically allow them to do more productive things with their time. You know, whether it's doctor that's using a tool to actually use a I to predict help bank better predictions. They're still gonna be a human involved, you know, And what Romney talked about this morning and networking is really allowing our I T customers focus more on their business problems where they don't have to spend their time finding bad hard were bad software and making better experiences for the people. They're actually trying to serve >>right, trying to get your take on on autonomy because because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own. But >>there's there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read. There's a book that just came out. Oh, what's the title? You look like a thing. And I love you. It was a book named by an A I, um if you want to learn a lot about a I, um and you don't know much about it, Get it? It's really funny. Um, so in there there is in China. Ah, factory where the Aye Aye. Is optimizing um, output of cockroaches now they just They want more cockroaches now. Why do they want that? They want to grind them up and put them in a lotion. It's one of their secret ingredients now. It depends on what parameters you allow that I to change, right? If you decide Thio let the way I flood the container, and then the cockroaches get out through the vents and then they get to the kitchen to get food, and then they reproduce the parameters in which you let them be autonomous. Over is the challenge. So when we're working with very narrow Ai ai, when use hell the Aye. Aye. You can change these three things and you can't just change anything. Then it's a lot easier to make that autonomous decision. Um and then the last part of it is that you want to know what is the results of a negative outcome, right? There was the result of a positive outcome. And are those results something that we can take actually? >>Right, Right. Roger, don't give you the last word on the time. Because kind of the next order of step is where that machines actually write their own algorithms, right? They start to write their own code, so they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in the space, you have huge data set. You got great technology. Got tensorflow. When will the machines start writing their own A their own out rhythms? Well, and actually >>it's already starting there that, you know, for example, we have we have a product called Google Cloud. Ottawa. Mel Village basically takes in a data set, and then we find the best model to be able to match that data set. And so things like that that that are there already, but it's still very nascent. There's a lot more than that that can happen. And I think ultimately with with how it's used I think part of it is you have to start. Always look at the downside of automation. And what is what is the downside of a bad decision, whether it's the wrong algorithm that you create or a bad decision in that model? And so if the downside is really big, that's where you need to start to apply Human in the loop. And so, for example, in medicine. Hey, I could do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need tohave have that place in many situations to make sure that it's being applied well. >>But is that just today? Or is that tomorrow? Because, you know, with with exponential growth and and as fast as these things are growing, will there be a day where you don't necessarily need maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and, you know, kind of pros and cons of treatment options that are more emotional than necessarily mechanical, because it seems like eventually that the doctor has a role. But it isn't necessarily in accurately diagnosing a problem. >>I think >>I think for some things, absolutely over time the algorithms will get better and better, and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where, where that is. So, for example, self driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is gonna be much lower than what's there with humans today, and so that will get there. But it will take time. >>And there was a day when will be illegal for you to drive. You have manslaughter, right? >>I I believe absolutely there will be in and and I don't think it's that far off. Actually, >>wait for the day when I have my car take me up to Northern California with me. Sleepy. I've only lived that long. >>That's right. And work while you're while you're sleeping, right? Well, I want to thank everybody Aton for being on this panel. This has been super fun and these air really big issues. So I want to give you the final word will just give everyone kind of a final say and I just want to throw out their Mars law. People talk about Moore's law all the time. But tomorrow's law, which Gardner stolen made into the hype cycle, you know, is that we tend to overestimate in the short term, which is why you get the hype cycle and we turn. Tend to underestimate, in the long term the impacts of technology. So I just want it is you look forward in the future won't put a year number on it, you know, kind of. How do you see this rolling out? What do you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. >>Yeah, you know, for me and, you know, the day of the terminus Heathrow. I don't know if it's 100 years or 1000 years. That day is coming. We will eventually build something that's in part of the human. I think the mission about the book, you know, you look like a thing and I love >>you. >>Type of thing that was written by someone who tried to train a I to basically pick up lines. Right? Cheesy pickup lines. Yeah, I'm not for sure. I'm gonna trust a I to help me in my pickup lines yet. You know I love you. Look at your thing. I love you. I don't know if they work. >>Yeah, but who would? Who would have guessed online dating is is what it is if you had asked, you know, 15 years ago. But I >>think yes, I think overall, yes, we will see the Terminator Cp through It was probably not in our lifetime, but it is in the future somewhere. A. I is definitely gonna be on par with the Internet cell phone, radio. It's gonna be a technology that's gonna be accelerating if you look where technology's been over last. Is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah, we're just on this curve of technology accelerations. This in the >>exponential curves China. >>Yeah, I think the thing I'm most excited about for a I right now is the addition of creativity to a lot of our jobs. So ah, lot of we build an augmented writing product. And what we do is we look at the words that have happened in the world and their outcomes. And we tell you what words have impacted people in the past. Now, with that information, when you augment humans in that way, they get to be more creative. They get to use language that have never been used before. To communicate an idea. You can do this with any field you can do with composition of music. You can if you can have access as an individual, thio the data of a bunch of cultures the way that we evolved can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building Thio Give a I to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the Internet to build a model on a data set. And I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not, um and so it's It's an interesting time because we're in that middle area. Um, and >>it's getting loud, all right, Roger will throw with you before we have to cut out, or we're not gonna be able to hear anything. So I actually start every presentation out with a picture of the Mosaic browser, because what's interesting is I think that's where >>a eyes today compared to kind of weather when the Internet was around 1994 >>were just starting to see how a I can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the company's I talked to the first question is, Why should I be using this? And what benefit does it give me? Why 70% ask you why? Yeah, and and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long >>run, and we underestimate the longer I think that every company out there and every product will be fundamentally transformed by eye over the course of the next decade, and it's actually gonna have a bigger impact on the Internet itself. And so that's really what we have to look forward to. >>All right again. Thank you everybody for participating. There was a ton of fun. Hope you had fun. And I look at the score sheet here. We've got Bob coming in and the bronze at 15 points. Rajan, it's 17 in our gold medal winner for the silver Bell. Is Sharna at 20 points. Again. Thank you. Uh, thank you so much and look forward to our next conversation. Thank Jeffrey Ake signing out from Caesar's Juniper. Next word unpacking. I Thanks for watching.

>> Live from Boston, it's theCUBE! Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back everyone to the IBM CDO Summit and theCUBE's live coverage, I'm your host Rebecca Knight along with my co-host Paul Gillin. We have Joe Selle joining us. He is the Cognitive Solution Lead at IBM. And Thomas Ward, Supply Chain Cloud Strategist at IBM. Thank you so much for coming on the show! >> Thank you! >> Our pleasure. >> Pleasure to be here. >> So, Tom, I want to start with you. You are the author of Risk Insights. Tell our viewers a little bit about Risk Insights. >> So Risk Insights is a AI application. We've been working on it for a couple years. What's really neat about it, it's the coolest project I've ever worked on. And it really gets a massive amount of data from the weather company, so we're one of the biggest consumers of data from the weather company. We take that and we'd visualize who's at risk from things like hurricanes, earthquakes, things like IBM sites and locations or suppliers. And we basically notify them in advance when those events are going to impact them and it ties to both our data center operations activity as well as our supply chain operations. >> So you reduce your risk, your supply chain risk, by being able to proactively detect potential outages. >> Yeah, exactly. So we know in some cases two or three days in advance who's in harm's way and we're already looking up and trying to mitigate those risks if we need to, it's going to be a real serious event. So Hurricane Michael, Hurricane Florence, we were right on top of it and said we got to worry about these suppliers, these data center locations, and we're already working on that in advance. >> That's very cool. So, I mean, how are clients and customers, there's got to be, as you said, it's the coolest project you've ever worked on? >> Yeah. So right now, we use it within IBM right? And we use it to monitor some of IBM's client locations, and in the future we're actually, there was something called the Call for Code that happened recently within IBM, this project was a semifinalist for that. So we're now working with some non-profit groups to see how they could also avail of it, looking at things like hospitals and airports and those types of things as well. >> What other AI projects are you running? >> Go ahead. >> I can answer that one. I just wanted to say one thing about Risk Insights, which didn't come out from Tom's description, which is that one of the other really neat things about it is that it provides alerts, smart alerts out to supply chain planners. And the alert will go to a supply chain planner if there's an intersection of a supplier of IBM and a path of a hurricane. If the hurricane is vectored to go over that supplier, the supply chain planner that is responsible for those parts will get some forewarning to either start to look for another supplier, or make some contingency plans. And the other nice thing about it is that it launches what we call a Resolution Room. And the Resolution Room is a virtual meeting place where people all over the globe who are somehow impacted by this event can collaborate, share documents, and have a persistent place to resolve this issue. And then, after that's all done, we capture all the data from that issue and the resolution and we put that into a body of knowledge, and we mine that knowledge for a playbook the next time a similar event comes along. So it's a full-- >> It becomes machine learning. >> It's a machine learning-- >> Sort of data source. >> It's a full soup to nuts solution that gets smarter over time. >> So you should be able to measure benefits, you should have measurable benefits by now, right? What are you seeing, fewer disruptions? >> Yes, so in Risk Insights, we know that out of a thousand of events that occurred, there were 25 in the last year that were really the ones we needed to identify and mitigate against. And out of those we know there have been circumstances where, in the past IBM's had millions of dollars of losses. By being more proactive, we're really minimizing that amount. >> That's incredible. So you were going to talk about other kinds of AI that you run. >> Right, so Tom gave an overview of Risk Insights, and we tied it to supply chain and to monitoring the uptime of our customer data centers and things like that. But our portfolio of AI is quite broad. It really covers most of the middle and back and front office functions of IBM. So we have things in the sales domain, the finance domain, the HR domain, you name it. One of the ones that's particularly interesting to me of late is in the finance domain, monitoring accounts receivable and DSO, day sales outstanding. So a company like IBM, with multiple billions of dollars of revenue, to make a change of even one day of day sales outstanding, provides gigantic benefit to the bottom line. So we have been integrating disparate databases across the business units and geographies of IBM, pulling that customer and accounts receivable data into one place, where our CFO can look at an integrated approach towards our accounts receivable and we know where the problems are, and we're going to use AI and other advanced analytic techniques to determine what's the best treatment for that AI, for those customers who are at risk because of our predictive models, of not making their payments on time or some sort of financial risk. So we can integrate a lot of external unstructured data with our own structured data around customers, around accounts, and pull together a story around AR that we've never been able to pull before. That's very impactful. >> So speaking of unstructured data, I understand that data lakes are part of your AI platform. How so? >> For example, for Risk Insights, we're monitoring hundreds of trusted news sources at any given time. So we know, not just where the event is, what locations are at risk, but also what's being reported about it. We monitor Twitter reports about it, we monitor trusted news sources like CNN or MSNBC, or on a global basis, so it gives our risk analyst not just a view of where the event is, where it's located, but also what's being said, how severe it is, how big are those tidal waves, how big was the storm surge, how many people were affected. By applying some of the machine learning insights to these, now we can say, well if there are couple hundred thousand people without power then it's very likely there is going to be multimillions of dollars of impact as a result. So we're now able to correlate those news reports with the magnitude of impact and potential financial impact to the businesses that we're supporting. >> So the idea being that IBM is saying, look what we've done for our own business (laughs), imagine what we could do for you. As Inderpal has said, it's really using IBM as its own test case and trying to figure this all out and learning as it goes and he said, we're going to make some mistakes, we've already made some mistakes but we're figuring it out so you don't have to make those mistakes. >> Yeah that's right. I mean, if you think about the long history of this, we've been investing in AI, really, since, depending on how you look at it, since the days of the 90's, when we were doing Deep Blue and we were trying to beat Garry Kasparov at chess. Then we did another big huge push on the Jeopardy program, where we we innovated around natural language understanding and speed and scale of processing and probability correctness of answers. And then we kind of carry that right through to the current day where we're now proliferating AI across all of the functions of IBM. And there, then, connecting to your comment, Inderpal's comment this morning was around let's just use all of that for the benefit of other companies. It's not always an exact fit, it's never an exact fit, but there are a lot of pieces that can be replicated and borrowed, either people, process or technology, from our experience, that would help to accelerate other companies down the same path. >> One of the questions around AI though is, can you trust it? The insights that it derives, are they trustworthy? >> I'll give a quick answer to that, and then Tom, it's probably something you want to chime in on. There's a lot of danger in AI, and it needs to be monitored closely. There's bias that can creep into the datasets because the datasets are being enhanced with cognitive techniques. There's bias that can creep into the algorithms and any kind of learning model can start to spin on its own axis and go in its own direction and if you're not watching and monitoring and auditing, then it could be starting to deliver you crazy answers. Then the other part is, you need to build the trust of the users, because who wants to take an answer that's coming out of a black box? We've launched several AI projects where the answer just comes out naked, if you will, just sitting right there and there's no context around it and the users never like that. So we've understood now that you have to put the context, the underlying calculations, and the assessment of our own probability of being correct in there. So those are some of the things you can do to get over that. But Tom, do you have anything to add to that? >> I'll just give an example. When we were early in analyzing Twitter tweets about a major storm, what we've read about was, oh, some celebrity's dog was in danger, like uh. (Rebecca laughs) This isn't very helpful insight. >> I'm going to guess, I probably know the celebrity's dog that was in danger. (laughs) >> (laughs) actually stop saying that. So we learned how to filter those things out and say what are the meaningful keywords that we need to extract from and really then can draw conclusions from. >> So is Kardashian a meaningful word, (all laughing) I guess that's the question. >> Trending! (all laughing) >> Trending now! >> I want to follow up on that because as an AI developer, what responsibility do developers have to show their work, to document how their models have worked? >> Yes, so all of our information that we provided the users all draws back to, here's the original source, here's where the information was taken from so we can draw back on that. And that's an important part of having a cognitive data, cognitive enterprise data platform where all this information is stored 'cause then we can refer to that and go deeper as well and we can analyze it further after the fact, right? You can't always respond in the moment, but once you have those records, that's how you can learn from it for the next time around. >> I understand that building test models in some cases, particularly in deep learning is very difficult to build reliable test models. Is that true, and what progress is being made there? >> In our case, we're into the machine learning dimension yet, we're not all the way into deep learning in the project that I'm involved with right now. But one reason we're not there is 'cause you need to have huge, huge, vast amounts of robust data and that trusted dataset from which to work. So we aspire towards and we're heading towards deep learning. We're not quite there yet, but we've started with machine learning insights and we'll progress from there. >> And one of the interesting things about this AI movement overall is that it's filled with very energetic people that's kind of a hacker mindset to the whole thing. So people are grabbing and running with code, they're using a lot of open source, there's a lot of integration of the black box from here, from there in the other place, which all adds to the risk of the output. So that comes back to the original point which is that you have to monitor, you have to make sure that you're comfortable with it. You can't just let it run on its own course without really testing it to see whether you agree with the output. >> So what other best practices, there's the monitoring, but at the same time you do that hacker culture, that's not all bad. You want people who are energized by it and you are trying new things and experimenting. So how do you make sure you let them have, sort of enough rein but not free rein? >> I would say, what comes to mind is, start with the business problem that's a real problem. Don't make this an experimental data thing. Start with the business problem. Develop a POC, a proof of concept. Small, and here's where the hackers come in. They're going to help you get it up and running in six weeks as opposed to six months. And then once you're at the end of that six-week period, maybe you design one more six-week iteration and then you know enough to start scaling it and you scale it big so you've harnessed the hackers, the energy, the speed, but you're also testing, making sure that it's accurate and then you're scaling it. >> Excellent. Well thank you Tom and Joe, I really appreciate it. It's great to have you on the show. >> Thank you! >> Thank you, Rebecca, for the spot. >> I'm Rebecca Knight for Paul Gillin, we will have more from the IBM CDO summit just after this. (light music)

(energetic music) >> Live from Miami Beach, Florida. It's theCUBE covering UiPathForward Americas. Brought to you by UiPath. >> Welcome to Miami everybody. This is theCUBE the leader in live tech coverage. We're here covering the UiPathForward Americas conference. UiPath is a company that has come out of nowhere, really. And, is a leader in robotic process automation, RPA. It really is about software robots. I am Dave Vellante and I am here with Stu Miniman. We have one day of coverage, Stu. We are all over the place this weekend. Aren't we? Stu and I were in Orlando earlier. Flew down. Quick flight to Miami and we're getting the Kool-Aid injection from the RPA crowd. We're at the Fontainebleau in Miami. Kind of cool hotel. Stu you might remember, I am sure, you do, several years ago we did the very first .NEXT tour. .NEXT from Nutanix at this event. About this same size, maybe a little smaller. This is a little bigger. >> Dave, this is probably twice the size, about 1,500 people here. I remember about a year ago you were, started buzzing about RPA. Big growth in the market, you know really enjoyed getting into the keynote here. You know, you said we were at splunk and data was at the center of everything, and the CEO here for (mumbles), it's automation first. We talked about mobile first, cloud first, automation first. I know we got a lot of things we want to talk about because you know, I think back through my career, and I know you do too, automation is something we've been talking about for years. We struggle with it. There's challenges there, but there's a lot of things coming together and that's why we have this new era that RPA is striking at to really explode this market. >> Yeah, so I made a little prediction that I put out on Twitter, I'll share with folks. I said there's a wide and a gap between the number of jobs available worldwide and the number for people to fill them. That's something that we know. And there's a productivity gap. And the numbers aren't showing up. We're not seeing bump-ups in productivity even though spending on technology is kind of through the roof. Robotic Process Automation is going to become a fundamental component of closing that gap because companies, as part of the digital process transformation, they want to automate. The market today is around a billion. We see it growing 10 x over the next five to seven years. We're going to have some analysts on today from Forester, we'll dig into that a little bit, they cover this market really, really closely. So, we're hearing a lot more about RPA. We heard it last week at Infor, Charles Phillips was a big proponent of this. UiPath has been in this business now for a few years. It came out of Romania. Daniel Dines, former Microsoft executive, very interesting fellow. First time I've seen him speak. We're going to meet him today. He is a techy. Comes on stage with a T-shirt, you know. He's very sort of thoughtful, he's talking about open, about culture, about having fun. Really dedicated to listening to customers and growing this business. He said, he gave us a data point that they went from nothing, just a couple of million dollars, two years ago. They'll do 140 million. They're doing 140 million now in annual reccurring revenue. On their way to 200. I would estimate, they'll probably get there. If not by the end of year, probably by the first quarter next year. So let's take look at some of the things that we heard in the keynote. We heard from customers. A lot of partners here. Seen a lot of the big SIs diving in. That's always a sign of big markets. What did you learn today at the keynotes? >> Yeah, Dave, first thing there is definitely, one of the push backs about automation is, "Oh wait what is that "going to do for jobs?" You touched on it. There's a lot of staff they threw out. They said that RPA can really bring, you know, 75% productivity improvement because we know productivity improvement kind of stalled out over all in the market. And, what we want to do is get rid of mundane tasks. Dave, I spent a long time of my career helping to get, you know, how to we get infrastructure simpler? How do we get rid of those routine things? The storage robe they said if you were configuring LUNs, you need to go find other jobs. If you were networking certain basic things, we're going to automate that with software. But there are things that the automation are going to be able to do, so that you can be more creative. You can spend more time doing some higher level functions. And that's where we have a skills gap. I'm excited we're going to have Tom Clancy, who you and I know. I've got his book on the shelf and not Tom Clancy the fiction author, but you know the Tom Clancy who has done certifications and education through storage and cloud and now how do we get people ready for this next wave of how you can do people and machines. One of my favorite events, Dave, that we ever did was the Second Machine Age with MIT in London. Talking about it's really people plus machines, is really where you're going to get that boom. You've interviewed Garry Kasparov on this topic and it's just fascinating and it really excites me as someone, I mean, I've lived with my computers all my life and just as a technologist, I'm optimistic at how, you know, the two sides together can be much more powerful than either alone. >> Well, it's an important topic Stu. A lot of the shows that we go to, the vendors don't want to talk about that. "Oh, we don't want to talk about displacing humans." UiPath's perspective on that, and we'll poke them a little on that is, "That's old news. "People are happy because they're replacing their 'mundane tasks.'" And while that's true, there's some action on Twitter. (mumbles name) just tweeted out, replying to some of the stuff that we were talking about here, in the hashtag, which is UiPathForward, #UiPathForward, "Automation displaces unskilled workers, "that's the crux of the problem. "We need best algorithms to automate re-training and "re-skilling of workers. "That's what we need the most for best socio-economic "outcomes, in parallel to automation through "algorithm driven machines," he's right. That gap, and we talked about this at 2MA, is it going to be a creativity gap? It's an education issue, it's an education challenge. 'Cause you just don't want to displace, unskilled workers, we want to re-train people. >> Right, absolutely. You could have this hollowing out of the market place otherwise, where you have really low paid workers on the one end, and you have really high-end creative workers but the middle, you know, the middle class workers could be displaced if they are not re-trained, they're not put forward. The World Economic Forum actually said that this automation is going to create 60-million net new jobs. Now, 60-million, it sounds like a big number, but it is a large global workforce. And, actually Dave, one of the things that really struck me is, not only do you have a Romanian founder but up on stage we had, a Japanese customer giving a video in Japanese with the subtitles in English. Not something that you typically see at a U.S. show. Very global, in their reach. You talked about the community and very open source focus of something we've seen. This is how software grows very fast as you get those people working. It's something I want to understand. They've got, the UiPath that's 2,000 customers but they've got 114,000 certified RPA developers. So, I'm like, okay, wait. Those numbers don't make sense to me yet, but I'm sure our guests are going to be able to explain them. >> And, so you're right about the need for education. I was impressed that UiPath is actually spending some of it the money that it's raised. This company, just did a monster raise, 225-million. We had Carl Ashenbach on in theCUBE studio to talk about that. Jeff Freck interviewed him last week. You can find that interview on our YouTube play list and I think on out website as well. But they invested, I think it was 10-million dollars with the goal of training a million students in the next three years. They've hired Tom Clancy, who we know from the old EMC education world. EMC training and education world. So they got a pro in here who knows to scale training. So that's huge. They've also started a 20-million investment fund investing in start ups and eco-system companies, so they're putting their money where their mouth is. The company has raised over 400-million dollars to date. They've got a 3-billion dollar evaluation. Some of the other things we've heard from the keynote today, um, they've got about 1,400 employees which is way up. They were just 270, I believe, last year. And they're claiming, and I think it's probably true, they're the fastest growing enterprise software company in history, which is kind of astounding. Like you said, given that they came out of Romania, this global company maybe that's part of the reason why. >> I mean, Dave, they said his goal is they're going to have 4,000 employees by 2019. Wait, there are a software company and they raised huge amounts of money. AS you said, they are a triple unicorn with a three billion dollar valuation. Why does a software company need so many employees? And 3,000, at least 3,000 of those are going to be technical because this is intricate. This is not push button simplicity. There's training that needs to happen. How much do they need to engage? How much of this is vertical knowledge that they need to get? I was at Microsoft Ignite two weeks ago. Microsoft is going really deep vertically because AI requires specialized knowledge in each verticals. How much of that is needed from RPA? You've got a little booklet that they have of some basic 101 of the RPA skills. >> I don't know if you can see this, but... Is that the right camera? So, it's this kind of robot pack. It's kind of fun. Kind of go through, it says, you got to reliable friend you can automate, you know, sending them a little birthday wish. They got QR codes in the back you can download it. You know, waiters so you can order online food. There's something called Tackle, for you fantasy football players who help you sort of automate your fantasy football picks. Which is kind of cool. So, that's fun. There's fun culture here, but really it's about digital transformation and driving it to the heart of process automation. Daniel Dines, talked about taking things from hours to minutes, from sort of accurate to perfectly accurate. You know, slow to fast. From very time consuming to automated. So, he puts forth this vision of automation first. He talked about the waves, main frames, you know the traditional waves client server, internet, etc. And then, you know I really want to poke at this and dig into it a little bit. He talked about a computer vision and that seemed to be a technical enabler. So, I'm envisioning this sort of computer vision, this visual, this ability to visualize a robot, to visualize what's happening on the screen, and then a studio to be able to program these things. I think those are a couple of the components I discerned. But, it's really about a cultural shift, a mind shift, is what Daniel talked about, towards an automation first opportunity. >> And Dave, one of the things you said right there... Three things, the convergence of computer vision, the Summer of AI, and what he meant by that is that we've lived through a bunch of winters. And we've been talking about this. And, then the business.. >> Ice age of a, uh... >> Business, process, automation together, those put together and we can create that automation first era. And, he talked about... We've been talking about automation since the creation of the first computer. So, it's not a new idea. Just like, you know we've been talking on theCUBE for years. You know, data science isn't a new thing. We sometimes give these things new terms like RPA. But, I love digging into why these are real, and just as we've seen these are real indicators, you know, intelligence with like, whether you call it AI or ML, are doing things in various environments that we could not do in the past. Just borders of magnitude, more processing, data is more important. We could do more there. You know, are we on the cusp of really automation. being able to deliver on the things that we've been trying to talk about a couple of generations? >> So a couple of other stats that I thought were interesting. Daniel put forth a vision of one robot for every person to use. A computer for every person. A chicken for every pot, kind of thing (laughs) So, that was kind of cool. >> "PC for every person," Bill Gates. >> Right, an open and free mind set, so he talked a about, Daniel talked about of an era of openness. And UiPath has a market place where all the automations. you can put automations in there, they're all free to use. So, they're making money on the software and not on the automation. So, they really have this... He said, "We're making our competitors better. "They're copying what we're doing, "and we think that's a good thing. "Because it's going to help change the world." It's about affecting society, so the rising tides lift all boats. >> Yeah Dave, it reminds me a lot of, you know, you look at GitHub, you look at Docker Hub. There's lots of places. This is where code lives in these open market places. You know, not quite like the AWS or IBM market places where you can you can just buy software, but the question is how many developers get in there. They say they got 250,000 community members already there. So, and already what do they have? I think hundreds of processes that are built in there, so that will be a good metric we can see to how fast that scales. >> We had heard from a couple of customers, and Wells Fargo was up there, and United Health. Mr. Yamomoto from SNBC, they have 1,000 robots. So, they are really completely transforming their organization. We heard from a partner, Data Robot, Jeremy Atchins, somebody who's been on theCUBE before, Data Robot. They showed an automated loan processing where you could go in, talk to a chat bot and within minutes get qualified for a loan. I don't know if you noticed the loan amount was $7,000 and the interest rate was 13.6% so the applicant, really, must not of had great credit history. Cause that's kind of loan shark rates, but anyway, it was kind of a cool demo with the back end data robot munging all the data, doing whatever they had to do, transferring through a CSV into the software robot and then making that decision. So, that was kind of cool, those integrations seemed to be pretty key. I want to learn more about that. >> I mean it reminds me of chat box have been hot in a lot of areas lately, as how we can improve customer support and automate things on infrastructure in the likes of, we'll see how those intersections meet. >> Yeah, so we're going to be covering this all day. We got technologists coming on, customers, partners. Stu and I will be jamming. He's @Stu and I'm @Dvellante. Shoot us any questions, comments. Thanks for the ones we've had so far. We're here at the Fontainebleau in Miami Beach. Pretty crazy hotel. A lot of history here. A lot of pictures of Frank Sinatra on the wall. Keep it right there, buddy. You're watching theCUBE. We'll be right back after this short break. (energetic music)

(upbeat electronic music) >> Live from Washington D.C. It's theCUBE. Covering Inforum D.C. 2018. Brought to you by Infor. >> And welcome back to Washington D.C. We're in the Washington Convention Center here for Inforum 2018, continuing the coverage here on theCUBE, I'm John Walls with Dave Vellante, we're joined now by Anne Benedict, who is the S.V.P. of human resources at Infor. Anne good afternoon to you. >> Thank you, thanks for having me. >> You bet, thanks for being here. Now, 17,000 employees, so obviously you've got a lot of responsibility there. You're not only an Infor Executive, but you wear the hat of being an Infor client, (laughs) as well. Tell us about that, and how that works out, and, I guess, how you can test drive a lot of different services on your own before it goes out to the market. >> I like to joke that I feel like I have the best HR leadership role in the business, or in the world perhaps, because I get to not only lead a great company full of great people, 17,000 employees around the world, I'm so proud of them, but then I also get to be a customer of one of the greatest products in the HCM world that there is, and I have a direct line to the product managers, to the developers, to the consultants who can really help us to use our product to it's fullest advantage internally for our selves. So, it's like a toy box that every H.R. executive dreams of, and it's right there at my door step to test, to use, to innovate with them. They're always open to our ideas, our feedback internally. We're often a beta customer for the features, and functionality that are coming out to our customers, so it's a great position to be in. >> So what about the relationship, because there is a great give and take. Obviously, because you are a tremendous resource on the development side. What is that exchange like, and how does that work in terms of what's working, what's not, what you think others would want instead, or what they'd like to tweak a little bit. How does that work? >> So, you know, we're trying to sort of straddle a balance between using the product as it's intended to be designed for the breadth of our customers, no matter what industry they're in. We're obviously in a technology industry, but we have a lot of health care customers, government customers, services customers who have their own particular needs. So, we like to experiment with the technology the way it's designed for other industries, but then also I can make adjustments for use for our own company as a services company, as a technology company, and a good example of that for example is I'm working very closely with product management right now to help them design the next iteration of what our talent management suite will look like. So, we have a design concept for how we want to give performance feedback, for example, internally at Infor, and we're sharing that design the product management team to help them create the next version of the product that will meet the design requirements that we've set out for ourselves, and that I think a lot of other companies are moving towards. It's a modern approach to talent management, and we're working very closely hand in hand with product management to make sure they're designing something that we, we're co-designing it with them really. So, what I'm expecting is for us to have a really great next iteration of that product that is very modern, and up to date on what science is telling us about performance feedback. >> So, you're a pioneer, in a way, but you probably don't want to mess with with core H.R., that's table stakes. Talent management is something that, frankly, not a lot of companies do well. So, you may be more receptive to experimentation there. Is that a fair assertion? >> Yeah, I would say that's true, and also my background is, I grew up in H.R. with quite a breadth of experiences, but my depth of expertise has always been on the talent management and leadership development side. So, that's been sort of where I've been wanting to play with the product, and give my point of view on where I think it should evolve. It's just my particular strength that I bring, I think, to this role and to the product as well. >> How do you see the role of the Senior H.R. Executive evolving? How has it changed in the last several years? How is, maybe, digital transformation, this whole big data, the data movement? How does that factor into that role, and your vision of where that goes? >> Yeah, I think companies are looking for a different type of H.R. Executive than they have in the past. And I was fortunate that this wasn't by design. It was very serendipitous, but my career path led me, I think, in the exact right direction. So, I started my first 10 years of my career as a consultant at Mercer doing H.R. consulting. So, I was consulting the companies how to make, how to create the best H.R. department possible, how to create H.R. strategy, how to operationalize that. And, it was that consulting mindset that I've taken with me throughout my career. After consulting I moved internally to various companies, and that skill set of just being able to identify a problem, come up with a solution, and measure an implementation, I've taken with me in my role. So, I think companies are looking for H.R. executives who bring that sort of mind set to the role. And, I think that's what I've been able to do at Infor. And then, I think also when I was a consultant I was also advising customers and clients on technology, and how to use technology for H.R., so that's why I'm so thrilled to have this role, because it's the best of both worlds where I get to play with the technology, and also be a cutting edge H.R. leader. >> Alright so-- >> Hopefully. >> How do you asses the Infor HCM capabilities? Come on, give us the good, the bad, what's on the to do list. You know, give us the rundown. >> Yeah, I think it's a phenomenal product, and I'm not just saying that. >> Okay, what makes it phenomenal? >> When I walked in the door a year and nine months ago we were just about to go live with the multi-tenant cloud product. We were one of the first to do that, and we did it in over 65 countries with 17,000 employees, and since then we have subsequently rolled out more functionality, benefits enrollment, absence management, compensation planning, LMS, and each time we learn a little bit more. I can't underestimate the importance of getting the process right before you get the technology in, and the change management that goes around it. If I would say, I would give us a B it might have been around those areas, but the product itself is really it has the perfect balance of coming out of the box with some functionality that you can use right away that's best practice process. >> So you get value right off the bat. >> Yeah, and not a lot of configuration required, easy to get in. We implemented it with that broad scope in a very, very short amount of time, which is almost impossible with our competitors, so. So, I think for that it's fantastic, and then for the specific needs that we've had it's been very easy to build that in as well, so it has best of both worlds I would say. >> So, we saw some pretty cool demos yesterday around talent science, and it struck me as an audience member. There were all kinds of different kinds of attributes of, you know, ambition and et cetera, et cetera, et cetera, but you know the one that wasn't on there was like performer, but it struck me that these attributes lead to performance. I guess that's the basic philosophy, but I wanted to test that with you. Just give me the bottom line. >> Yeah. >> But it really is more complicated than that, isn't it? >> It is, yeah, and that's one of the most exciting things about H.R. right now too, I think. And this comes back to H.R. Executive of the future is, I come from an IO Psychology background where data, we used to have to do experiments on subjects with, and collecting data was always the hardest part to studying work, and studying personalities, studying behavior, and now we have all this data available to us that we've never had before. And, talent science is a perfect example of how data is really empowering our decisions. And, to answer your question about how it is predicting performance; A particular attribute doesn't necessarily lead to performance in any role. So, in one role, ambition, really high ambition is actually not a factor for success. In another role, it is. So, it really is, there is no right personality profile that can predict success in any role. It's very role specific. And what talent science is able to do is really find the science behind what is the specific role that will lead to success, and what are the attributes that will lead to non-success, also in a role. And, that's such a powerful thing. What we've found with talent science is that depending on the role we can reduce turnover by 20 up to 70% by choosing people who fit a role profile versus those who don't. >> It's interesting it's like, you know, those books, like the seven attributes or-- >> Or Covey-- >> Of highly successful people, but essentially you're codifying that by role. And, that's true. It doesn't just work for any role. Sales person may be different than an engineer, may be different than a an operations person et cetera. >> So, this is really fascinating, because you have the human science, right, we're all imperfect, we make crazy decisions, sometimes irrational, we act wild, or predictably, whatever it is. And, now you're taking data science, and overlaying with that, so you're trying to come up with some kind of predictable markers, or whatever, for imperfect beings in a way. How's all that merging, I mean, how is technology being the glue in that process? >> Yeah, well I think there's no such thing as right and wrong, or perfect and imperfect. You know, I could get into a leadership speil, but any strength that either of you might have, if you use that to an extreme it becomes a weakness, actually. And, like I used in the example of ambition, high ambition in certain roles, may not be a factor toward success. Where as other roles it might be. Whatever particular DNA, behavioral DNA, that you bring to a role as an individual, it's incumbent upon us as a company to figure out what is the right role for the personality that you bring, and the behavior, and the strengths that you have. And, that's what we're really able to do with talent science, which is, so, if you apply for a role where you don't match the profile I may be able to propose to you, hey, you have really high ambition that's not right for this role, but it may be right for this other role. Have you ever considered that? And, that way we can really, you know, we talk about human potential here, at Inforum. That's the real tool, real tangible way that we can really find the human potential in every single person, no matter what their profile looks like, or strengths, or weaknesses, or faults, as you say. Whatever-- >> Whatever it is, right? >> Whatever they come with we can find the right fit. >> Does technology, generally, and say artificial intelligence or machine intelligence, specifically, can it moderate or adjudicate human bias? Or, does it actually reinforce it? >> Yeah, that's a very good question, and obviously very pertinent to today. I think, a couple of things. So, the assessment I'm speaking of, we would never rely on the machine to make a decision. So, it's telling you, as a manager, here are some of the gaps that a particular individual has towards the role that you are planning to hire them for, but we suggest that you ask these interview questions, and make a decision for yourself. So, you really can't replace that human intervention in the process, that human judgment, their sense from an interview, but it really helps them hone the interview in on the things that they really should focus on. Figuring out, are we comfortable with those gaps? Does the person realize they have those gaps? And, really, for both the candidate and the manager to make the right decision. So, in the assessment it's always, we never rely on the machine to make a decision. But, it is incumbent on us to make sure that as we're designing these tools, as we're designing the technology behind them that we have as much diversity in the people who are designing them as possible. To make sure they're being designed in a way that doesn't have bias built into them. And, that's why it's so important for us to have diversity in technology. Why we're doing SB code. Why we believe in bringing up people from all backgrounds to participate in technology, 'cause it's so important to have that diversity, as we're building this stuff. >> Can't take the humans out of the equation, yet. >> There's still some gut check, right, there's still some intuition that has to come into play here. >> Yeah, absolutely, and that's one of the attributes of humans that we, machines can't replace yet. So, that ability to empathize, the ability to show all the emotional skills, we know machines can't do that today, maybe someday they will. But, today they can't, so humans will bring that. But, I really think that the power comes in the combination of AI, and machines, and humans. And, that's what we're talking about here around human potential. It's the power of the combination of the two. And, I think we will see that that combination will be required for a very long time, before machines take over the world (laughs) >> I always tell the story, John and I interviewed Garry Kasparov. >> That was great. >> The great chess champion. >> Chess master. >> When he lost to the IBM supercomputer, instead of giving up he said, "I'm going to beat the supercomputer", so he took machines plus humans to beat the supercomputer, so to this day the greatest chess player in the world is a machine and a supercomputer. So, that is a great example of augmentation. Now, it probably doesn't work so well for autonomous vehicles, but-- (all laughing) >> Well now, thanks for being with us. Thanks for sharing the story. We appreciate that, the time. And, if you see our application come down the pike-- >> Okay (laughs) >> Flag us where we're deficient, if you would, please. >> You'll be welcome, you're welcome. >> Excellent (laughs) >> Thanks for having me. >> Thank you, Anne Benedict, thanks for being with us. We'll be back with more here on theCUBE. We're live in the nation's capitol, Washington D.C. >> That was awesome. >> Thank you. (upbeat electronic music)

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, its theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer event in San Francisco, Spring, 2018. About 100 people, predominantly practitioners, which is a pretty unique event. Not a lot of vendors, a couple of them around, but really a lot of people that are out in the wild doing this work. We're really excited to have a return guest. We last saw him at Spark Summit East 2017. Can you believe I keep all these shows straight? I do not. Alfred Essa, he is the VP, Analytics and R&D at McGraw-Hill Education. Alfred, great to see you again. >> Great being here, thank you. >> Absolutely, so last time we were talking it was Spark Summit, it was all about data in motion and data on the fly, and real-time analytics. You talked a lot about trying to apply these types of new-edge technologies and cutting-edge things to actually education. What a concept, to use artificial intelligence, a machine learning for people learning. Give us a quick update on that journey, how's it been progressing? >> Yeah, the journey progresses. We recently have a new CEO come on board, started two weeks ago. Nana Banerjee, very interesting background. PhD in mathematics and his area of expertise is Data Analytics. It just confirms the direction of McGraw-Hill Education that our future is deeply embedded in data and analytics. >> Right. It's funny, there's a often quoted kind of fact that if somebody came from a time machine from, let's just pick 1849, here in San Francisco, everything would look different except for Market Street and the schools. The way we get around is different. >> Right. >> The things we do to earn a living are different. The way we get around is different, but the schools are just slow to change. Education, ironically, has been slow to adopt new technology. You guys are trying to really change that paradigm and bring the best and latest in cutting edge to help people learn better. Why do you think it's taken education so long and must just see nothing but opportunity ahead for you. >> Yeah, I think the... It was sort of a paradox in the 70s and 80s when it came to IT. I think we have something similar going on. Economists noticed that we were investing lots and lots of money, billions of dollars, in information technology, but there were no productivity gains. So this was somewhat of a paradox. When, and why are we not seeing productivity gains based on those investments? It turned out that the productivity gains did appear and trail, and it was because just investment in technology in itself is not sufficient. You have to also have business process transformation. >> Jeff Frick: Right. >> So I think what we're seeing is, we are at that cusp where people recognize that technology can make a difference, but it's not technology alone. Faculty have to teach differently, students have to understand what they need to do. It's a similar business transformation in education that I think we're starting to see now occur. >> Yeah it's great, 'cause I think the old way is clearly not the way for the way forward. That's, I think, pretty clear. Let's dig into some of these topics, 'cause you're a super smart guy. One thing's talk about is this algorithmic transparency. A lot of stuff in the news going on, of course we have all the stuff with self-driving cars where there's these black box machine learning algorithms, and artificial intelligence, or augmented intelligence, bunch of stuff goes in and out pops either a chihuahua or a blueberry muffin. Sometimes it's hard to tell the difference. Really, it's important to open up the black box. To open up so you can at least explain to some level of, what was the method that took these inputs and derived this outpout. People don't necessarily want to open up the black box, so kind of what is the state that you're seeing? >> Yeah, so I think this is an area where not only is it necessary that we have algorithmic transparency, but I think those companies and organizations that are transparent, I think that will become a competitive advantage. That's how we view algorithms. Specifically, I think in the world of machine learning and artificial intelligence, there's skepticism, and that skepticism is justified. What are these machines? They're making decisions, making judgments. Just because it's a machine, doesn't mean it can't be biased. We know it can be. >> Right, right. >> I think there are techniques. For example, in the case of machine learning, what the machines learns, it learns the algorithm, and those rules are embedded in parameters. I sort of think of it as gears in the black box, or in the box. >> Jeff Frick: Right. >> What we should be able to do is allow our customers, academic researchers, users, to understand at whatever level they need to understand and want to understand >> Right. >> What the gears do and how they work. >> Jeff Frick: Right. >> Fundamental, I think for us, is we believe that the smarter our customers are and the smarter our users are, and one of the ways in which they can become smarter is understanding how these algorithms work. >> Jeff Frick: Right. >> We think that that will allow us to gain a greater market share. So what we see is that our customers are becoming smarter. They're asking more questions and I think this is just the beginning. >> Jeff Frick: Right. >> We definitely see this as an area that we want to distinguish ourselves. >> So how do you draw lines, right? Because there's a lot of big science underneath those algorithms. To different degrees, some of it might be relatively easy to explain as a simple formula, other stuff maybe is going into some crazy, statistical process that most layman, or business, or stakeholders may or may not understand. Is there a way you slice it? Is there kind of wars of magnitude in how much you expose, and the way you expose within that box? >> Yeah, I think there is a tension. The tension traditionally, I think organizations think of algorithms like they think of everything else, as intellectual property. We want to lock down our intellectual property, we don't want to expose that to our competitors. I think... I think that's... We do need to have intellectual property, however, I think many organizations get locked into a mental model, which I don't think is just the right one. I think we can, and we want our customers to understand how our algorithm works. We also collaborate quite a bit with academic researchers. We want validation from the academic research community that yeah, the stuff that you're building is in fact based on learning science. That it has warrant. That when you make claims that it works, yes, we can validate that. Now, where I think... Based on the research that we do, things that we publish, our collaboration with researchers, we are exposing and letting the world know how we do things. At the same time, it's very, very difficult to build an engineer, an architect, scalable solutions that implement those algorithms for millions of users. That's not trivial. >> Right, right, right. >> Even if we give away quite a bit of our secret sauce, it's not easy to implement that. >> Jeff Frick: Right. >> At the same time, I believe and we believe, that it's good to be chased by our competition. We're just going to go faster. Being more open also creates excitement and an ecosystem around our products and solutions, and it just makes us go faster. >> Right, which gives to another transition point, which would you talk about kind of the old mental model of closed IP systems, and we're seeing that just get crushed with open source. Not only open source movements around specific applications, and like, we saw you at Spark Summit, which is an open source project. Even within what you would think for sure has got to be core IP, like Facebook opening up their hardware spec for their data centers, again. I think what's interesting, 'cause you said the mental model. I love that because the ethos of open source, by rule, is that all the smartest people are not inside your four walls. >> Exactly. >> There's more of them outside the four walls regardless of how big your four walls are, so it's more of a significant mental shift to embrace, adopt, and engage that community from a much bigger accumulative brain power than trying to just trying to hire the smartest, and keep it all inside. How is that impacting your world, how's that impacting education, how can you bring that power to bear within your products? >> Yeah, I think... You were in effect quoting, I think it was Bill Joy saying, one of the founders of Sun Microsystems, they're always, you have smart people in your organization, there are always more smarter people outside your organization, right? How can we entice, lure, and collaborate with the best and the brightest? One of the ways we're doing that is around analytics, and data, and learning science. We've put together a advisory board of learning science researchers. These are the best and brightest learning science researcher, data scientists, learning scientists, they're on our advisory board and they help and set, give us guidance on our research portfolio. That research portfolio is, it's not blue sky research, we're on Google and Facebook, but it's very much applied research. We try to take the no-knowns in learning science and we go through a very quick iterative, innovative pipeline where we do research, move a subset of those to product validation, and then another subset of that to product development. This is under the guidance, and advice, and collaboration with the academic research community. >> Right, right. You guys are at an interesting spot, because people learn one way, and you've mentioned a couple times this interview, using good learning science is the way that people learn. Machines learn a completely different way because of the way they're built and what they do well, and what they don't do so well. Again, I joked before about the chihuahua and the blueberry muffin, which is still one of my favorite pictures, if you haven't seen it, go find it on the internet. You'll laugh and smile I promise. You guys are really trying to bring together the latter to really help the former. Where do those things intersect, where do they clash, how do you meld those two methodologies together? >> Yeah, it's a very interesting question. I think where they do overlap quite a bit is... in many ways machines learn the way we learn. What do I mean by that? Machine learning and deep learning, the way machines learn is... By making errors. There's something, a technical concept in machine learning called a loss function, or a cost function. It's basically the difference between your predicted output and ground truth, and then there's some sort of optimizer that says "Okay, you didn't quite get it right. "Try again." Make this adjustment. >> Get a little closer. >> That's how machines learn, they're making lots and lots of errors, and there's something behind the scenes called the optimizer, which is giving the machine feedback. That's how humans learn. It's by making errors and getting lots and lots of feedback. That's one of the things that's been absent in traditional schooling. You have a lecture mode, and then a test. >> Jeff Frick: Right. >> So what we're trying to do is incorporate what's called formative assessment, this is just feedback. Make errors, practice. You're not going to learn something, especially something that's complicated, the first time. You need to practice, practice, practice. Need lots and lots of feedback. That's very much how we learn and how machines learn. Now, the differences are, technologically and state of knowledge, machines can now do many things really well but there's still some things and many things, that humans are really good at. What we're trying to do is not have machines replace humans, but have augmented intelligence. Unify things that machines can do really well, bring that to bear in the case of learning, also insights that we provide. Instructors, advisors. I think this is the great promise now of combining the best of machine intelligence and human intelligence. >> Right, which is great. We had Gary Kasparov on and it comes up time and time again. The machine is not better than a person, but a machine and a person together are better than a person or a machine to really add that context. >> Yeah, and that dynamics of, how do you set up the context so that both are working in tandem in the combination. >> Right, right. Alright Alfred, I think we'll leave it there 'cause I think there's not a better lesson that we could extract from our time together. I thank you for taking a few minutes out of your day, and great to catch up again. >> Thank you very much. >> Alright, he's Alfred, I'm Jeff. You're watching theCUBE from the Corinium Chief Analytics Officer event in downtown San Francisco. Thanks for watching. (energetic music)

>> Announcer: From Milpitas, California at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick, here with theCUBE. We're in Milpitas, California, at Western Digital, at the Autotech Council Autonomous Vehicle Event. About 300 people, really deep into this space. It's a developing ecosystem. You know, we think about Tesla, that's kind of got a complete, closed system. But there's a whole ecosystem of other companies getting into the autonomous vehicle space, and as was mentioned in the keynote, there are, literally thousands of problems. A great opportunity for startups. So we're excited to have Oded Sagee, he's a senior director of product marketing from Western Digital. Oded, great to see you. >> Thank you very much, Jeff. >> So you were just on the panel and, really that was a big topic, is there are thousands of problems to solve and this ecosystem's trying to come together, but it's complicated, right? It's not just the big car manufacturers anymore, and the tier one providers, but there's this whole ecosystem that's now growing up to try to solve these problems. So what are you seeing from your point of view? >> Yes, correct. So, definitely in the past automotive was a tough market to play in, but it was simple from the amount of players and people you needed to talk to to design your product inside. With the disruption of connectivity, smart vehicles, even before autonomous, there are so many new systems in the car now that generate data or consume data. And so, for us, to kind of figure out what's the use case, right? How is this going to look in the future? Who's going to define it? Who's going to buy it? Who's going to pay for it? It has become more and more complex. Happily, storage is in the center of all this. >> Jeff: Right. >> So we get a seat at the table and everyone wants to talk to us, but yes, it's a very big ecosystem now. And trying to resolve that problem, it's going to take some time. >> So what are some of the unique characteristics, from a storage point of view, that you have to worry about? Obviously environmental jumps out. We had the guy on before talking about bumpy roads, you know, the huge impacts on vibration. And now you spent a lot of money for a Toughbook back in the day to put a laptop in a cop car, this is a whole other level of expense, investment, and data flow. >> Right. So, for us, I think with all this disruption happening of full autonomous, people are, very much focused on making that autonomous work, right? So, for them it's all about connectivity, it's all about the sensor, whether it's Lidar, or, you know, cameras. Just making that work, right? All the algorithms and the software. And so, for them storage, currently is an afterthought, right? They were saying, once we meet mass production we'll just go and buy some storage and everything's going to be fine. So while they're prototyping, right? They can use any storage that they want. But, if you think about a full autonomous vehicle out there driving, not two hours a day like we are driving today, right? 20 hours a day, from cold to hot, going through areas without connectivity. Suddenly, the storage requirements are very, very different. And this is what we're trying to drive and explain that, if we don't design the future storage solutions today, What's going to end up, is that people are going to pay much more for storage just to make a basic use case work. >> Right. >> But if we start working now, and I'm talking about five, seven years out, we can have affordable solutions to make those business models work. >> And is that resonating in the industry, or are they just too focused on, you know, better cameras? >> It definitely does, but as companies change, right? So let's just take the car makers for a second. They didn't necessarily have a CTO in place, right? To drive engineering and semi-conductor. So you got to find those figures, and you got to start working and educating them. It definitely resonates if you have the right person. Once you find him, yes, it's on the list of priority. So we need to push. But it is happening. Yes, it is resonating. >> And it's so different because you do have this edge case. You have so much data being collected out in the field, if you will, within that vehicle. Some, to go back to the cloud, but you've got latency is always an issue, right? For safety. So, a little different storage challenge. So are there significant design thoughts that you guys are bringing into play on why this is so different and what is it going to take to really have kind of an optimal solution for autonomous vehicles? >> Yes, definitely there are a couple of vectors I would say, or knobs we need to work on. One of them is temperature. So, again vehicles do tend to go between hot and cold. Unlike many other components that just need to make sure that they operate between hot and cold, we actually have a big challenge on keeping data being accurate between hot and cold. So if you program cold and read hot and vice versa, data gets corrupted. >> Oh, even within the structures within the media? >> Yes. >> Okay. >> And people don't know that. So, for us to figure out, what's the temperature range that the car, through its lifetime, is going to go through. And make sure that we meet the use case, that's a big one. What we call the endurance on the cycling of the storage, again, if you cannot rely on connectivity, cannot rely on cloud because of latency, you need to record a lot of data in the car. So, again, a car drives for seven years, 15 years, and you want to record constantly, how much do you need to record? We don't necessarily have the technology today to meet that use case and we need to work with the ecosystem, in figuring it out. So these are just two examples. >> And I would imagine clean power, as you're saying these things, but they can need others. You're not in daddy's data center anymore. This is a pretty harsh environment, I would imagine. >> Very harsh. >> Ugly power, inconsistent power, turning off the car before everything is spun down. There's all kinds of little, kind of environmental impacts in that whole realm that you would never think of in, kind of a typical data center, for instance. >> Correct. And even, you touched power, that's very interesting because even some people think, oh, there's not power limitation in a car. You can just enjoy how much power you want. Actually, it's very, very sensitive. The battery, if you think about an EV car now has so many components to run and so even the power consumption, right? Just the energy that you need to consume is becoming critical for each, and every component >> in the vehicle. >> Right. And it's everybody's AI comparison, right? Is if Kasparov had to fight the computer with the same amount of power, it wouldn't have been much of a match. So the power to run all this AI stuff is not insignificant, so it is going to be a huge drain on these electric vehicles. Pretty exciting times. So when you get up in the morning, what's the biggest thing, when you talk to people about autonomous vehicles, that they just don't get? That people should really be thinking about. >> Yeah, so it goes back to some of the things we've discussed. Definitely, again, we're seeing the use cases change. We are working again with the broad ecosystem to explain the fundamental challenges that we have, right? What is our design cycle? What are the challenges that we have? So we start with educating the ecosystem, so they know what we have. And from that we trigger a discussion because they realize, oh, okay, because I do have a use case that, probably, you don't have a solution for, how do we go together? And we're doing it across the board. It's not only happening in automotive. It's happening in surveillance. It's happening in the home space. A lot of people don't know, but the home space, if you think about it, again, set-top boxes used to be huge, sat outside in the room. People are moving to these sticks, right? And they're behind the TV and they have no ventilation and they're small and they record all the time. And they get to temperatures that we've never seen in the past. So we even need to educate the telcos of the world, the set-top box makers. Everything is changing. Automotive is definitely ahead in a lot of innovation and disruption, but everything is changing for us. >> Right, a lot of those are fond of just the bright shiny object that everybody can see, right? We can't necessarily see a lot of IOT that GE's putting in to connect their factories. Alright, Oded, well thanks for taking a few minutes out of your busy day and I really appreciate the insight. >> Thank you very much. >> All right, he's Oded, I'm Jeff, You're watching theCUBE from Western Digital at The Autonomous Vehicle Event for the Autotech Council. Thanks for watching. Catch you next time. (electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)

>> Announcer: Live from San Jose. It's theCUBE. Presenting big data, Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Hello everyone, I'm John Furrier, co-host of theCube. I'm here with Dave Vellante, my co-host. Exclusive conversation around the role of data, data for good and bad. We always cover the role of data. We used to talk about AI and data for good but in this exclusive interview... And we have some exclusive material about data for bad. Dave, we've been talking about weaponizing data a year ago in SiliconEAGLE in theCUBE, around how data is being weaponized, and certainly in the elections. We know the Russians were involved. We know that data, you can buy journalists, you can create fake news. And for every click-bate and fake news is bad content. But also on the other side of this, there's good bate; good news. So the world's changin'. There needs to be a better place, needs to be some action taken, because there's now evidence that the role that the Russians had, using fake news and weaponizing it to sway the election and other things has been out there. So this is somethin' that we've been talkin' about. >> Yeah I mean the signature of the hacks is pretty clear. I think there is a distinct signature when you talk to the experts of when it's China or when it's Russia. Russia, very clever, about the way they target somebody whose maybe a pawn; but they try to make him or her feel like a king, grab their credentials and then work their way in. They've been doing this for decades, right? >> And the thing is to, is that now it's not just state-sponsored, there's new groups out there that they can enable open source tools. We report on theCUBE that terrorist organizations and bad actors, are taking open source tools and threats from state nations, posing as threats to democracy in the U.S. and other countries. This is a huge problem. >> And it's, in a way, it's harder than the nuclear problem. We had weapons pointed at each other, right. This is... The United States has a lot to lose. If we go on the offense, others can attack us and attack our systems, which are pretty mature. So, recently we talked to Garry Kasparov. I had an exclusive interview with him. He's very outspoken. Kasparov is the greatest chess player in history, by most accounts. And he is a political activist, he's an author. And he had a number of things to say about this. Let's listen to him, it's about a couple minute clip, and then we'll come back and talk about it. Watch this. >> Garry: Knowing Vladimir Putin and the mentality of the KGB mentality and the way he has been approaching the global problems; I had no doubt that the question was not if Putin would attack somewhere, but the question is when and where? And the attack on U.S. democracy was a surprise here but it was not surprise for us because we could see how they built these capabilities for more than a decade. Because they have been creating fake news industry in Russia to deal with Russian opposition 2004, 2005. Then they used against neighboring countries like Estonia in 2007. Then they moved to eastern Europe and then through western Europe. So when they ended up attacking the United States, they would've had almost a decade of experience. And it's quite unfortunate that, while there was kind of information about this attacks, the previous administration decided just to take it easy. And the result is that we have this case of interference; I hope there will be more indictments. I hope we'll get to the bottom of that. Because, we know that they are still pretty active in Europe. And they will never seize there-- >> Dave: Germany, France-- >> Garry: Exactly. But it's... I call Putin as: merchant of doubt. Because, unlike Soviet propaganda machine, he's not selling one ideology. All he wants is to spread chaos. So that's why it's not about and, oh this is the only, the right teaching. No, no, no. No, it's wrong, it's wrong, everything... Yeah, maybe there are 10 different ways of saying the truth. Truth is relevant. And that's a very powerful message because it's spreading these doubts. And he's very good in just creating these confusions and actually, bringing people to fight each other. And I have to say he succeeded-- >> Dave: Our president is taken a page out of that. Unfortunately. But I also think the big issue we face as a country, in the United States, is 2020. Is the election in 2020 is going to be about who leverages social media and the weaponization of social media. And the Russian attackers you talk to the black hats, very sophisticated, very intriguing how they come in, they find the credentials-- >> Garry: But look, we know, Jesus, every expert knows that in this industry, if you are trying to defend yourself, if you are on the defense all the time you will lose. It's a losing proposition. So the only way to deter the aggression is to make sure that they won't be counterattacks. So that there will be devastating blows, those who are attacking the United States. And you need the political will because, technology is here; America is still the leading power in the world. But the political will, unfortunately-- >> Dave: However, I would say that, but it's different than with nuclear warheads. Robert Gates was on theCUBE, he said to me, and I asked him about offense versus defense. He said the only thing about the Unite States is we have a lot to lose. So we have to be careful. (laughter) How aggressive we can be. >> Garry: No, exactly. That is just, it's, yes. It's a great error of uncertainty: what can you lose? If you show strength. But I can tell you exactly how you are going to lose everything, if you are not-- >> Dave: Vigilant. >> Garry: If you are not vigilant. If you are not deterrent. If you are not sending the right signal to the Putins of this world that aggression against America will have the price that you cannot bear. >> So John, pretty unequivocal comments from Garry Kasparov. So a lot of people don't believe that you can actually manipulate social media that way. You've been in social for a long time, since the beginning days. Maybe you could explain how one, would a country or a state sponsored terrorism; how would they go about manipulating individuals? >> You know Dave, I've been involved in internet infrastructure from the beginning days of Web 1.0 and through search engines. Student of the data. I've seen the data. I've seen our, the data that we have from our media company. I've seen the data on Facebook and here's the deal: there's bad actors doin' fake news, controlling everything, creating bad outcomes. It's important for everyone to understand that there's an actual opposite spectrum. Which is the exact opposite of the bad; there's a good version. So what we can learn from this is that there's a positive element of this, if we can believe it, which is actually a way to make it work for good. And that is trust, high-quality data, reputation and context. That is a very hard problem. Facebook is tryin' to solve it. You know we're workin' on solving that. But here's the anatomy of the hack. If you control the narrative, you can control the meme. If you can control the meme, you can control the idea. If you can control the idea, you can control the belief system. If you can control the belief system, you can control the population. That is exactly what has happened with the election. That is what's happening now in social networks. That's why so many people are turning off to social networks. Because this is hackable; you can actually hack the brains and outcomes of people. Because, controlling the narrative, controlling the meme, controlling the idea, controlling the belief system: you can impact the population. That has absolutely been done. >> Without firin' a shot. >> Without firing a shot. This is the new cold social network wars that are goin' on. And again, that has been identified, but there's an opposite effect. And the opposite effect is having a trust system, a short cut to trust; there will be a Google in our future, Google, like what Google did to search engines. It will be for social networks. That is, whoever can nail the trust, reputation, context: what is real and what is not. Will ultimately have all the users goin' to their doorstep. This is the opportunity for news organizations, for platforms and it's all going to be driven by new infrastructure, new software. This is something we can learn from. But there is a way to hack, it's been done. I've just laid it out. That's what's happening. >> Well, blockchain solved or play a role in solving this problem of reputation in your opinion. >> Well you know that I believe centralized is bad. 'Cause you can hack a centralized database and the data. Ownership is huge. I personally believe that blockchain and this notion of decentralized data ownership will ultimately go back to the people and that the decentralized applications and cryptocurrency leads a path, it's not yet proven, there's no clear visibility yet. But many believe that the wallet is a new browser and that cryptocurrency can put the power to the people; so that new data can emerge. To vet in a person who says they're something that they're not. News that says they're somethin' that they're not. This is a trust. This is something that is not yet available. That's what I'm sayin'. You can't get it with Google, you can't get it with Facebook. You can't get it in these platforms. So the world has to change at an infrastructure level. That's the opportunity to blockchain. Aside from all the things like who's going to give the power for the miners; a variety of technical issues. But conceptually, there is a path there. That's a new democracy. This is global phenomenon. It's a societal change. This is so cutting edge, but it's yet very promising at the same time. >> This is super important because I can't tell you how many times have you've received an email from one political persuasion or the other that lays out emphatically, that this individual did that or... And you do some research and you find out it's fake news. It happens all the time. >> There's no context for these platforms. Facebook optimizes their data for advertising optimization and you're going to see data being optimized for user control, community control, community curation. More objective not subjective data. This is the new algorithm, this is what machine learning in AI will make a difference. This is the new trust equation that will emerge. This is a phenomenal opportunity for entrepreneurs. If you're in the media business and you're not thinking about this, you will be out of business. That's our opinion. >> Excellent John. Well thanks for your thoughts and sharing with us how these hacks are done. This is real. The midterm elections, 2020 is really going to be won or lost on social media. Appreciate that. >> And Facebook's fumbling and they're going to try to do good. We'll see what they do. >> Alright. >> Alright. >> That's a wrap. Good job. >> Thanks for watching.

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

>> Announcer: Live from New York, 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 Garry Kasparov a little bit later on. It's going to be fascinating. Dave, glad you're here. Dave, good to see you, sir. >> John, always a pleasure. >> How you been? >> Up from DC, you know, I was in your area last week doing some stuff with John Furrier, but I've been great. >> Stopped by the White House, drop in? >> You know, I didn't this time. No? >> No. >> Dave: My son, as you know, goes to school down there, so when I go by my hotel, I always walk by the White House, I wave. >> Just in case, right? >> No reciprocity. >> 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 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 is creating. >> 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. The Big Data theme was just coming in, and it appeared, everybody was very excited. Still excited, obviously, about the data-driven concept. But we're now entering a new era. It's like every 10 years, the parlance in our industry changes. 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 the late innings for those. I think the industry is changing. The describers of what we're entering are autonomous, pervasive, self-healing, intelligent. 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. We're trying to still figure out the names. To me, it's an indicator that things are changing. It's early innings now. 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. That's something that I want to poke at with the folks at IBM and our guests today. How are people going to build 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. >> You said, every industry's 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. >> That's right. Digital means data, data is at the center of this transformation. A colleague of mine, David Moschella, 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, and Facebook, and others, they're building services on top of this matrix. The matrix is comprised of vertical slices by industry and horizontal slices of technology. 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. 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've got a bunch of great, hardworking people, embedded legacy. You feel good about where you are. And now you're coming to that workforce and saying, "Here's a new hat." >> I think that'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, 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. The big question is, how do they close that gap? You're absolutely right. The key is skillsets, and the skills have to be, you know, 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 hypertargeting. 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 their guests, with a lot of our guests, that really are kind of futurists and have thought through, I think, the changes that are coming. >> You can't have a DH anymore, right, that's what you're saying? You need a guy that can play the field. >> Not only play the field, not only a utility player, but somebody who's a utility player, but great. Best of breed at all these different skillsets. >> Machine learning, we haven't talked much about that, and another term, right, that certainly has different definitions, but certainly real 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 24/7. >> Yeah, so, real quick, I know we're tight on time here. 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 auto-didactic, self-learning, in a manner. Those are just the 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. >> 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. (fast electronic music)

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

>> Announcer: Live from Las Vegas, it's theCUBE covering HPE Discover 2017, brought to you by Hewlett Packard Enterprise. >> Okay, welcome back everyone. We're live here in Las Vegas for SiliconANGLE's CUBE coverage of HPE Discover 2017. This is our seventh year of covering HPE Discover Now. HPE Discover in its second year. I'm John Furrier, my co-host Dave Vellante. We've got two great guests, two doctors, PhD's in the house here. So Eng Lim Goh, VP and SGI CTO, PhD, and Tuomas Sandholm, Professor at Carnegie Mellon University of Computer Science and also runs the marketplace lab over there, welcome to theCube guys, doctors. >> Thank you. >> Thank you. >> So the patient is on the table, it's called machine learning, AI, cloud computing. We're living in a really amazing place. I call it open bar and open source. There's so many new things being contributed to open source, so much new hardware coming on with HPE that there's a lot of innovation happening. So want to get your thoughts first on how you guys are looking at this big trend where all this new software is coming in and these new capabilities, what's the vibe, how do you look at this. You must be, Carnegie Mellon, oh this is an amazing time, thoughts. >> Yeah, it is an amazing time and I'm seeing it both on the academic side and the startup side that you know, you don't have to invest into your own custom hardware. We are using HPE with the Pittsburgh Supercomputing Center in academia, using cloud in the startups. So it really makes entry both for academic research and startups easier, and also the high end on the academic research, you don't have to worry about maintaining and staying up to speed with all of the latest hardware and networking and all that. You know it kind of. >> Focus on your research. >> Focus on the research, focus on the algorithms, focus on the AI, and the rest is taken care of. >> John: Eng talk about the supercomputer world that's now there, if you look at the abundant computer intelligent edge we're seeing genome sequencing done in minutes, the prices are dropping. I mean high performance computing used to be this magical, special thing, that you had to get a lot of money to pay for or access to. Democratization is pretty amazing can I just hear your thoughts on what you see happening. >> Yes, Yes democratization in the traditional HPC approach the goal is to prediction and forecasts. Whether the engine will stay productive, or financial forecasts, whether you should buy or sell or hold, let's use the weather as an example. In traditional HPC for the last 30 years what we do to predict tomorrows weather, what we do first is to write all the equations that models the weather. Measure today's weather and feed that in and then we apply supercomputing power in the hopes that it will predict tomorrows weather faster than tomorrow is coming. So that has been the traditional approach, but things have changed. Two big things changed in the last few years. We got these scientists that think perhaps there is a new way of doing it. Instead of calculating your prediction can you not use data intensive method to do an educated guess at your prediction and this is what you do. Instead of feeding today's weather information into the machine learning system they feed 30 years everyday, 10 thousand days. Everyday they feed the data in, the machine learning system guess at whether it will rain tomorrow. If it gets it wrong, it's okay, it just goes back to the weights that control the inputs and adjust them. Then you take the next day and feed it in again after 10 thousand tries, what started out as a wild guess becomes an educated guess, and this is how the new way of doing data intensive computing is starting to emerge using machine learning. >> Democratization is a theme I threw that out because I think it truly is happening. But let's get specific now, I mean a lot of science has been, well is climate change real, I mean this is something that is in the news. We see that in today's news cycle around climate change things of that as you mentioned weather. So there's other things, there's other financial models there's other in healthcare, in disease and there's new ways to get at things that were kind of hocus pocus maybe some science, some modeling, forecasting. What are you seeing that's right low hanging fruit right now that's going to impact lives? What key things will HPC impact besides weather? Is healthcare there, where is everyone getting excited? >> I think health and safety immediately right. Health and safety, you mentioned gene sequencing, drug designs, and you also mentioned in gene sequencing and drug design there is also safety in designing of automobiles and aircrafts. These methods have been traditionally using simulation, but more and more now they are thinking while these engines for example, are flying can you collect more data so you can predict when this engine will fail. And also predict say, when will the aircraft lands what sort of maintenance you should be applying on the engine without having to spend some time on the ground, which is unproductive time, that time on the ground diagnosing the problems. You start to see application of data intensive methods increased in order to improve safety and health. >> I think that's good and I agree with that. You could also kind of look at some of the technology perspective as to what kind of AI is going to be next and if you look back over the last five to seven years, deep learning has become a very hot part of machine learning and machine learning is part of AI. So that's really lifted that up. But what's next there is not just classification or prediction, but decision making on top of that. So we'll see AI move up the chain to actual decision making on top of just the basic machine learning. So optimization, things like that. Another category is what we call strategic reasoning. Traditionally in games like chess, or checkers and now Go, people have fallen to AI and now we did this in January in poker as well, after 14 years of research. So now we can actually take real strategic reasoning under imperfect information settings and apply it to various settings like business strategy optimization, automated negotiation, certain areas of finance, cyber security, and so forth. >> Go ahead. >> I'd like to interject, so we are very on it and impressed right. If we look back years ago IBM beat the worlds top chess player right. And that was an expert system and more recently Google Alpha Go beat even a more complex game, Go, and beat humans in that. But what the Professor has done recently is develop an even more complex game in a sense that it is incomplete information, it is poker. You don't know the other party's cards, unlike in the board game you would know right. This is very much real life in business negotiation in auctions, you don't quite know what the other party' thinking. So I believe now you are looking at ways I hope right, that poker playing AI software that can handle incomplete information, not knowing the other parties but still able to play expertly and apply that in business. >> I want to double down on that, I know Dave's got a question but I want to just follow this thread through. So the AI, in this case augmented intelligence, not so much artificial, because you're augmenting without the perfect information. It's interesting because one of the debates in the big data world has been, well the streaming of all this data is so high-velocity and so high-volume that we don't know what we're missing. Everyone's been trying to get at the perfect information in the streaming of the data. And this is where the machine learning if I get your point here, can do this meta reasoning or this reasoning on top of it to try to use that and say, hey let's not try to solve the worlds problems and boil the ocean over and understand it all, let's use that as a variable for AI. Did I get that right? >> Kind of, kind of I would say, in that it's not just a technical barrier to getting the big data, it's also kind of a strategic barrier. Companies, even if I could tell you all of my strategic information, I wouldn't want to. So you have to worry not just about not having all the information but are there other guys explicitly hiding information, misrepresenting and vice versa, you doing strategic action as well. Unlike in games like Go or chess, where it's perfect information, you need totally different kinds of algorithms to deal with these imperfect information games, like negotiation or strategic pricing where you have to think about the opponents responses. >> It's your hairy window. >> In advance. >> John: Knowing what you don't know. >> To your point about huge amounts of data we are talking about looking for a needle in a haystack. But when the data gets so big and the needles get so many you end up with a haystack of needles. So you need some augmentation to help you to deal with it. Because the humans would be inundated with the needles themselves. >> So is HPE sort of enabling AI or is AI driving HPC. >> I think it's both. >> Both, yeah. >> Eng: Yeah, that's right, both together. In fact AI is driving HPC because it is a new way of using that supercomputing power. Not just doing computer intensive calculation, but also doing it data intensive AI, machine learning. Then we are also driving AI because our customers are now asking the same questions, how do I transition from a computer intensive approach to a data intensive one also. This is where we come in. >> What are your thoughts on how this affects society, individuals, particularly students coming in. You mentioned Gary Kasparov losing to the IBM supercomputer. But he didn't stop there, he said I'm going to beat the supercomputer, and he got supercomputers and humans together and now holds a contest every year. So everybody talks about the impact of machines replacing humans and that's always happened. But what do you guys see, where's the future of work, of creativity for young people and the future of the economy. What does this all mean? >> You want to go first or second? >> You go ahead first. (Eng and Tuomas laughing) >> They love the fighting. >> This is a fun topic, yeah. There's a lot of worry about AI of course. But I think of AI as a tool, much like a hammer or a saw So It's going to make human lives better and it's already making human lives better. A lot of people don't even understand all the things that already have AI that are helping them out. There's this worry that there's going to be a super species that's AI that's going to take over humans. I don't think so, I don't think there's any demand for a super species of AI. Like a hammer and a saw, a hammer and a saw is better than a hammersaw, so I actually think of AI as better being separate tools for separate applications and that is very important for mankind and also nations and the world in the future. One example is our work on kidney exchange. We run the nationwide kidney exchange for the United Network for Organ Sharing, which saves hundreds of lives. This is an example not only that saves lives and makes better decisions than humans can. >> In terms of kidney candidates, timing, is all of that. >> That's a long story, but basically, when you have willing but incompatible live donors, incompatible with the patient they can swap their donors. Pair A gives to pair B gives to pair C gives to pair A for example. And we also co-invented this idea of chains where an altruist donor creates a while chain through our network and then the question of which combination of cycles and chains is the best solution. >> John: And no manual involvement, your machines take over the heavy lifting? >> It's hard because when the number of possible solutions is bigger than the number of atoms in the universe. So you have to have optimization AI actually make the decisions. So now our AI makes twice a week, these decisions for the country or 66% of the transplant centers in the country, twice a week. >> Dr. Goh would you would you add anything to the societal impact of AI? >> Yes, absolutely on the cross point on the saw and hammer. That's why these AI systems today are very specific. That's why some call them artificial specific intelligence, not general intelligence. Now whether a hundred years from now you take a hundred of these specific intelligence and combine them, whether you get an emergent property of general intelligence, that's something else. But for now, what they do is to help the analyst, the human, the decision maker and more and more you will see that as you train these models it's hard to make a lot of correct decisions. But ultimately there's a difference between a correct decision and, I believe, a right decision. Therefore, there always needs to be a human supervisor there to ultimately make the right decision. Of course, he will listen to the machine learning algorithm suggesting the correct answer, but ultimately the human values have to be applied to decide whether society accepts this decision. >> All models are wrong, some are useful. >> So on this thing there's a two benefits of AI. One is a this saves time, saves effort, which is a labor savings, automation. The other is better decision making. We're seeing the better decision making now become more of an important part instead of just labor savings or what have you. We're seeing that in the kidney exchange and now with strategic reasoning, now for the first time we can do better strategic reasoning than the best humans in imperfect information settings. Now it becomes almost a competitive need. You have to have, what I call, strategic augmentation as a business to be competitive. >> I want to get your final thoughts before we end the segment, this is more of a sharing component. A lot of young folks are coming in to computer science and or related sciences and they don't need to be a computer science major per se, but they have all the benefits of this goodness we're talking about here. Your advice, if both of you could share you opinion and thoughts in reaction to the trend where, the question we get all the time is what should young people be thinking about if they're going to be modeling and simulating a lot of new data scientists are coming in some are more practitioner oriented, some are more hard core. As this evolution of simulations and modeling that we're talking about have scale here changes, what should they know, what should be the best practice be for learning, applying, thoughts. >> For me you know the key thing is be comfortable about using tools. And for that I think the young chaps of the world as they come out of school they are very comfortable with that. So I think I'm actually less worried. It will be a new set of tools these intelligent tools, leverage them. If you look at the entire world as a single system what we need to do is to move our leveraging of tools up to a level where we become an even more productive society rather than worrying, of course we must be worried and then adapt to it, about jobs going to AI. Rather we should move ourselves up to leverage AI to be an even more productive world and then hopefully they will distribute that wealth to the entire human race, becomes more comfortable given the AI. >> Tuomas your thoughts? >> I think that people should be ready to actually for the unknown so you've got to be flexible in your education get the basics right because those basics don't change. You know, math, science, get that stuff solid and then be ready to, instead of thinking about I'm going to be this in my career, you should think about I'm going to be this first and then maybe something else I don't know even. >> John: Don't memorize the test you don't know you're going to take yet, be more adaptive. >> Yes, creativity is very important and adaptability and people should start thinking about that at a young age. >> Doctor thank you so much for sharing your input. What a great world we live in right now. A lot of opportunities a lot of challenges that are opportunities to solve with high performance computing, AI and whatnot. Thanks so much for sharing. This is theCUBE bringing you all the best coverage from HPE Discover. I'm John Furrier with Dave Vellante, we'll be back with more live coverage after this short break. Three days of wall to wall live coverage. We'll be right back. >> Thanks for having us.

>> Narrator: It's theCube, covering Sapphire Now 2017, brought to you by SAP Cloud Platform and Hana Enterprise Cloud. >> Hi, welcome to theCube, I'm Lisa Martin, with my cohost George Gilbert, we are covering SAP Sapphire Now 2017. George, we've just watched the keynote, the very dynamic keynote with quite a few characters, I want to get your take on some of the things we heard in the keynote today, Bill McDermot kicked it off very lively, one of the first things that was interesting to me, and I'd love to get your opinion, that the journey to the club requires empathy and transparency. It's not often something that we hear from an CEO. What were your thoughts on his vision as to what SAP is doing around empathy and transparency. >> I guess I would take it in the soft skills that it might have been intended which was, empathy in that there's going to be changed management, not just because you're moving the operational capabilities from on-prem to the cloud, but because you're exposing new capabilities that will impact how people do their jobs. And transparency I think is part of the program of migration where you're going to break some things as you move them, and this is going to call out in the process of migration what few things you need to change. I think that's what he meant by transparency, because it's not a complete seamless lift and shift. >> Definitely. I think another thing that kind of jumped to mind is that, not only are these firsts changing, they talked about the digital core and the essential elements of that, but also the fact that they are listening to their customers, customers saying we want transparency, we want to see how things are going like you said, it's not a lift and shift, we need to get more understanding, but I think the undertone of we're listening to our customers was quite strong, when they talked about the new SAP Cloud Trust Center, that seemed to really bring it home in terms of what he was talking about, where not just customers of SAP, but that they're using Hana, can see what's happening within their cloud infrastructures, but also people who aren't using it yet, so really broadening transparency to foster new customers, and acquiring new customers going forward. >> Yes, I guess with the transparency, the footprint for enterprise applications is just growing and growing, and he talked about at one point, we're not just talking to the CIO, the CEO has to be involved, the head of sales, head of procurement, head of supply chain, and I think it is related to the idea of the digital core, and then the what they call the sort of win applications around them, which is the core where the traditional systems of record and the win, they're like the AI in machine learning and Internet of Things and Blockchain, these are strategic new capabilities that enable applications, not just about efficiency, but about opening up new business models, new product and service lines, things like that. >> And they talked about, you mentioned, they talked about openness as the game changer with the nucleus of a digital enterprise being that digital core. You talked about machine learning, AI, blockchain, give us a little bit of an insight as to this expansion of Leonardo, they talked a lot about Leonardo, what were some of the things that really stuck out in your mind as the new capabilities, and who's their audience here. >> Okay, great questions, because their audience is not the typical, their typical buyer was the CFO, because it cost so much, so he had to be involved. IT, the CIO, because he had to sort of standardize the infrastructure on which it ran. And then between the two of them, they were essentially putting in a platform for business process efficiency, and that's what they called the core, and then Leonardo is now the win that surrounds that And that has, they see that having transformational capabilities, and that impacts then not just the departments that were looking for efficiency, but looking for transformation, so that's why they have to get involved, the head of sales, the head of procurement, supply chain, things like that. It's a different sell, just to offer an example, the best description I ever heard for trying to sell enterprise software is like trying to get a bill through both houses of congress, and congress just got a lot bigger. >> So from a target audience perspective, we know that they work with small medium sized businesses, Enterprise, we had Google on stage, they're partnering with Apple, with Facebook, etc, looking at Leonardo, from a target audience perspective, are they talking to mostly the large enterprise north of 1500 employees? >> Those customers come first, because they always have the more sophisticated, greater number of more sophisticated skillsets in place, and as these systems mature from the early adopters, they work the kinks out they're able to generalize things better, and then it's more easily absorbed into the main stream. McDermot said something interesting, which was you're either an early adopter or an also ran. I think he's trying to motivate people to get started, but the adoption curve doesn't really change just because we're doing more advanced technologies. >> One of the things that interested me, is if you look at a small to medium business, and they mentioned a number of businesses, Mod Pizza for example, during the intro, and there's a great video about them on their website, but if you look at an SMB or SMBE about, as a competitor, they're much smaller, typically, much more agile, much more nimble, that was one of the things I was sort of expecting to hear in some sense in the keynote about the small enterprises really becoming the disruptors because they can react and move faster than a larger legacy incumbent. What were your thoughts there? >> In Tech we look at the smaller to mid sized companies as being more nimble, but that's changed in the last few years, where the big incumbents, the rich just get richer, partly because, partly because they have these data assets that they can keep turning into newer and newer products. That may change in the next few years, but right now, the more data you have the more your advantage. And the capital intensity is for the most part so low that they can use all their profits just to buy the little guys who look promising. That's in tech, outside tech, I think the answer to your question will be, how easy can SAP make it to absorb and install and implement and run their system. In the past it was so flexible that you really needed extremely sophisticated implementation advice to get it up and running. If they've taken that out and simplified it, and made it like just, you know, configure these buttons, then that would make a difference. I'm not sure we have seen the answer to that yet. >> Okay, playing on the incumbency theme if you will. Google, Diane Green was on stage, and, at Google Cloud Nexus just a couple of months ago here in San Francisco, they announced a partnership with SAP to deliver Hanna on Google Cloud platform, and today they talked about kind of the expansion of that, they had a customer, a consulting agency that was their proof in the pudding. And one of the things Bill McDermot did say was we are now partnering with Apple with Facebook with Google, so they're talking about some of these incumbents, looking at Google as an incumbent, but also as a competitor of Microsoft Azure, of AWS who SAP also works with, what was your take on the conversation that Diane Green had in announcing this expansion and hey here's a consultancy that's leveraging SAP Han into Google Cloud. >> Well Diane Green had to talk about both, because just running SAP on the Google Cloud platform, without sentient systems integrated to help, a customer who might want to buy it in, implement it, and then integrate it with their existing systems, they probably can't do that on their own, because SAP is still complex enterprise software, even if some of the operational capabilities are offloaded to a cloud vendor, so she needed both SAP and an implementation partner to say hey we're serious, but I guess I would add that when you're evaluating SAP there's more than just the core app, the core app is sort of the center of the universe for a customer who is looking to take their systems of record into the cloud, but there's an ecosystem on each cloud that surrounds that that makes it easy to build applications that leverage, that ecosystem's richest on Amazon, it's not far behind on Azure, and Google is still booting that up. >> So what advantage does this SAP partnership with Google give to Google, but also what advantage of any does it give to SAP? >> Okay, great question, so on the advantage to Google, it puts them as a peer, or more closer as a peer to Azure and Amazon, and then to SAP they can say we're cloud agnostic, I believe their infrastructure technology is both made up of Cloud Foundry which is cross cloud technology coming from Pivotal, and then Open Stack as a sort of infrastructure technology that's coming from a whole bunch of the legacy IT vendors who didn't want to be beholden to Amazon. >> What are the other things today, if we look at future trends, and that's kind of what I was expecting to hear, and we heard about a lot of them, big data block chain, we heard about IOT, industrial IOT, IOE, Deep Learning, they talked a lot about how Leonardo was going to facilitate machine learning, artificial intelligence, really help deliver automation, but one of the things that I was wondering if we were going to hear about was mobile. So a few months ago, I look at my notes here, they announced, I believe it was at Mobile World Congress, this partnership with Apple, so SAP opened their cloud platform to iOS developers with the goal of really establishing a bigger presence in mobile apps to power iPhones, etc, with Hana. Curious about did you expect to hear things about mobile today, or was that not part of the plan. >> If I had expected to hear more it would have been from a partner like IBM. Because with Apple they were essentially creating a toolkit for people to be able to build user interfaces on an iOS phone, and I think they've done Android as well, but in other words, the developer is left to their imaginations to fill in the functional capabilities of whatever app, they just have a frame work that makes building an Apple UI accessible. What IBM did with Apple was actually more significant, which was, hey we have all these industry solution groups, and we all these bright ideas functionality in the cloud, but we dont' have an accessible way to deliver it. SO what IBM teamed up to do with Apple, wasn't just give me, tell Apple give me an iOS UI development kit, it was let's collaborate on building some real apps that pilots need, that delivery folks or field servers folks need. So, I guess, I wasn't blown away by what they did with Apple. >> Okay, maybe that's a to be continued. One of the other themes that we heard today from Brad Luker, was software needs to become a strategy and that openness in that respect is an absolute game changer, allowing machine learning integration, social data integration for customer profiling, and really helping these user of SAP understand customer behaviors. He also said that every company today regardless of size needs to drive innovation by connecting all these business processes when software becomes strategy. What was your take on that from a thematic perspective, as well as a real world implication perspective for SAP customers from the small enterprises to the large. >> You know, I would have through that that would be the whole focus, you know the famous Mark Andersen SA from several years ago, Software's Eating the World. It's now really kind of data is eating software, it's data programs the machine learning algorithms that increasingly make up software. But he was a little bit, he talked at a high level about it, the only example I recall was Hybris, which is their commerce front end, where they're going to link marketing sales service, support, customer experience, and they're going to open this up through micro services, so that other developers can easily leverage these capabilities. That to me was end to end processes integrated on a SAP platform, but I would have liked to have seen a lot more examples of that. >> So you talked about Hybris, and on the Leonardo front, the expansion of that, they really talked about this expansion of Leonardo giving companies the ability to reinvent, that word has been used a lot by a lot of companies including Dell, years ago reinvent, reimagine, that could be used to mean a lot of things, but they talked about that as a facilitator of intelligently connecting lots of things, people, processes, systems, etc, what's your take on Leonardo as an accelerator of innovation as they positioned it to be. >> You know, that was sort of to re-emphasize they called the digital core, which is their legacy, not in a bad way, that's their asset that they can leverage to move in any direction. The traditional apps. And Leonardo was the win capability, how to leapfrog your competition. And they used this wonderful example of a win farm, where they could then look at a particular instance of a winmill and find where the stresses were and a capability I haven't seen yet, they were actually able to put a virtual sensor on that errant winmill and see where the stresses were coming from. But that capability isn't completely unique, there's GE and Predicts, and there's Parametric Technology with their Thingworks, and IBM has their Genius of Things, they're not alone in going after this notion of the digital twin and integrating it within the entire business process life cycle, their value add should be to make it easy to create that life cycle for the digital twin as designed as built as deployed as serviced as operated, to make that possible without tons of programing and to link it in with core business processes like field service, but again, it seemed a little bit more like a scenario than a finished app. >> Okay maybe you're saying for them to be differentiated it needs to be more of a me too, it needs to be much more simpler, maybe this is just the precipice they're on, and just didn't context it that way. >> It felt like a hey this is where we're moving to, as opposed to this is where we already are, and they have a lot of assets to bring to bear to get to that point, it just, they weren't really concrete in saying okay here's the functionality we have today, here's what we're going to add over the next 12 to 18 months, so it felt more like a this is where we're going. >> That's a good point that you bring that up from a road map perspective, and perhaps that will appear in some of the break ads which I would anticipate because they talked about that in the transparency and the empathy part of the keynote when Bill McDermot was first on stage about we're listening to our customers, we need to show you these roadmaps, so they did mention in text having impressed as well that it's for three particular products that they have these three year road maps, and obviously they'll be adding more over time. But if you look at SAP, 45 year old company, their roots in on-prem ERP, looking at their evolution and even kind of getting to the topic we were just on, the virtual reality and understanding sensors, is this a natural progression of an ERP company to transition to completely the cloud, help keep their customers there, establish this nucleus of the digital core, and then expand upon things to bring in machine learning, advanced analytics, predictive modeling. Is that a natural expansion? >> You know it's funny the way you asked that, because I think the answer is yes. But it happened in this wave where first it's completely custom, and you have the excentures, PWCs and the specialized sort of system integrators, the small ones that have boutique capabilities in big data and machine learning. They start building those sorts of apps first for big companies, or for internet center companies who really need to be at the bleeding edge, then comes the IBMs of the world where they have these semi-repeatable capabilities, custom development in the industry solutions groups and in their global business services, and so they're there composing a bunch of semi-finished piece parts, and then when it gets to SAP, it should be pretty much almost packaged and SAP goes in and configures it for the customers, in other words they flip a bunch of switches that make choices, so you go from completely custom to configured and almost fully packaged, and that's a natural progression over time, and every time we encounter newer technology that starts on the back, goes again to the fully custom solution, so I guess I do expect SAP to follow this pattern, their sweet spot, their business model is the repeatable stuff. >> When they talked about running core businesses in the cloud to get the benefits of scale, elasticity, availability, I think this was actually Byrne that was saying that they need to be using intelligent apps to automate as much as possible the hyper connectivity as they were talking about is really going to enable that, and he did predict that 80 percent of business processes will be running through SAP or 80 percent of them running will be fully autonomous in the near future. That's a bold number. >> Yeah, you know and that's the number behind the anxiety that everyone has about so what happens to my job, especially when we have conversational bots, we don't need host on our shows, I mean it's a bit of an exaggeration. There are a lot of people who worry that jobs will get completely automated, and then there are other people who say look, it's not every task I do that can be automated, it's some tasks, and there will be a machine that augments me, and changes the nature of my work, but doesn't replace me. One example is Gary Kasparov, who was beaten by IBMs Deep Blue chess playing program, I forget how long ago, maybe 12 or something like that. The best chess players in the world now, are not the computers, they're the ones who pair with a grandmaster with a computer playing against another grand master with a computer, because there's an intuition as to where to look that is not completely replacing human judgment. It's more like a compliment of judgment and then raw calculating horsepower. >> Interesting accompaniment. Well George, thanks for sharing your insights on the keynote, from SAP Sapphire Now. For George Gilbert, I'm Lisa Martin, stick around, we've got more coverage from SAP Sapphire now 2017. (upbeat electronic music)

>> Announcer: Live, from Orlando, Florida, it's theCUBE, covering ServiceNow Knowledge17. Brought to you by ServiceNow. >> We're back. This is Dave Vellante with Jeff Frick. Chris Bedi is here, he's the CIO of ServiceNow. Chris, good to see you again. >> Good to see you as well. >> Yeah, so, lot going on this week, obviously. You said you're getting pulled in a million different directions. One of those, of course, is the CIO event, CIO Decisions, it's something you guys host every year. I had the pleasure of attending parts of it last year. Listened to Robert Gates and some other folks, which was great. What's happened this year over there? >> So, CIO Decisions, it's really where we bring together our forward thinking executives. We keep it intimate, about a hundred, because really it's about the dialogue. Us all learning from each other. It really doesn't matter, the industry, I think we're all after the same things, which is driving higher levels of automation, increase the pace of doing business, and innovating at our companies. So we had Andrew McAfee, MIT research scientist, really helping push the boundaries in our imagination on where machine learning and predictive analytics could go. And then we had Daniel Pink talking about his latest book, To Sell is Human. And really as CIOs, we often find ourselves selling new concepts, new business models, new processes, new analytics, new ways of thinking about things. And so, really trying to help, call it exercise, our selling muscle, if you will. Because we have to sell across, up, down, and within our own teams, and that is a big part of the job. Because as we move into this new era, I think the biggest constraint is actually between our own ears. Our inability to imagine a future where machines are making more decisions than humans, platforms are doing more work on behalf of humans. Intellectually, we know we're headed there, but he really helped to bring it home. >> Well, you know, it's interesting, we talk about selling and the CIOs. Typically IT people aren't known as sales people, although a couple years ago I remember at one of the Knowledges, Frank Slootman sort of challenged the CIO to become really more business people, and he predicted that more business people would become CIOs. So, do you consider yourself a sales person? >> I do. Selling people on a vision, a concept, the promise of automation. You know, technology, people fear it, right? You know, when you're automating people's work the fear and the uncertainty endowed, or what I call the organizational anti-bodies, start to come out. So you have to bust through that, and a large part of that is selling people on a promise of a better future. But, it's got to be real. It's got to be tied to real business outcomes with numbers. It can't be just a bunch of PowerPoint slides. >> So we always like to take the messaging from the main tent and then test it with the practitioners, and this year there's this sort of overall theme of working at lightspeed, you and I have talked about this, how does that resonate with CIOs and how do you put meaning behind that? 'Cause, you know, working at lightspeed, it's like, ooh that sounds good, but how do you put meat on that bone? >> So, the way I think about working at lightspeed is three dimensions, velocity, intelligence, and experience. And velocity is how fast is your company operating? I read a study that said 40% of Fortune 500 companies are going to disappear in the next 10 years. That's almost half, right? But I think what's going to separate the winners from the losers is the pace at which they can adapt and transform. And, with every business process being powered by IT platforms, I think CIOs and IT are uniquely positioned to explicitly declare ownership of that metric and drive it forward. So velocity, hugely important. Intelligence. Evolving from the static dashboards we know today, to real time insights delivered in context that actually help the human make decisions. And, BI in analytics as we know it today, needs to evolve into a recommendation engine, 'cause why do we develop BI in analytics? To make decisions, right? So why can't the platform, and it can, is the short answer, with the ability to rapidly correlate variables and recognize complex patterns, give recommendations to the humans, and I would argue, take it a step further, make decisions for the humans. ServiceNow did a study that said 70% of CIOs believe machines will make more accurate decisions than humans, now we just got to get the other 30% there. And then on experience, I think the right experience changes our behavior. I think we in IT need to be in the business of creating insanely great customer and employee experiences. Too often we lead with the goal of cost reduction or efficiency, and I think that's okay, but if we lead with the goal of creating great experiences, the costs and the inefficiencies will naturally drop out. You can't have a great experience and have it be clunky and slow, it's just impossible. >> And it's interesting on the experience because the changing behavior is the hardest part of the whole equation. And I always think back to kind of getting people off an old solution. People used to say, for start ups, you got to be 10x better or 1/10th the cost. 2x, 3x is not enough to get people to make the shift. And so to get the person to engage with the platform as opposed to firing off the text, or firing off an email, or picking up the phone, it's got to be significantly better in terms of the return on their investment. So now they get that positive feedback loop and, ah, this is a much better way to get work done. >> It has to. And we can't, you know, bring down the management hammer and force people to do things. It's just not the way, you know, people work. And very simple example of an experience driving the right behavioral outcome, so ServiceNow is a software company, very important for us to file patents. The process we had was clunky and cumbersome. You know, we're not perfect at ServiceNow either. So we re-imagined that process, made it a mobile first experience built on our platform, of course. But by simply doing that, there was no management edict, you have to, no coercion, if you will, we saw an 83% increase in the number of patent applications filed by the engineers. So the right experience can absolutely give you the right desired economic behavior. >> You talked about 70% of CIOs believe that machines will make better decisions than humans. We also talked about Andrew McAfee, who wrote a book with Eric Brynjolfsson. And in that book, The Second Machine Age, they talked about that the greatest chess player in the world, when the supercomputer beat Garry Kasparov, he actually created this contest and they beat the supercomputer with a combination of man and other supercomputers. So do you see it as machine, sort of, intelligence augmenting human intelligence, or do you actually see it as machines are going to take over most of the decisions. >> So, I actually think they are going to start to take over some basic decision making. The more complex ones, the human brain, plus a machine, is still a more, you know, advanced, right? Where it's better suited to make that decision. But I also think we need to challenge ourselves in what we call a decision. I think a lot of times, what we call a decision, it's not a decision. We're coming to the same conclusion over and over and over again, so if a computer looked at it, it's an algorithm. But in our brains, we think a human has to be involved and touch it. So I think it's a little bit, it'll challenge us to redefine what's actually a decision which is complex and nuanced, versus we're really doing the same thing over and over again. >> Right, and you're saying the algorithm is a pattern that repeats itself and leads to an action that a machine can do. >> Yeah. >> It doesn't require intuition >> And we don't call that a decision anymore. >> Right, right. So, in thinking about you gave us sort of the dimensions of lightspeed, what are some of the new metrics that will emerge as a result of this thinking? >> Yeah, I don't think any of the old metrics go away. I'll talk about a few. You know, in lightspeed, working at lightspeed, we need to start measuring, for one, back on that velocity vector, what is the percentage of processes in your company that have a cycle time of zero, or near zero. Meaning it just happens instantaneously. We can think of loads of examples in our consumer life. Calling a car with Uber, there's no cycle time on that process, right? So looking at what percentage of your processes have a cycle time of zero. How much work are you moving to the machines? What percentage of the work is the platform proactively executing for you? Meaning it just happens. I also think in an IT context of percentage of self healing events, where the service never goes down because it's resilient enough and you have enough automation and intelligence. But there are events, but the infrastructure just heals itself. And I think, you know, IT itself, we've long looked at IT as a percentage of revenue. I think with all of the automation and cost savings and efficiencies we drive throughout the enterprise, we need to be looking at IT as a margin contribution vehicle. And when we change that conversation, and start measuring ourselves in terms of margin, I think it changes the whole investment thesis, in IT. >> So that's interesting. Are you measured on margin contribution? >> We're doing that right now. I don't, if an IT organization is waiting for the CFO or CEO to ask them about their margin contribution, they're playing defense. I think IT needs to proactively measure all of it's contributions and express it in terms of margin. 'Cause that's the language the CEO, and COO, and CFO are talking about, so meet them in a language that they understand better. >> So how do you do, I mean, you certainly can create some kind of conceptual value flow. IT supports this sort of business process and this business process drives this amount of revenue or margin. >> So I stay away from revenue, because I think any time IT stands up and says, we're driving revenue, it's really hard. Because there's so many external and internal factors that contribute to that. So we more focus on automation, in terms of hours saved, expressing and dollarizing that. Hard dollars, that we're able to take out of the organization and then bubbling that into an operating margin number. >> Okay, so you sort of use the income statement below the revenue line to guide you and then you fit into that framework. >> Absolutely. >> When you talk to other CIOs about this, do they say, hey, that sounds really interesting, how do I get started on that, or? >> I think it resonates really well, because, again, IT as percentage of revenue is an incredibly incomplete metric to measure our contribution. With everything going digital, you want to pour more money into technology. I mean, studies have shown, and Andrew McAfee talked about this, over the last 50, 100 years, the companies that have thrived have poured more, disproportionally more, into technology and innovation than their competitors. So, if we only measure the cost side of the equation we're doing ourselves a disservice. >> And so, how do you get started on this path, I mean, let's call this path, sort of, what we generally defined as lightspeed, measured on margin, how do you get started on that? >> First step is the hardest. But, it's declaring that your going to do it. So we've come up with a framework, you know, that maps at a process level, at a department level, and at a company level, where are we on this journey to lightspeed? If lightspeed is the finish line, where are we? And I define three stages, manual, automated, cloud, before you get to lightspeed. And then, using those same three dimensions of velocity, intelligence, and experience, to tell you where you are. And, the very first thing we did was baseline all of our business processes, every single one, and mapped it. But once you have it mapped on that framework then you can say, how do we advance the ball to the next level? And, it's not going to magically happen overnight. This is hard work. It's going to happen one process at a time, right? But pretty soon everything starts to get faster and I think things will start to really accelerate. >> When you think about, sort of, architecting IT, at ServiceNow versus some other company, I mean, you come into ServiceNow as the CIO, everything runs on ServiceNow, that is part of the mandate, right? But that's not the mandate at every company, now increasingly may be coming that way in a lot of companies, but how is your experience at ServiceNow differ from the some of the traditional G2000? >> Probably the unique part about being the CIO at ServiceNow is actually really fun, in that I get to be customer zero in that I implement our products before all of our customers. You know, get to sit down with the product managers, discuss real business problems that all of our customers are facing, and hopefully be their voice inside the four walls of service now, and be the strategic partner to the product organization. Now implementing everything, our goal is to be the best possible implementation of ServiceNow on the planet. And that's not just demonstrated by go lives, it's demonstrated by, again, the economic and business outcomes we're deriving from using the platform. So, that part is fun, challenging, and hard work all at the same time. >> So how's Jakarta lookin'? >> Fantastic. We're super excited about everything that's coming out, whether it's the communities on customer service, or our software asset management. That's been a pain, right, for IT organizations for a long time, which is these inbound software audits, from other companies, and you're responding to them and it's a fire drill. In my mind, our software asset management transforms software audits from a once a year, twice a year event, to always-on monitoring, where you're just fixing it the whole time. And it's not an event anymore. I mean, the intelligence that we're baking into the platform now, super exciting around the machine learning and the predictive analytics concepts, we have more analytics than we had before, I mean there's just so much in there, that's just exciting. We're already using it, I can't wait for our customers to get a hold of it. >> Well, CJ this morning threw out a number of 30-plus percent performance improvement. I had said to myself, your saying that with conviction, that's 'cause you guys got to be running it yourselves. >> Yeah, we are. >> What are you seeing there? >> That's not a trivial number, and I think the product teams have done a great job really digging in and makin' sure our platform operates at lightspeed. >> One of the things that Jeff and I have been talking about this week, and really this is your passion here, is adoption, how do you get people to stop using all these other tools like email, and kind of get them to use the system? >> I think, showing them the promise of what it can bring. I think it's different conversations at different levels. I think, too, an operator, someone who's using the email to manage their work, they're hungry for a different solution. Life, working, and email, and managing your business that way, it's hard, right? To a mid-level manager, I think the conversation is maybe about the experience, how consumers of their service will be happier and more satisfied. At executive level, it gets maybe more into some of the economic outcomes, of doing it. Because implementing our platform, you know, you're going to burn some calories doing it, not a lot. Our time to value is really really quick, but still, it's a project and it's initiative and it's got to have an outcome tied to it. >> You know, Chris, as you're saying that it's always tough to be stuck kind of half way. You know, you're kind of on the tool internally and it's great. >> We don't use the word tool. >> Excuse me, not the tool. The app, the platform, actually. But then you still got external people that are coming at you through text, email, et cetera. I mean, is part of the vision, and maybe it's already there, I'm not as familiar with the parts I should be, in terms of enabling kind of that next layer of engagement with that next layer of people outside the four walls, to get more of them in it as well. Because the half-pregnant stage is almost more difficult because you're going back and forth between the two. >> And our customer service product does a lot of that. If you look at what Abhijit showed today, which is fantastic, Communities is another modality to start to interact with people. Certainly, we have Connect, part of our platform, is a collaboration app within the overall platform, so you can chat, just like you would with any consumer app, in terms of chatting capabilities, and that mobile first experience. We're thinking about other modalities too. Should you be able to talk to ServiceNow, just like you talk to Alexa, and converse with ServiceNow, Farrell touched on this a little bit, through natural language, right? We all know it's coming, and it's there, it's just pushing in that direction. >> How about the security piece? You know, Shawn shared this morning, you guys are well over year in now, and he talked about that infamous number of 200 plus days-- >> Chris: Nine months, yeah. >> Yeah, compressing that. Are you seeing that internally in your own? >> We are. We use Shawn's product, we're a happy customer. The vulnerability management, the security incident response, and very very similar results. And just like the customer who was on stage said, go live in Iterate, and that's exactly what we did. Everyone has a vulnerability management tool, like a Qualys, that's feeding in. Bring in all those Qualys alerts, our platform will help you normalize them and just start to reduce the level of chaos for the SOC and IT operations. Then make it better, then drive the automation, so we're seeing very similar benefits. >> How do you manage the upgrade side, we've been asking a lot of customers this week in the upgrade cycle. Some say, ah, I'll do in minus one just to sort of let the thing bake a little bit. You guys are in plus one. How do you manage that in production, though? >> Sure, so we upgrade before our customers, and that's part of our job, right? To make sure we test it out before our customers. But I'll say something in general about enterprise software upgrades, which is, there is a cost to them and the cost is associated with business risk. You want to make sure you're not going to disrupt your business. There is some level of regression testing you just have to do. Now, strategies I think that would be wise are automating as much of that testing as you can, through a testing framework, which we're helping our customers do now. And I think with some legacy platforms, that was incredibly expensive and hard and you could never quite get there. Us being a modern cloud platform, you can actually get there pretty quickly to the point where the 80, 90% of your regression testing is automated and you're doing that last 10 to 20%. 'Cause at the end of the day, IT needs to make sure the enterprise is up and running, that's job number one. But that's a strategy we employ to make upgrades as painless as possible. >> That's got to be compelling to a lot of the customers that you talk to, that notion of being able to automate the upgrade process. >> For sure, it is. >> You're eliminating a lot of time and they count that as money. >> It is money, and automating regression testing, it's a decision and a strategy but the investment pays off very very quickly. >> Dave: So there's an upfront chunk that you have to do to figure out how to make that work? >> Just like anything worth doing. >> Dave: Yeah, right. >> Right? >> Excellent. What's left for you at the show? >> What's left for me? I love interacting with customers. I got to talk with a lot of CIOs at CIO Decisions. I actually enjoy walking through the partner pavilion and meeting a lot of our partners and seeing some of the innovation that their driving on the platform. And then just non-stop, I get ideas all day from meeting with customers. It's so fun. >> Dave: Chris, thanks very much for coming to theCube. >> Thank you. >> We appreciate seeing you again. >> Chris: Good seeing you. >> Alright, keep it right there everybody. Jeff and I will be back with our next guest. This is theCube, we're live from Knowledge17. We'll be right back.