>>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.

>> 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: 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)

>> Hello, Wikibon community, and welcome to our 2017 predictions for the technology industry. We're very excited to be able to do this, today. This is one of the first times that Wikibon has undertaken something like this. I've been here since about April, 2016, and it's certainly the first time that I've been part of a gathering like this, with so many members of the Wikibon community. Today I'm joined with, or joined by, Dave Vellante, who's our co-CEO. So I'm the Chief Research Officer, here, and you can see me there on the left, that you can see this is from our being on TheCube at big data, New York City, this past September, and there's Dave on the right-hand side. Dave, you want to say hi? >> Dave: Hi everybody; welcome. >> So, there's a few things that we're going to do, here. The first thing I want to note is that we've got a couple of relatively simple webinar housekeeping issues. The first thing to note is everyone is muted. There is a Q&A option. You can hit the tab and a window will pop up and you can ask questions there. So if you hear anything that requires an answer, something we haven't covered or you'd like to hear again, by all means, hit that window, ask the question, and we'll do our best to get back to you. If you're a Wikibon customer, we'll follow up with you shortly after the call to make sure you get your question answered. If, however, you want to chat with your other members of the community, or with either Dave or myself, you want to comment, then there's also a chat option. On some of the toolbars, it's listed under the More button. So if you go to the More button, and you want to chat, you can probably find that there. Finally, we're also recording the webinar, and we will turn this into a Wikibon deliverable for the overall community. So, very excited to be doing this. Now, Dave, one of the things that we note on this slide is that we have TheCube in the lower left-hand corner. Why don't you take us through a little bit about who we are and what we're doing? >> Okay, great; thanks, Peter. So I think many of you or most of you know that SiliconANGLE Media Inc is sort of the umbrella company, and underneath SiliconAngle, we have three brands: the Wikibon research brand, which was started in the 2007 time frame. It's a community of IT practitioners. TheCube is, some people call it the ESPN of tech. We'll do 100 events this year, and we extensively use TheCUBE as a data-gathering mechanism and a way to communicate to our community. We've got some big shows coming up, pretty much every week, but of course we've got Amazon Reinvent coming up, and we'll be in London with HPE Discover. And so, we cover the world and cover technology, particularly in the enterprise, and then there's the SiliconANGLE publishing team, headed up by Rob Hoaf. It was founded by my co-CEO John Ferrier, and Rob Hoaf, former Business Week, is now leading that team. So those are the three main brands. We've got a new website coming out this month, on SiliconANGLE, so really excited about that and just thank the community for all your feedback and participation, so Peter, back to you. >> Thank you, Dave, so what you're going to hear today is what the analyst team here at Wikibon has pulled together for what we regard as some of the most interesting things that we think are going to happen over the next two years. Wikibon has been known for looking at disruptive technologies, and so while the focus, from a practical standpoint, in 2017, we do go further out. What is the overarching theme? Well, the overarching theme of our research and our conversations with the community is very simple. It's: put more data to work. The industry has developed incredible tools to gather data, to do analysis on data, to have applications use data and store data. I could go on with that list. But the data tends to be quite segmented and quite siloed to a particular application, a particular group, or a particular other activity. And the goal of digital business, in very simple terms, is to find ways to turn that data into an asset, so that it can be applied to other forms of work. That data could include customer data, operational data, financial data, virtually any data that we can imagine. And the number of sources that we're going to have over the next few years are going to be astronomical. Now, what we want to do is we want to find ways so that data can be freed up, almost like energy, in a physical sense, to dramatically improve the quality of the work that a firm produces. Whether it's from an engagement standpoint, or a customer experience standpoint, or actual operations, and increasingly automation. So that's the underlying theme. And as we go through all of these predictions, that theme will come out, and we'll reinforce that message during the course of the session. So, how are we going to do this? The first thing we're going to do is we're going to have six predictions that focus in 2017. Those six predictions are going to answer crucial questions that we're getting from the community. The first one is: what's driving system architecture? Are there new use cases, new applications, new considerations that are going to influence not only how technology companies create the systems and the storage and the networking and the database, and the middleware and the applications, but also how users are going to evolve the way they think about investing? The second one is: do micro-processor options matter? Through 20 years now, we've pretty much focused on one, limited class of micro-processor, the X386, er, the X86 architecture. But will these new workloads drive opportunities or options for new micro-processors? Do we have to worry about that? Thirdly, all this data has to be stored somewhere. Are we going to continue to store it, limited only on HDDs, or are other technologies going to come into vogue? Fourthly, in the 2017 time frame, we see the cloud, a lot's happening, professional developers have flocked to it, enterprises are starting to move to it in a big way, what does it mean to code in the cloud? What kinds of challenges are we going to face? Are they technological? Are they organizational, institutional? Are they sourcing? Related to that, obviously, is Amazon's had enormous momentum over the past few years. Do we expect that to continue? Is everybody else going to be continuing to play catch-up? And the last question for 2017 that we think is going to be very important is this notion of big data complexity. Big data has promised big things, and quite frankly has, except in some limited cases, been a little bit underwhelming. As some would argue, this last election showed. Now, we're going to move, after those six predictions, to 2022, where we'll have three predictions that we're going to focus on. One is: what is the new IT mandate? Is there a new IT mandate? Is it going to be the same old, same old, or is IT going to be asked to do new things? Secondly, when we think about Internet of Things, and we think about Augmented Reality or virtual reality, or some of these other new ways of engaging people, is that going to draw out new classes of applications? And then finally, after years of investing heavily in mobile applications, in mobile websites, and any number of other things, and thinking that there was this tight linkage where mobile equaled digital engagement, we're starting to see that maybe that's breaking, and we have to ask the question: is that all there is to digital engagement, or is there something else on the horizon that we're going to have to do? The last prediction, in 2027, we're going to take a stab here and say: will we all work for AI? So, these are the questions that we hear frequently from our clients, from our community. These are the predictions we're going to attend to and address. If you have others, let us know. If there's other things that you want us to focus on, let us know, but here's where we're starting. Alright. So let's start with 2017. What's driving system architecture? Our prediction for 2017 regarding this is very simple. The IoT edge use cases begin shaping decisions in system and application architecture. Now, the right-hand side, if you look at that chart, you can see a very, very important result of the piece of research that David Foyer recently did. And it shows IoT edge options, three-year costs. From left to right, moving all the data into the cloud over a normal data communications, telecommunications circuit, in the middle, moving that data into a central location, namely using cellular network technologies, which have different performance and security attributes, and then finally, keeping 95 percent of the data at the edge, processing it locally. We can see that the costs are overwhelming, favoring being smarter by how we design these applications and keeping more of that data local. And in fact, we think that so long as data and communications costs remain what they are, that there's going to be an irrevokeable pressure to alter key application architectures and ways of thinking to keep more of that crossing at the edge. The first point to note, here, is it means that data doesn't tend to move to the center as much as many are predicting, but rather, the cloud moves to the edge. The reason for that is that data movement isn't free. That means we're going to have even more distributed, highly autonomous apps, so none of those have to be managed in ways that sustain the firm's behavior in a branded, consistent way. And very importantly, because these apps are going to be distributed and autonomous, close to the data, it ultimately means that there's going to be a lot of operational technology players that impact the key decisions, here, that we're going to see made as we think about the new technologies that are going to be built by vendors and in the application architectures that are going to be deployed by users. >> So, Peter, let me just add to that. I think the key takeaway there is, as you mentioned, and I just don't want it to get lost, is 95 percent of the data, we're predicting, will stay at the edge. That's a much larger figure than I've seen from other firms or other commentary, and that's substantial, that's significant, it says it's not going to move. It's probably going to sit on flash, and the analytics will be done at the edge, as opposed to this sort of first bar, being cloud only. That 95 percent figure has been debated. It's somewhat controversial, but that's where we are today. Just wanted to point that out. >> Yeah, that's a great point, Dave. And the one thing to note, here, that's very important, is that this is partly driven by the cost of telecommunications or data communications, but there also are physical realities that have to be addressed. So, physics, the round trip times because of the speed of light, the need for greater autonomy and automation on the edge, OT and the decisions and the characteristics there, all of these will contribute strongly to this notion of the edge is increasingly going to drive application architectures and new technologies. So what's going to power those technologies? What's going to be behind those technologies? Let's start by looking at the CPUs. Do micro-processor options matter? Well, our prediction is that evolution in workloads, the edge, big data, which we would just, for now, put AI and machine learning, and cognitive underneath many of those big data things, almost as application forms, creates an opening for new micro-processor technologies, which are going to start grabbing market share from x86 servers in the next few years. Two to three percent next year, in 2017. And we can see a scenario where that number grows to double digits in the next three or four years, easily. Now, these micro-processors are going to come from multiple sources, but the factors driving this are, first off, the unbelievable explosion in devices served. That it's just going to require more processing power all over the place, and the processing power has to become much more cost-effective and much more tuned specifically to serving those types of devices. Data volumes and data complexity is another reason. Consumer economics is clearly driving a lot of these factors, has been for years, and it's going to continue to do so. But we will see new, ARM-based processors and other, and GPUs for big data apps, which have the advantage of being also supported in many of the consumer applications out there, driving this new trend. Now, the other two factors. Moore's Law is not out of room. We don't want to suggest that, but it's not the factor that it used to be. We can't presume that we're going to get double the performance out of a single class of technology every year or so, and that's going to remove any and all other types of micro-processor sets. So there's just not as much headroom. There's going to be an opportunity now to drive at these new workloads with more specialized technology. And the final one is: the legacy software issue's never going to go away; it's a big issue, it's going to remain a big issue. But, these new workloads are going to create so much new value in digital business settings, we believe, that it will moderate the degree to which legacy software keeps a hold on the server marketplace. So, we expect a lot of ARM-based servers that are lower cost, tuned and specialized, supporting different types of apps. A lot of significant opportunity for GPUs for big data apps, which do a great job running those kinds of graph-based data models. And a lot of room, still, for RISC in pre-packaged HCI solutions. Which we call: single managed entities. Others call: appliances. So we see a lot of room for new micro-processors in the marketplace over the next few years. >> I guess I'll add to that, and I'll be brief, just in the interest of time, the industry has marched to the cadence of Moore's Law for, as we know, many, many decades, and that's been the fundamental source of innovation. We see the innovation curve shifting and changing to become combinatorial, a combination of technologies. Peter mentioned GPU, certainly visualization's in there. AI, machine learning, deep learning, graph databases, combining to be the fundamental driver of innovation, going forward, so the answer here is: yes, they matter. Workloads are obviously the key. >> Great, Dave. So let's go to the next one. We talked about CPUs, well now, let's talk about HDDs. And more broadly, storage. So the prediction is that anything in a data center that physically moves gets less useful and loses share of wallet. Now, clearly that includes tape, but now it's starting to include HDDs. In our overall enterprise systems, storage systems revenue forecast, which is going to be published very, very shortly, we've identified that we think that the revenue attributable to HDD-based enterprise storage systems is going to drop over the next few years, while flash-based enterprise storage system revenue rises dramatically. Now, we're talking about storage system revenue here, Dave. We're not just talking about the HDDs, themselves. The HDD market starts, continues to grow, perhaps not as fast, partly because, even as the performance side of the HDD market starts to fade a bit, replaced by flash, that bulk, volume part of the HDD marketplace starts to substitute for tape. So, why is this happening? One of the main reasons it's happening is because the storage revenue, the storage systems revenue is very strongly influenced by software. And those software revenues are being bundled into the flash-based systems. Now, there's a couple reasons for this. First off, as we've predicted for quite some time, we do see a flash-only data center option on the horizon. It's coming well into focus. Number two is that, the good news is flash-based products are starting to come down and also are in sight of HDD-based products at the performance level. But very importantly, and here's one of the key notions of the value of data, and finding new ways to increase the use of data: flash, our research shows, offers superior business value, too, precisely because you can make so many copies of it and have a single set of data serve so many different applications and so many users, at scales that just aren't possible with traditional, HDD-based enterprise storage systems. Now, this applies to labor, too, Dave, doesn't it? >> Yeah, so a couple of points here. Yes, labor being one of those, sort of, areas that Peter's talking about are, ah, in jeopardy. We see about $200 billion over the next 10 years shifting from what we often refer to as non-differentiated IT labor, in provisioning and networking configuration and laying cable, et cetera, shifting from where it is today in services and/or on-prem IT labor, to vendor R&D or the cloud. So that's a very important point. I think I just wanted to add some color to what you were talking about before when you talked about HDD revenue continuing to grow, I think you were talking about, specifically, in the enterprise, in this storage systems view. And the other thing I want to add is, Peter, referenced sort of the business value of flash, as you, many of you know, David Floyer and Wikibon predicted, very early on, the impact that flash would have on spinning disk, and not only because of cost related to compression and de-duplication, but also this notion that Peter's talking about, of data sharing. The ability of development organizations to use the same data and minimize the number of copies. Now, the thing to watch, here, and kind of the wildcard is the hyperscale model. Hyperscalers, as we know, are consuming many, many, you know, exabytes and petabytes of data. They do things differently than is done in the enterprise, so that's something that we're watching very closely in terms of that model, that model being the hyperscale model, how it mimics or how it doesn't mimic what traditionally has occurred in the enterprise and how that will affect adoption of both flash and spinning disk. But as Peter said, we'll be releasing this data very shortly, and you'll be able to dig into it with is. >> And very importantly, Dave, in response to one of the comments in the chat, we're not talking about duplication of data everywhere, we're talking about the ability to provide logical and effective copies to single-data sources, so that, just because you can just drive a lot more throughput. So, that's the HDD. Now, let's turn to some of this notion of coding the cloud. What are we going to do with code in the cloud? Well our prediction is that the new cloud development stack, which is centered on containers and APIs, matures rapidly, but institutional habits in development constrain change. Now, why do we say that? I want to draw your attention to the graphic on the right-hand side. Now, this is what we think the mature, or the maturing cloud development stack looks like. As you can see, it's a lot of notions of containers, a lot of notions of other types of technologies. We'll see APIs interspersed throughout here as a primary way of getting to some of these container-based applications, services, microservices, et cetera, but this same, exact chart could be mapped back to SOA from 10 years ago, and even from some of the distributed computing environments that were put forward 20 years ago. The challenge here is that a sizable percentage, and we're estimating about 80 percent of in-house development, is still set up to work the old way. And so long as development organizations are structured to build monolithic apps or take care of monolithic apps, they will tend to create monolithic apps, with whatever technology's available to them. So, while we see these stacks becoming more vogue and more in use, we may not see, in 2017, shops being able to take full advantage of them. Precisely because the institutional work forms are going to change more slowly. Now, big data will partly contravene these habits. Why? Because big data is going to require quite different development approaches, because of the complexity associated of analytic pipelines, building analytic pipelines, managing data, figuring out how to move things from here to there, et cetera; there's some very, very complex data movement that takes place within big data applications. And some of these new application services, like Cognitive, et cetera, will require some new ways of thinking about how to do development. So, there will be a contravening force here, which we'll get to, shortly, but the last one is: ultimately, we think time-to-value metrics are going to be key. As KPI's move from project cost and taking care of the money, et cetera, and move more towards speed, as Agile starts to assert itself, as organizations start to, not only, build part of the development organization around Agile, but also Agile starts bleeding into other management locations, like even finance, then we'll start to see these new technologies really start asserting themselves and having a big impact. >> So, I would add to that, this notion of the iron triangle being these embedded processes, which as we all know, people, processes, and technology, people and process are the hardest to change, I'm interested, Peter, in your thoughts on, you hear a lot about Waterfall versus Agile; how will organizations, sort of, how will that affect organizations, in terms of their ability to adopt some of these, you know, new methodologies like Agile and Scrum? >> Well, the thing we're saying is the technology's going to happen fast, the Agile processes are being well-adopted, and are being used, certainly, in development, but I have had lots of conversations with CIOs, for example, over the last year and a half, two years ago, where they observed that they're having a very difficult time with reconciling the impedance mismatch between Agile development and non-Agile budgeting. And so, a lot of that still has to be worked out, and it's going to be tied back to how we think about the value of data, to be sure, but ultimately, again, it comes back to this notion of people, Dave, if the organization is not set up to fully take advantage of these new classes of technologies, if they're set up to deliver and maintain more monolithic applications, then that's what's going to tend to get built, and that's what's going to get, and that's what the organization is going to tend to have, and that's going to undermine some of the new value propositions that these technologies put forward. Well, what about the cloud? What kind of momentum does Amazon have? And our prediction for 2017 is that Amazon's going to have yet another banner year, but customers are going to start demanding a simplicity reset. Now, TheCUBE is going to be at Amazon Reinvent with John Ferrier and Steve Minnamon are going to be up there, I believe, Dave, and we're very excited. There's a lot of buzz happening about Reinvent. So follow us up there, through TheCUBE at Reinvent. But what I've done on the right-hand side is sent you a piece of Wikibon research. What we did is we wrote up, and we did an analysis of all of the AWS cases put forward, on their website, about how people are using AWS, and there's well over 650, or at least there were when we looked at it, and we looked at about two-thirds of them, and here's what we came up with. Somewhere in the vicinity of 80 percent, or so, of those cases are tied back to firms that we might regard as professional software delivery organizations. Whether they're stash or business services or games, provided games, or social networks. There's a smaller piece of the pie that's dedicated to traditional enterprise-type class of customers. But that's a growing and important piece, and we're not diminishing it at all, but the broad array of this pie chart, folks are relatively able to hire the people and apply the skills and devote the time necessary to learn some of the more complex, 75-plus Amazon services that are now available. The other part of the marketplace, the part that's moving into Amazon, the promise of Amazon is that it's simple, it's straightforward, and it is. Certainly more so than other options, but we anticipate that there will have to be a new type of, and Amazon's going to have to work even harder to simplify it, as it tries to attract more of that enterprise crowd. It's true that the flexibility of Amazon is certainly spawning complexity. We expect to see new tools, in fact, there are new tools on the market from companies like Appfield, for example, for handling and managing AWS billing and services, and that is, our CIOs are telling us, they're actually very helpful and very powerful in helping to manage those relationships, but the big issue here is that other folks, like VM Ware, have done research to suggest that the average shop has two to three big cloud relationships. That makes a lot of sense to us. As we start adding hybrid cloud into this and the complexities of inter-cloud communication and inter-cloud orchestration starts to become very real, that's going to even add more complexity, overall. >> So I'd add to that, just in terms of Amazon momentum, obviously those of you who follow what I read, you know, have been covering this for quite some time, but to me, the marginal economics of Amazon's model continue to be increasingly attractive. You can see it in the operating profits. Amazon's gap, operating profits, are in the mid-20s. 25, 26 percent. Just to give you a sense, EMC, who's an incredibly profitable company, its gap operating profits are in the teens. Amazon's non-gap operating profits are into 30 percent, so it's an incredibly profitable company. The more it grows, the more profitable it gets. Having said that, I think we agree with what Peter's saying in terms of complexity; think about API creep in Amazon. And different proprietary APIs for each of the data services, whether it's Kinesis or EC2 or S3 or Dynamo DB or EMR, et cetera, so the data complexity and the complexity of the data pipeline is growing, and I think that opens the door for the on-prem folks to at least mimic the public cloud experience to a great degree; as great a degree as possible. And you're seeing people, certainly, companies do that in their marketing, and starting to do that in the solutions that they're delivering. So by no means are we saying Amazon takes over the world, despite, you know, the momentum. There's a window open for those that can mimic, to the large extent, the public cloud capabilities. >> Yeah, very important point there. And as we said earlier, we do expect to see the cloud move closer to the edge, and that includes on-prem, in a managed way, as opposed to presuming that everything ends up in the cloud. Physics has something to say about that, as do some of the costs of data movement. Alright, so we've got one more 2017 prediction, and you can probably guess what it is. We've spent a lot of years and have a pretty significant place in spin big data, and we've been pretty aggressive about publishing what we think is going to happen in big data, or what is happening in big data, over the last year or so. One of the reasons why we think Amazon's momentum is going to increase is precisely because we think it's going to become a bigger target for big data. Why? Because big data complexity is a serious concern in many organizations today. Now, it's a serious concern because the spoke nature of the tools that are out there, many of which are individually extremely good, means that shops are spending an enormous amount of time just managing the underlying technology, and not as much time as they need to learning about how to solve big data problems, doing a great job of piloting applications, demonstrating to the business the financial returns are there. So as a result of this bespoked big data tool aggregates, we get multi-source, and we need to cobble it together from a lot of different technology sources, a lot of uncoordinated software and hardware updates that dramatically drive up the cost of on-prem administration. A lot of conflicting commitments, both from the business as well as from the suppliers, and very, very complex contracts. And as a result of that, we think that that's been one of the primary reasons why there's been so many pilot failures and why big data has not taken off the way that it probably should have. We think, however, that in 2017, we're going to see, and here's our prediction, we're going to see failure rates for big data pilots drop by 50 percent, as big vendors, IBM, Microsoft, AWS, and Google, amongst the chief ones, and we'll see if Oracle gets into that list, bring pre-packaged, problem-based analytic pipelines to market. And that's what we mean by this concept, here, of big data, single-managed entities. The idea that we can pull together, a company can pull together, or that it can pull together all the various elements necessary to provide the underlying infrastructure so that a shop can focus more time making sure that they understand the use-case, they understand how to go get the data necessary to serve that use-case, and understand how to pilot and deploy the application, because the underlying hardware and system software is pre-packaged and used. Now, we think that these, the SMEs, that are going to be most successful will be ones that are not predicated only on more proprietary software, but utilize a lot of open-source software. The ones that we see that are most successful today are in fact combining the pre-packaging of technology with the availability, or access, to the enormous value that the open-source market continues to build as it constructs new tools and delivers them out to big data applications. Ultimately, you've seen this before, or you've heard this before, from us: time-to-value becomes the focus. Similar to development, and we think that's one of the convergences that we have, here. We think that big data apps, or app patterns, will start to solidify. George Gilbert's done some leading-edge research on what some of those application patterns are going to be, and how those application patterns are going to drive analytic pipeline decisions, and very important, the process of building out the business capabilities necessary to build out the repeatable big data services to the business. Now, very importantly, some of these app patterns are going to be, are going to look like machine learning, cognitive AI, in many respects, all of these are part of this use-case to app trend that we see. So, we think that big data's kind of an umbrella for all of those different technology classes. It's going to be a lot of marketing done that tries to differentiate machine learning, cognitive AI. Technically, there are some differences, but from our perspective, they're all part of the effort of trying to ensure that we can pull together the technology in a more simple way so that it can be applied to complex business problems more easily. One more point I'll note, Dave, is that, and you adjust that world a lot, so I'd love to get your comments on this, but one of the more successful single-managed entities out there is, in fact, Watson from IBM, and it's actually a set of services and not just a device that you buy. >> Yeah, so a couple comments, there. One is that you can see the complexity in the market data, and we've been covering big data markets for a long time now, and there were two things that stood out when we started covering this. One is that software, as a percentage of the total revenue, is much lower than you would expect, in most markets. And that's because of the open-source contribution and the, you know, the multi-year collapse that we've seen in infrastructure software pricing. Largely due to open-source and cloud. The other piece of that is professional services, which have dominated spending within big data, because of the complexity. I think you're right, when you look at what happened at World of Watson and, you know, what IBM's trying to do, and others, in your prediction, there, are putting together a full, end-to-end data pipeline to do, you know, ingest and data wrangling and collaboration between data scientists, data engineers, and application developers and data quality people, and then bringing in the analytics piece. And essentially, you know, what many companies have done, and IBM included, they've cobbled together sets of tools and they've sort of layered on a way to interact with those tools, so the integration has still been slow in coming, but that's where the market is headed, so that we actually can build commercial, off the shelf applications. There's been a lack of those applications. I remember, probably four years ago, Mike Olsen at a (unintelligible) predicted: this will be the year of the big data app. And it still has not happened, so, and until it does, that complexity is going to reign. >> Yeah, and so it, again, as we said earlier, we anticipate that the big data, the need for developers to become more a part of the big data ecosystem, and the need for developers to get more value out of some of the other new cloud stacks are going to come together and will reinforce each other over the course of the next 24 to 36 months. So those were our 2017 predictions. Now let's take a look at our 2022 predictions, and we've got three. The first one is we do think a new IT mandate's on the horizon. Consistent with all these trends we've talked about, the idea of new ways of thinking about infrastructure and application architecture, based on the realities of the edge, new ways of thinking about how application developers need to participate in the value equation activities of big data, new ways of organizing to try to take greater advantage of the new processes, new technologies for development. We think, very strongly, that IT organizations will organize work to generate greater value from data assets by engineering proximity of applications and data. What do we mean by that? Well, proximity can mean physical proximity, but it also is something that we mean in terms of governance, tool similarity, infrastructure commonality, we think that over the next four to five years, we'll see a lot of effort to try to increase the proximity of not only data assets from a data standpoint, or the raw data, but also understanding from an infrastructure, governance skillset, et cetera, standpoint. So that we can actually do a better job of, again, generating more work out of our data by finding new and interesting ways of weaving together systems of records, big analytics, IOT, and a lot of other new application forms we see on the horizon, including one that I'll talk about in a second. Data value becomes a hot topic. We're going to have to do a better job, as a community, of talking about how data is valuable. How it creates (unintelligible) in the business, how it has to be applied, or has to be thought of as a source of value, in building out those systems. We talked earlier about the notion of people, process, and technology, well, we have to add to that: data. Data needs to be an asset that gets consumed as we think about how business changes. So data value's going to become a hot topic, and it's something we're focused on, as to what it means. We think, as Dave mentioned earlier, it's going to catalyze a true private cloud solutions for legacy applications. Now, I know Dave, you're going to want to talk about, in a second, what this might need. For example, things like the Amazon, VM Ware recent announcement. But it also means that strategic sourcing becomes reality. The idea of just going after the cheapest solution, or cost-optimized solution, which, don't get me wrong, don't get us wrong, is not going to go away, but it means that increasingly we're going to focus on new sourcing arrangements that facilitate creating greater proximity for those crucial aspects that make our shop run. >> Okay, so a couple of thoughts there, Peter. You know, there's a lot of talk, a couple years ago, and it's slowly beginning to happen, of bringing transaction and analytic systems together. What that oftentimes means is somebody takes their mainframe for the transactions and sticks it in finneban pipe into an exodata. I don't think that's what everybody envisioned when you started to sort of discuss that mean. So that's sort of happening slowly. But it's something that we're watching. This notion of data value, and shifting from, really a process economy to a data, or an insight, economy is something that's also occurring. You're seeing the emergence of the chief data officer. And our research shows that there are five things a chief data officer must do to really get started. The first is to understand data value, and how data contributes to the monetization of their company. So not monetizing the data, per se, and I think that's a mistake that a lot of people made, early on, is trying to figure out how to sell their data, but it's really to understand how data contributes to value for your organization. The second piece is how to access that data, who gets access to that data, and what data sources you have. And the third is the quality and trust of that data. And those are sequential things that our research shows a chief data officer has to do. And then the other, sort of parallel items, are relationship with the line of business and re-skilling. And those are complicated issues for most organizations to undertake, and something that's going to take, you know, many, many years to play out. The vast majorities of customers that we talk to say their data-driven, but aren't necessarily data-driven. >> Right, so, the one other thing I wanted to mention, Dave, is that we did some research, for example, on the VM Ware, Amazon relationship, and the reason why we were positive on it is quite simple. That it provides a path for VM Ware's customers, with their legacy applications running under VM Ware, to move those applications and the data associated with those applications, if they choose to, closer to some of the new, big data applications that are showing up in Amazon. So there's an example of this notion of making it more proximate, making applications and data more proximate, based on physics, based on governance, based on overall tooling and skilling, and we anticipate that that's going to become a new design center for a lot of shops over the course of the next few years. Now, coming to this notion of a new design center, the next thing we want to note is that, IoT, the Internet of Things, plus augmented reality, is going to have an impact on the marketplace. We got very excited about IoT, simply by thinking about the things, but our perspective is, increasingly, we have to recognize that people are going to always be a major feature, and perhaps the greatest value-creating feature, of systems. And augmented reality is going to emerge as a crucial actuator for the Internet of Things, and people. And that's kind of what we mean, is that augmented reality becomes an actuator for people. As will Chat Box and other types of technologies. Now, an actuator in an IoT sense is the devices or set of capabilities that take the results of models and actually turn that into a real-world behavior. So, if we think about this virtuous cycle that we have on the right-hand side, the internet, these are the three capabilities that we think people or firms are going to have to build out. They're going to have to build out an Internet of Things and People that are capable of capturing data, and turning analogue data into digital data, so that it can be moved into these big data applications. Again, with machine learning and AI and cognitive, sort of being part of that or underneath that umbrella, so that, then, we can build more models, more insights, more software that then translates into what we're calling systems of enaction. Or systems of "enaction", not "inaction". Systems of enaction. Businesses still serve customers, and these systems of enaction are going to generate real-world outcomes from these models and insights, and these real-world outcomes will certainly be associated with things, but they will also be associated with human being and people. And as a consequence of this, this we think is so powerful and is going to be so important over the course of the next five years that we anticipate that we will see a new set of disciplines focused on social discovery. Historically, in this industry, we've been very focused on turning insights or discovery about physics into hardware. Well, over the next few years, and Dave mentioned moving from the process to some new economy, we're going to see an enormous investment in understanding the social dynamics of how people work together and turn that into software. Not just how accountants do things, but how customers and enterprises come together to make markets happen, and through that social discovery, create these systems of enaction so that businesses can successfully, can successfully attend to and deliver the promises and the, ah, and the predictions that they're making through their other parts of their big data applications. >> So, Peter, you've pointed out many times that the big change, relative to processes, and historically, in the IT business, we've known what the processes are. The technology was sort of unknown, and mysterious. That's flipped. It's now, really the process is the unknown piece. That's the mysterious part. The technology is pretty well-understood. I think, as it relates to what you're talking about here with IoT and AR, what people tell us, the practitioners that are struggling with this, first of all, there's so much analogue data that people are trying to digitize, the other piece is there's a limited budget that folks have, and they're trying to figure out, alright, do I spend it on getting more data, and will that improve my data, increase my observation space? Or do I spend it on better models, and improving my models and iterating? And that's a trade-off that people have to make, and of course the answer is "both", but how those funds are allocated is something that organizations are really trying to better understand. There's a lot of trial and error going on. Because obviously, more data, in theory anyway, means you can make better decisions. But it's that iteration of that model, that trial and error and constant improvement, and both of those take significant resources. And budgets are still tight. >> Very true, Dave, and in fact, George Gilbert's research with the community is starting to demonstrate that more of the value's going to come from the models, as opposed to the raw data. We need the raw data to get to the models, but more of the value's going to come from the models. So that's where we think more people are going to focus their time and attention. Because the value will be in the insights and the models. But to go back to your point: where do you put your money? Well, you got to run these pilots, you got to keep up with your competitors, you got to serve customers better, so you're going to have to build all these models, sometimes in a bespoked way. But George is publishing an enormous amount of research right now that's very valuable to a lot of our community members that really shows how that pipeline, how those analytic pipelines or the capabilities associated with those analytic pipelines are starting to become better understood. So that we can actually start getting experience and generating efficiencies or generating a scale out of those analytic pipelines. And that's going to be a major feature underlying this basic trend. Now, this notion of people is really crucial, because as we think about the move to the Internet of Things and People, we have to ask ourselves: has digital engagement really, fully considered what it means to engage people throughout their customer journey? And the answer is: no, it hasn't. We believe that by 2022, IT will be given greater responsibility for management of demand chains. Working to unify customer journey designs and operations across all engagement functions. And by engagement functions, we mean marketing, sales, we mean product, we mean service, we mean fulfillment. That doesn't mean that they all report to IT. Don't mean that, at all. But it means that IT is going to have to, again, find ways to apply data from all these different sources so that it can, in fact, simplify and unify and bring together consistent design and operations so that all these functions can be successful and support reorganization if necessary, because the underlying systems provide that degree of unity and focus on customer success. Now, this is in strong opposition to the prediction made a few years ago, that marketing was going to emerge as the be-all and end-all, that's going to spend more than IT. That was silly, it hasn't happened, and you'd have to redefine marketing very aggressively to see that actually happening. But, when we think about this notion of putting more data to work, the first thing we note, and this is what all the digital natives have shown us, the data can transform a product into a service. That is the basis for a lot of the new business models we're talking about, a lot of these digital native business models and successes that they've had, and we think it's going to be a primary feature of the IT mandate to help business understand how data, more data can be put to work, transforming products into services. It also means, at a tactical level, that mobile applications have been way too focused on solving the seller's problems. We want to caution folks, don't presume that because your mobile application has gotten lost in some online store somewhere that that means that digital engagement's a failure. No, it means that you have to focus digital engagement on providing value throughout the customer journey, and not just from the problem to the solution, where the transaction for money takes place. Too much mobile applications, or too many mobile applications have been focused, in a limited way, on the marketers' problem within the business, of trying to generate, trying to generate awareness and demand. And it has to be, mobile has to be applied in a coherent and comprehensive way, across the entire journey. And ultimately, I hate to say this, but we think collaboration's going to make a comeback. But collaboration to serve customers. So the business can collaborate better inside, but in support of serving the customers. Major, major feature of what we think is going to happen over the course of the next couple years. >> I think the key point there is we all, there's many mobile apps that we love, and utilize, but there are a lot that are not so great. And the point that we've made to the community, quite often, is that it used to be that the brands had all the power, they had all the information, there was an asymmetry of information, the customer, the consumer didn't really know much about pricing. The web, obviously, has leveled that playing field and what many brands are trying to do is recreate that asymmetry and maybe got over their skis a little bit, before providing value to the customers. And I think your point is that, to the extent that you can provide value to that customer, that information advantage will come back to you. But you can't start with that information advantage. >> Great point, Dave. But it also means that we need to, that IT needs to look at the entire journey and see transactions and the discover, evaluate, buy, apply, use and fix throughout this entire journey and find ways of designing systems that provide value to customers at all times and in all places. So the demand chain notion, which historically has been focused on trying to optimize the value that the buyer gets in the buy process, at a cost-effective way, that notion of demand chain has to be applied to the entire engagement lifecycle. Alright, so that's 2022. Let's take a crack at our big prediction for 2027. And it's at, ah, it's on a lot of people's minds. Will we all work for AI? There've been a lot of studies done, over the course of the past year, year and a half, that have been kind of suggested that 47 percent of jobs are going to go away, for example. And that's not, that's not the only high end. Actually, folks have suggested much more, over the next 10, 15 years. Now, if you take a look at the right-hand side, you see a robot thinker. Now, you may not know this, but when The Thinker was actually first, when Rodan first constructed The Thinker, what he was envisioning was actually someone looking down into the seven levels of Hell as described by Dante. And I think that a lot of people would agree that the notion of no work is a Hell for a lot of people. We don't think that that's going to happen in the same way that most folks do. We believe that AI technology advances will far outpace the social advances. Some tasks will be totally replaced, but most jobs will only be partially replaced. We have to draw a clear distinction between the idea that a job performs only this or that task, as opposed to a job or an individual, an employee, as part of a complex community that ensures that a business is capable of serving customers. It doesn't mean we're not going to see more automation, but automation is going to focus mostly on replacing tasks. And to the degree that that task sets a particular job is replaced, then those jobs will be replaced. But ultimately, there's going to be a lot of social friction that gates how fast this happens. One of the key reasons for the social friction is something in behavioral economics that's known as loss avoidance. People are more afraid of losing something than they are of gaining something. And, whether it's a union or whether it's regulations or any number of other factors, that's going to gate the rate at which this notion that AI crushes employment occurs. AI will tend to compliment, not substitute for labor. And that's been a feature of technology for years. It doesn't, again, mean that some tasks and some task sets, sort of those in line with jobs, aren't replaced; there will be people put out of work as a consequence of this. But for the most part, we will see AI tend to compliment, not fully substitute for most jobs. Now this creates, also, a new design consideration. Historically, as technologists, we've looked at what can be done with technology, and we've asked: can we do it? And if the answer is "yes", we tend to go off and do it. And now, we need to start asking ourselves: should we do it? And this is not just a moral imperative. This has other implications, as well. So, for example, the remarkably bad impact that a lot of automated call centers have had on customer service from a customer experience standpoint. This has to become one of the features of how we think about bringing together, in these systems of enaction, all the different functions that are responsible for serving a customer. Asking ourselves: well, we can do it, from a technical standpoint, but should we do it from a customer experience, from a community relations, and even from a, ah, from a cultural imperative standpoint, as we move forward? >> Okay, I'll be brief, because we're wrapping up here, but first of all, machines have always replaced humans. When, largely with physical tasks, now we're seeing that occur with cognitive tasks. People are concerned, as Peter said. The middle class is obviously under fire. The median income in the United States has dropped from $55,000 in 1999 to just above $50,000 today. So, something's going on, and clearly you can look around and see whether it's an an airport with kiosks or billboards, electronic machines and cognitive functions are replacing human functions. Having said that, we're sanguine, because the, the story I'll tell is that the greatest chess player in the world is not a machine. When Deep Blue beat Gary Kasparov, what Gary Kasparov did is he started a competition to collaborate with other, you know, human chess players with machines, to beat the machine, and they succeeded at that, so this, again, I come back to this combination of technologies. Combinatorial technologies are really what's going to drive the innovation curve over the next, we think, 20 to 50 years. So, it's something that is far out there, in terms of our predictions, but it's also something that is relevant to the society, and obviously the technology industry. So thank you, everybody. >> So, we have one more slide, and it's Conclusions Slide, so let me hit these really quick, and before I do so, let me note that George, our big data analyst is George Gilbert. George Gilbert: G-I-L-B-E-R-T. Alright, so, very quickly, tech architecture question, we think edge IoT is going to have a major effect in how we think about architecture of the future. Micro-processor options? Yup, new micro-processor options are going to have an impact in the marketplace. Whither HDDs? For the performance side of storage, flash is coming on strong. Code in the cloud? Yes, the technologies are great, but development has to change its habits. Amazon momentum? Absolutely going to continue. Big data complexity? It's bad and we have to find ways to make it simpler so that we can focus more on the outcomes and the results, as opposed to the infrastructure and the tooling. 2022, new IT mandate? Drive the value of that data. Get more value out of your data. The Internet of Things and People is going to become the proper way of thinking about how these new systems of enaction work, and we anticipate that demand chain management is going to be crucial to extending the idea of digital engagement. Will we all work for AI? Dave just mentioned, as we said, there's going to be dislocation, there's going to be tasks that are replaced, but not by 2027. Alright, so thank you very much for your time, today. Here is how you can contact Dave and myself. We will be publishing this, the slides and this broadcast. Wikibon's going to deliver three coordinated predictions talks over the course of the next two days, so look for that. Go up to SiliconANGLE, we're up there a fair amount. Follow us on Twitter, and we want to thank you very much for staying with us during the course of this session. Have a great day.