>> Announcer: From around the globe, it's theCUBE! With digital coverage of IBM Think 2021. Brought to you by IBM. >> Well welcome everyone as theCUBE continues our IBM Think series. It's a pleasure to have you with us here on theCUBE. I'm John Walls, and we're joined today by Brian Loveys who is the Director of Offering Management for Customer and Employee Care Applications at IBM in the Data and AI Division. So, Brian, thanks for joining us from Ottawa, Canada. Good to see you today. >> Yeah, great to be here, John. And looking forward to the session today. >> Which, by the way, I've learned Ottawa are the home of the world's largest ice skating rink. I doubt we get into that today, but it is interesting food for thought. So, Brian, first off, let's just talk about the AI landscape right now. I know IBM obviously very heavily invested in that. Just in terms of how you see this currently in terms of enterprise adoption, what people are doing with it, and just how you would talk about the state of the industry right now. >> You know, it's a really interesting one, right? I think if you look at it, you know, different companies, different industries, frankly, are at different stages of their AI journey, right? I think for me personally, what was really interesting was, and we're all going through the pandemic right now, but last year with COVID-19 in the March timeframe, it was really interesting to see the impact, frankly, in the space that I play predominantly in around customer care, right? When the pandemic hit, immediately call centers, contact centers got flooded with calls, right? And so it created a lot of problems for organizations. But what was interesting to me is it accelerated a lot of adoption of AI to organizations that typically lag in technology, right? So if you think about public sector, right, that was one area that got hit very, very hard with questions and those types of things, and trying to, you know, communicate out information. So it was really interesting to see those organizations, frankly, accelerate really, really quickly, right? And if you actually, you know, talk to those organizations now, I think one of the most interesting things to me in thinking about it and talking to them now is like, hey, you know, we can do this, right? AI is really not that complicated. It can be simplified, we can take advantage of it and all of those types of things, right? So I think for me, you know, I kind of see different industries at sort of different levels, but I think with COVID in particularly, you know, and frankly not just COVID, but even digital transformation alongside COVID is really driving a lot of AI in an accelerated manner. The other thing that I'll kind of talk to a little bit here is I still think we're very much in the early innings of this, right? There's a tremendous opportunity to innovate in this space. And I think we all know that, you know, data is continually being created every single day. And as more people become even more digitalized, there's more and more data being created. Like it's how do you start to harness that data more effectively, right, in your business every day. And frankly, I think we're just scratching the surface on it. And I think tremendous amount of opportunity as we move forward. >> Yeah, you really raised an interesting point which I hadn't thought about in terms of, we think about disruptors, we think about technology being a disruptor, right, but in this case it was purely, or really largely environment, you know, that was driving this disruption, right, forcing people to make these adoption moves and transitions maybe a little quicker than they expected. Well, so because of that, because maybe somebody had to speed up their timetable for deployments and what have you, what kind of challenges have they run into then, where, because as you describe it, it's not been the more organic kind of decision-making that might be made sometimes, situation dictated it. So what have you seen in terms of challenges, you know, barriers, or just a little more complexity, perhaps, for some people who're just now getting into the space because of the environment you were talking about? >> I think a lot of this is like, you know, people don't know where to get started, right, a lot of the time, or how AI can be applied. So a lot of this is going to be about education in terms of what it can and cannot do. And then it all depends on the use cases you're talking about, right? So if I think about, you know, building out machine learning models and those types of things, right, you know, the set of challenges that people will typically face in these types of things are, you know, how do I, you know, collect all the data that I need to go build these models, right? How do I organize that data? You know, how do I get the skillsets needed to ultimately, you know, take advantage of all of that data to actually then apply to where I need it in my business, right? So a lot of this is, you know, people need to understand those concepts or those pieces to ultimately be successful with AI. And you know, what IBM is doing right here, and I'll kind of, this will be a key theme throughout this conversation today is, you know, how do you sort of lower the time to value to get there across that spectrum, but also, you know, frankly, the skills required along the way as well? But a lot of it is like, people don't know what they don't know at the end of the day. >> Well, let me ask you about your AI play then. A lot of people involved in this space, as you well know, competition's pretty fierce and pretty widespread. There's a deep bench here. In terms of IBM though, what do you see as kind of your market differentiator then? You know, what do you think sets you apart in terms of what you're offering in terms of AI deployments and solutions? >> No, that's a great question. I think it's a multifaceted answer, frankly. The first thing I'll kind of talk through a little bit, right, is really around our platform and our framework, right? We kind of refer to as our AI ladder, but it's really an integrated, you know, sort of cohesive platform for companies around the journey to AI, right? So kind of what I was mentioning a bit earlier, right? If you think about, you know, AI is really about supplying the right data into AI, and then being able to infuse it to where you need it to go, right? So to do that, you need a lot of the underlying information architecture to do that, right? So you need the ability to collect the data. You need the ability to organize the data. You need the ability to build out these models or analyze the data, right? And then of course you need to be able to infuse that AI wherever you need it to be, right? And so we have a really nice integrated platform that frankly can be deployed on any cloud, right, so we get the flexibility of that deployment model with that integrated platform. And if you think about it, we also have built, right, you know, sort of these industry-leading AI applications that sit on top of that platform and that underlying infrastructure, right? So Watson Assistant, right, our conversational AI which we'll talk probably a little bit more on this conversation, right? Watson Discovery focused on, you know, intelligent document processing, right, AI search type applications. We've got these sort of market-leading applications that sit on top, but there's also other things, right? Like we have a very, very strong research arm, right, that continues to invest and funnel innovations into our product platform and into our product portfolio, right? I think many people are aware of Project Debater we took on some of the top debaters in the world, right? But research ultimately is very much tied, right, and even, you know, some of the teams that I work with on the ground, we've got them tied directly into the squads that build these products, right? So we have this really big strong research arm that continues to bring innovation around AI and around other aspects into that product portfolio. But it's not just- >> I'm sorry go ahead, please. >> Go ahead, sorry. >> No, no, you go, (laughs) I interrupted, you go ahead. >> Don't worry, I was just going to say, the other two things I'll say like, you know, I'm saying this right, but we've got a lot of sort of proof points in around it, right, so if you talk about the scale, right, the number of customers, the number of case studies, the number of references across the board, right, in around AI at IBM it is significant, right? And not only that, but we've got a lot of, sort of I'll say industry and third-party industry recognition, right? So think about most people are aware of sort of Gartner Magic Quadrants, right, and we're the leader almost across the board, right, or a leader across the board. So, you know, cloud AI developer service, insight engines, machine learning, go down the line. So, you know, if you don't trust me, there's certainly a lot of third party validation around that as well, if that makes sense. >> Yeah, sure does. You know, we hear a lot about conversational AI and, you know, with online chat bots and voice assistance, and a myriad applications in that respect. Let's talk about conversational right now. Some people think is a little narrow, but yet there appears to be a pretty broad opportunity at the same time. So let's talk about that conversational AI element to what you're talking about at IBM and how that is coming into play. And perhaps is a pretty big growth sector in this space. >> Yeah, I think, again, I talk about scratching the surface, early innings, you'll see that theme a lot too. And I think this is another area around that, right? So, listen, let's talk about the broader side. Let's first talk about where conversational AI is typically applied, right? So you see it in customer service. That's the obvious place where I've seen the most deployments in. But if you think about, it's not just really around customer service, right? There's use cases around sales and marketing. You can think about, you know, lead qualification for example, right. You know, I'm on a website, how can I get information about a product or service? How can I automate some of that information collection, answering questions, how can I schedule console? All those things can be automated using, right, conversational AI, but organizations don't want these sort of points solutions across the customer journey. What they're ultimately looking for is a single assistant to kind of, you know, front that particular customer. So what if I do come on from a lead qual perspective, but really I'm not there for lead qual, I'm actually a customer, and I want to get a question answered, right? You don't want to have these awkward starts and stops with organizations, right? So on the customer side where we see the conversational AI going is really sort of covering that whole gambit in terms of that customer journey, right? And it's not just the customer journey, but you also want to be across channels, right? So you can imagine not just, you know, the website and the chat on the website, but also, right, across your messaging channels, across your phone, right? And not just that, but you also want to be able to have a really nice experience around, hey maybe I'm on a phone call with some automation, but I need to be able to hand them off to a digital play, right? Maybe that's easier to sign up for a particular offer, or do some authentication, or whatever it might be, right? So to sort of be able to switch between the channels is really, really going to become more important in terms of a seamless experience as you do kind of go through it, right- >> So let's talk about customers- >> Oh, go ahead sir. >> Yeah, you talked about customers a little bit, and you mentioned case studies, but I hope we can get into some specifics, if you can give us some examples about people, companies with whom you've worked and some success that you've had in that respect. And I think maybe the usual suspects come to mind. I think about finance, I think about healthcare, but you said, "Hey buddy, but customer call issues, you know, service centers, that kind of thing would certainly come into play," but can you give us an idea or some examples of deployments and how this is actually working today? >> Oh, absolutely, right? So I think you were kind of mentioning, you were talking about sort of industries that are relevant, right? So, you know, the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer side of it, right? So clearly in financial services, banks, insurance are clearly obvious ones. Telecommunication, retail, healthcare, these are all sort of big industries with a lot of sort of customers coming in, right? And so you'll see different use cases in those industries as well, right? So the obvious one, we've got a really good client, Royal Bank of Scotland, they've now changed their name to NatWest in Scotland. So they started out with customer service, right? So dealing with personal banking questions through their website. What's interesting, and you'll see this with a lot of these use cases is they will start small, right, with a single use case, but they'll start to expand from there. So for example, NatWest, right, they're starting with personal banking, but they're now expanding to other areas of the business across that customer journey, right? So that's a great example of where we've seen it. Cardinal Health, right, because we're not dealing with customers in terms of external customers, but dealing with internal customers, right, from an IT help desk standpoint. So it's not always external customers. Oftentimes, frankly, it can be employees, right? So they are using it through an IDR system, right? So through over the phone, right, so I can call, instead of getting that 1-800 number, I'm going to get a nice natural language experience over the phone to help employees with common problems that they have with their help desk. So, and they started really, really small, right? They started with, you know, simple things like password resets, but that represented a tremendous amount of volume that ultimately hit at their call centers. So NatWest is a great example. CIBC, another bank in Canada, Toronto, is a great example. And the nice thing about what CIBC is doing and they're a big, you know, we have four big banks here in Canada. What CIBC do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money, or all those types of things, or check your balance or whatever it might be. So putting a nice, simple interface on some of those common, transactional things that you would do with a bank as well. >> You know, before I let you go, I'd like to hit just a buzzword we hear a lot of these days, natural language processing, NLP. All right, so NLP, define that in terms of how you see it and how is it being applied today? Why does NLP matter, and what kind of differences is it making? >> Wow, natural language processing is a loaded term as a buzzword, I completely agree. I mean, listen, at the 50,000 foot level, natural language processing is really about understanding language, right? So what do I mean by that? So let's use the simple conversational example we just talked about. If somebody's asking about, you know, "I'd like to reset my password," right? You have to be able to understand, well what is the intent behind what that user is trying to do, right? They're trying to reset a password, right? So being able to understand that inquiry that user has that's coming in and being able to understand what the intent is behind it. That's sort of one key aspect of natural language processing, right? What is the intent or the topic around that paragraph or whatever it might be. The other sort of key thing around natural language processing, the importance of extracting certain things that you need to know. And again, using the conversational AI side, just for a minute, to give a simple example. If I said, "You know what, I need to reset my password." I know what the intent is, I want to reset a password, but, right, I don't know which password I'm trying to reset. Right, and so this is where sort of you have to be able to extract objects, and we call them entities a lot of the time and sort of the (indistinct) or lingo. But you got to be able to extract those elements. So, you know, I want to reset my ATM password. Great, right, so I know what they're trying to do, but I also need to extract that it's the ATM password that I'm trying to do. So that's one sort of key angle, natural language processing, and there's a lot of different AI techniques to be able to do those types of things. I'll also tell you though, there's a lot around the content side of the fence as well. So you can imagine how like a contract, right, and there were thousands of these contracts, and some of your terms may change. You know, how do you know, out of those thousands of contracts where the problems are, where I need to start looking, right? So another sort of key area of natural language processing is looking at the content itself, right? Can I look at these contracts and automatically understand that this is an indemnity clause, right? Or this is an obligation, right? Or those types of things, right, and being able to sort of pick those things out, so that I can help deal with those sort of contract-processing things. So that's sort of a second dimension. The third dimension I'll kind of give around this is really around, you can think about extracting things like sentiment, right? So we talked about, you know, extracting objects and nouns, and those types of things, but maybe I want to know in an analytics use case with customers, you know, what is the sentiment and, you know, analyzing social media posts or whatever it might be, what's the sentiment that people have around my product or service. So natural language process, if you think about it at the real high level is really about how do I understand language, but there's a variety of sort of ways to do that, if that makes sense. >> Yeah, no sure, and I think there are a lot of people out there saying, "Yeah, the sooner we can identify exasperation (laughs) the better off we're going to be, right, in handling the problems." So, it's hard work, but it's to make our lives easier, and congratulations for your fine work in that space. And thanks for joining us here on theCUBE. We appreciate the time today, Brian. >> Thank you very much. >> You bet, Brian Loveys, he's talking to us from IBM, talking about conversational AI and what it can do for you. I'm John Walls, thanks for joining us here on theCUBE. (upbeat music) ♪ Dah, deeah ♪ ♪ Dah, dee ♪ (chimes ringing)

>>from >>Around the globe. It's the cube with digital coverage of IBM think 2021 brought to you by IBM >>Well welcome everyone is the cube continues or IBM Thanks series. It's a pleasure to have you with us here on the cube. I'm john walls and we're joined today by brian loves who is the director of offering management for customer and employee care applications in the at IBM in the data and AI division. So brian, thanks for joining us from Ottawa Canada, good to see you today. >>Yeah, great to be here john I'm looking forward to the session today >>which by the way I've learned Ottawa is the home of the world's largest ice skating rink. I doubt we'll get into that today, but it is interesting food for thought. Uh so brian first off, let's just talk about um the Ai landscape right now. I know IBM obviously very heavily invested in that uh just in terms of how you see this currently as in terms of enterprise adoption, what people are doing with it and and just how you would talk about the state of the industry right now, >>you know, it's a really interesting one, right? I think if you look at it, you know different companies, different industries frankly are at different stages of their Ai journey, right? Um I think for me personally what was really interesting was, and we're all going through the pandemic right now, but last year with covid 19 in the March timeframe, it was really interesting to see the impact, frankly in the space that I played predominantly in around customer care, right? When the pandemic hit immediately call centers, contact centres got flooded with calls, right? And so it created a lot of problems for organizations. But it was interesting to me is it accelerated a lot of adoption of ai to organizations that typically lag and technology. Right? So if you think about public sector, right, that was one area that got hit very, very hard with questions and those types of things and trying to communicate and communicate out information. So it was really interesting to see those organizations frankly accelerate really, really quickly, right? And if you actually talk to those organizations now, I think one of the most interesting things to me and thinking about it and talking to them now is like, hey, you know, we can do this right, AI is really not that complicated, it can be simplified, we can take advantage of it and all of those types of things. Right? So I think for me, you know, I kind of see different industries that sort of different levels, but I think with Covid in particularly, you know, and frankly not just Covid, but even digital transformation alongside Covid is really driving a lot of ai in an accelerated manner. The other thing I'll kind of I'll kind of talk to a little bit here is I still think we're very much in the early innings of this, right, there is a tremendous opportunity innovating in the space and I think we all know that you know data is continually being created every single day and as more people become even more digitalized, there's more and more data being created. Like how do you start to harness that data more effectively, right in your business every day? And frankly I think we're just scratching scratching the surface on it and I think tremendous amount of opportunity as we move forward. >>Yeah, he really is really raised an interesting point which I hadn't thought about in terms of, we think about disruptors, we think about technology being a disrupter, right? But in this case it was purely really, largely environment that was driving this disruption, right, forcing people to to make these adoption moves and transitions maybe a little quicker than they expected. So because of that, because maybe somebody had to speed up their timetable for deployments and what have you what what kind of challenges have they run into them? Where because, as you describe it, it's not been the more organic kind of decision making that might be made, sometimes situation dictated it. So what have you seen in terms of challenges, barriers or just a little more complexity perhaps for some people who are just not getting into the space because of the environment you were talking about? >>I think a lot of this is like people don't know where to get started, right, a lot of the time or how ai can be applied. So a lot of this is going to be a bad education in terms of what it can and cannot do, and then it all depends on the use cases you're talking about, right? So if I think about, you know, building a machine learning models and those types of things right? You know, this set of challenges that people will typically face in these types of things are, you know, how do I collect all the data that I need to go build these models? Right? How do I organize that data? Um you know, how do I get the skill sets needed to ultimately, you know, take advantage of all that data to actually then apply to where I needed in my business? Right, So a lot of this is, you know, people need to understand, you know, those concepts are those pieces um to ultimately be successful with AI and you know what IBM is doing right here and I'll kind of this will be a key theme through this conversation today, is how do you sort of lower the time to value, to get there across that spectrum, but also, you know, frankly the skills >>required along the way as >>well, but a lot of it is like people don't know what they don't know at the end of the day. Mhm. >>Well, let me ask you about about your AI play then, a lot of people involved in this space, as you well know, you know, competitions pretty fierce and pretty widespread, there's a deep bench here um in terms of IBM know, what do you see is kind of your market different differentiator then, you know, what what do you think set you apart in terms of what you're offering in terms of AI deployments and solutions? >>No, that's a great question. I think it's a multifaceted answer, frankly. Um the first thing I'll kind of talk through a little bit right, is really around our platform and our our framework, right? We could refer to as our air ladder, um but it's really an integrated, you know, sort of cohesive platform for companies around the journey to AI, right? So kind of what I was mentioning earlier, right? If you think about, you know, AI is really about supplying the right data into A I. And then being able to infuse it to where you needed to go. Right? So to do that, you need a lot of the underlying information architecture to do that, Right? So you need the ability to collect the data, you need the ability to organize the data, you need the ability to to build out these models, right? Or analyze the data and then of course you need to be able to infuse that ai wherever you need it to be. Right. And so we have a really nice integrated platform that frankly can be deployed on any cloud. Right? So we got the flexibility that deployment model with that in greater platform. And you think about it? We also have built right, you know, sort of these industry leading Ai applications that sit on top of that platform and that underlying infrastructure. Right? So Watson assistant, Right. Our conversational AI, which we'll talk probably a little bit more on this conversation. Right, Watson discovery focus on, you know, intelligent document processing, right. AI search type applications. We've got these sort of market leading applications that sit on top, but there's also other things, right? Like we have a very, very strong research arm right, that continues to invest and funnel innovations into our product platform and into our product portfolio. Right? I think many people are aware of project debater, we took on some of the top debaters in the world, right? But research ultimately is very much tied, right? And even some of the teams that I work with on the ground, we've got them tied directly into the squads that build these products, Right? So we have this really big strong research arm that continues to bring innovation around AI and around other aspects into that product portfolio. But it's not just go ahead, >>Please go ahead. three. No, no. You know, I interrupted you. Go ahead. >>No, I was just gonna say that the other two things, I'll say it like, you know, I'm saying this right, but we've got a lot of sort of proof points and around it. Right? So, if you talk about the scale right? The number of customers, the number of case studies, a number of references across the board, right? In around AI AT IBM It is significant, Right? Um, and not only that, but we've got a lot of sort of, I'll say industry and third party industry recognition. Right? So think about most people are aware of sort of Gartner magic quadrants, right? And we're the leader almost across the board, Right? Or a leader across the board. So cloudy I developer service inside engines, machine learning go down the line. So, you know, if you don't trust me, there's certainly a lot of third party validation around that as well. That makes sense. >>Yeah, it sure does. You know, we're hearing a lot about conversational AI and, you know, with online chat bots and voice assistance and a myriad applications in that respect. Let's talk about conversational right now. Some people think it's little narrow, but, but yet there appears to be a pretty broad opportunity at the same time. So let's talk about that conversational AI um, uh, element um, to what you're talking about at IBM and how that is coming into play and, and perhaps is a pretty big growth sector in this space. >>Yeah, I think again, I talked about scratching the surface early innings. You'll see that theme a lot too. And I think this is another area around that. So listen, let's talk about the broader side. Let's first talk about where conversation always typically applied. Right? So you see it in customer service, that's the obvious place we're seeing the most appointments in. But if you think about, it's not just really around customer service, right? There's use cases around sales and marketing. If you think about, you know, lead qualification, for example, right? How can, you know, I'm on a website, how can I get information about a product or service? How can I automate some of that information collection, answering questions? How can I schedule console? All those things can be automated using great conversationally. I, the organizations don't want these sort of point solutions across the customer journey. What we're ultimately looking for is a single assistant to kind of, you know, front right, that particular customer. So what if I do come on from a legal perspective, but really I'm not here for legal. I'm actually a customer and I want to get a question answered, right? You don't want to have these awkward starts and stops with organizations, Right? So on the customer side where we see the conversation like, hey, I going and it's really kind of covering that full gambit in terms of that customer journey, right? And it's not just the customer journey, but you also want to be across channels, right? So you can imagine right now, not just, you know, the website and the chat on the website, but also right across their messaging channels, right across your phone. Right. And not just that, but you also want to be a really nice experience around, hey, maybe I'm on a phone call with some automation, but I need to be able to hand them off to a digital play. Right? Maybe that's easier to sign up for a particular offer or do some authentication or whatever might be, right. So to sort of be able to sort of switch between the channels, it's really, really going to become more important in this sort of sort of seamless experience as you just kind of go through it. Right? >>So you're coming by customers. Yeah. >>You talked about customers a little bit and you mentioned case studies, but can we get, I hope we can get into some specifics. You can give us some examples about people, companies with whom you've worked and and some success that you've had that respect. And I think maybe the usual suspects come to mind about finance. I might health care, but you said anybody with customer call issues, service centers, that kind of thing would certainly come into play. But can you give us an idea or some examples of deployments and how this is actually working today? >>Oh, absolutely. Right. So I think you kind of mentioned you become sort of industries that are relevant. Right? So, you know, the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer sort of side to it. Right? So clearly in financial services, banks, insurance, and clearly obvious ones telecommunications, retail, healthcare, these are all sort of big industries with a lot of sort of customers coming in. Right? So you'll see different use cases in those industries as well. Right. So the obvious one, we've got a really good client, Royal Bank of Scotland, they've now changed their name to natwest Open Scotland. Um So they started out with customer service. Right? So dealing with personal banking questions through their website, what's interesting and you'll see this with a lot of these use cases is they will start small, right with a single use case that they'll start to expand from there. So, for example, >>natwest right there, starting with they started with personal banking, but they're not expanding to other areas of the business across that customer journey. Right. So it's a great example of where we've seen it. Cardinal Health Right. We're not dealing with customers in terms of external customers but dealing with internal customers right from the help that standpoint. So it's not always external customers. Oftentimes frankly it can be employees. Right? So they are using it right through an I. V. R. System. Right? So through over the phone. Right. So I can call instead of getting that 1 800 number. I'm going to get a nice natural language experience over the phone to help employees with common problems that they have with their health does so. And they started really, really small, right? They started with simple things like password resets but that represented a tremendous amount of volume but ultimately headed their cost cost centers. So not West is a great example. C I B C. Another bank in Canada Toronto is a great example and the nice thing about what CNBC is doing and there are big, you know, we have four big banks here in Canada, what have you seen do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money or over those types of things or check your balance or whatever it might be. So putting a nice simple interface on some of those common transactional things that you >>would do with the bank as well, >>you know, before I let you go, uh I'd like to hit this of buzz where we hear a lot of these days natural language processing. NLP Alright, so, so NLP define that in terms of how you see it and and how is it being applied today? Why why does NLP matter? And what kind of difference is it making? >>Wow, that's a loaded natural language processing. There's a loaded term in a buzzword. I completely agree. I mean listen, at the 50,000 ft level, natural language processing is really about understanding length, Right? So what do I mean by that? So let's use the simple conversational example. We just talked about if somebody is asking about, I'd like to reset my password right? You have to be able to understand what is the intent behind what that user is trying to do right there? Trying to reset a password, right? So being able to understand that inquiry that the user has that's coming in and being able to understand what the intent is behind it. >>That's sort of one, you know, aspect of natural language processing, right? What is the intent or the topic around that paragraph or whatever it might be. The other sort of key thing around natural language processing the importance, extracting certain things that you need to know. And again using the conversational ai side, just for a minute to give a simple example if I said you know what I need to reset my password, I know what the intent is. I want to reset a password but Right I don't know which password I'm trying to reset. Right? So this is where you have to be able to extract objects and we call them entities a lot of time in sort of the ice bake or lingo but you've got to be able to extract those elements. So you know I want to reset my A. T. M. Password. Great. Right so I know what they're trying to do but I also need to extract that it's the A. T. M. Password that I'm trying to do. So that's one sort of key angle of natural language processing and there's a lot of different techniques to be able to do those types of things. I'll also tell you though there's a lot around the content side of the fence as well, right? So you can imagine having a contract, right? And there are thousands of these contracts and some of your terms may change. How do you know, out of those thousands of contracts where the problems are, where I need to start looking, Right? So another sort of keep key area of natural language processing is looking at the content itself. Can I look at these contracts and automatically understand that this is an indemnity clause, Right? And this is an obligation, right? Or those types of things, right? And be able to sort of pick pick those things out so that I can help deal with those sort of contract processing things. That's sort of a second dimension. The third dimensional kind of kind of give around this is really around. You can think about extracting things like sentiment, right? So we talked about, you know, extracting objects and downs and those types of things. But maybe I want to know and analytics use case with customers. Um you know, what is the sentiment and you know, analyzing social media posts or whatever it might be. What's the sentiment that people have around my product or service? So naturally this process, if you think about it, the real high level is really about how do I understand language? But there's a variety of sort of ways to do that if that makes sense? >>Yeah, sure. And I think there's a lot of people out there saying, yeah, the sooner we can identify exasperation, the better off we're going to be right and handling the problems. But it's hard work but it's to make our lives easier and congratulations for your fine work in that space. And thanks for joining us here on the cube. We appreciate the time. Today, brian, >>thank very much. >>You bet BRian Levine is talking to us from IBM talking about conversational Ai and what it can do for you. I'm john Walsh, thanks for joining us here on the cube. Mhm. >>Mhm.

>> (vocalizing) >> Narrator: From theCube studios in Palo Alto and Boston, it's theCube. Covering IBM Think, brought to you by IBM. >> Hi everybody, this is Dave Vallante and this is our continuing coverage of Think 2020, the digital event experience. This is the post-thing, the sort of halo effect, the afterthoughts, and joining me is Rob Thomas, he's back. The Senior Vice president of Cloud and Data Platform. Rob, thanks for taking some time to debrief on Think. >> Absolutely Dave, great to be here, good to see you again. >> Yeah, so you have a great event, you guys put it together in record time. I want to talk about sort of your innovation agenda. I mean, you are at the heart of innovation. You're talking cloud, data, AI, really the pillars of innovation, I could probably add in edge to extend the cloud. But I wonder if you could talk about your vision for the innovation agenda and how you're bringing that to customers. I mean, we heard from PayPal, you talked about Royal Bank of Scotland, Credit Mutual, a number of customer examples. How are you bringing innovation forward with the customer? >> I wouldn't describe innovation, maybe I'd give it two different categories. One is, I think the classic term would be consumerization, and you're innovating by making interiorized technology really easy to use. That's why we built out a huge design capability, it's why we've been able to get products like Watson Assistant to get companies live in 24 hours. That's the consumerization aspect, just making enterprise products really easy to use. The second aspect is even harder, which is, how do you tap into an institution like IBM Research, where we're doing fundamental invention. So, one of our now strengths in the last couple of months was around taking technology out of IBM Debater, project Debater, the AI system that could debate humans and then putting that into enterprised products. And, you saw companies like PayPal that are using Watson Assistant and now they have access to that kind of language capability. There's only two aspects here, there's the consumerization and then there's about fundamental technology that really changes how businesses can operate. >> I mean, the point you made about speed and implementation in your key note was critical, I mean really, within 24 hours, very important during this pandemic. Talk about automation, you know, you would think by now right, everything's automation. But, now you're seeing a real boom in automation and it really is driven by AI, all this data, so there's seems to be a next wave, almost a renaissance, if you will, in automation. >> There is and I think automation, when people hear first of the term, it's sometimes a scary term. Because people are like hey, is this going to take my job? Gain a lot of momentum for automation is a difficult, repetitive tasks that nobody really wanted to do in the first place. Whether it's things like data matching, containerizing an application. All these are really hard things and the output's great, but nobody really wants to do that work, they just want the outcome. And, as we've started to demonstrate different use cases for automation that are in that realm, a lot of momentum has taken off, that we're seeing. >> I want to come back to this idea of consumerization and simplification. I mean, when you think about what's been happening over the last several years. And, you and I have talked about this a lot, AI for consumer versus AI for business and enterprise. And really, one of the challenges for the encumbrance, if you will, is to really become data driven, put data at the core and apply machine intelligence to that, just to that data. Now the good news is, they don't have to invent all this stuff, because guys like you are doing that and talk about how you're making that simple. I mean, cloud packs is an example of that, simplification, but talk about how customers are going to be able to tap into AI without having to be AI inventors. >> Well, the classic AI problem actually is a data problem, and the classic data problem is data slide over, which is a company has got a lot of data but it's spread across a hundred or a thousand or tens of thousands different repositories or locations. Our strategy when we say a hybrid cloud is about how do we unify those data storage. So, it's called PaaS, on red hat open shift. We do a lot of things like data virtualization, really high performance. So, we take what is thousands of different data sources and we have that packed like a single fluid item. So then, when you're training models, you can train your models in one place and connect to all your data. That is the big change that's happening and that's how you take something like hybrid cloud, and it actually starts to impact your data architecture. And once you're doing that, then AI becomes a lot easier, because the biggest AI challenge that I described is, where's the data? Is the data in a usable form? >> A lot of times in this industry, you know, we go whale hunting, there are a lot of big companies out there, a lot of times they take priority. You know, at the same time though, a lot of the innovations are coming from companies, you know, we've never even heard of that could be multi-billion dollar companies by the end of the decade. So, how can, you know, small companies and mid-sized companies tap into this trend? Is it just for the big whales or could the small guys participate? >> The thing that's pretty amazing about modern cloud and data technology, I'll call it, is it's accessible to companies of any size. When we talked about, you know, the hundred or so clients that have adopted Watson Assistant since COVID-19 started, many of those are very small institutions with no IT staff or very limited IT staff. Though, we're making this technology very accessible. when you look at something like data, now a small company may not have a hundred different repositories, which is fine, but what they do have is they do want to make better predictions, they do want to automate, they do want to optimize the business processes that they're running in their business. And, the way that we've transformed our model consumption base starting small, it's really making technology available to, you know, from anywhere from the local deli to the Fortune 50 Company. >> So, last question is, What are your big takeaways from Think? I would ask that question normally when we're in a live event. It's a little different with the digital event, but there are still takeaways. What was your reaction and what do to leave people with? >> Even as we get back to doing physical events, which I'm positive will happen at some point. What we learned is there is something great about an immersive digital experience. So, I think the future of events is probably higher than this. Meaning, a big digital experience, to complement the physical experience. That's one big takeaway because the reaction was so positive to the content and how people could access it. Second one is the, all the labs that we did. So, for developers, builders, those were at capacity, meaning we didn't even take any more. So, there's definitively a thirst in the market for developing new applications, developing new data products, developing new security products. That's clear just by the attendance that we saw, that's exciting. Now, I'd say third, that is that AI is now moving into the mainstream, that was clear from the customer examples, whether it was with Tansa or UPS or PayPal that I mentioned before, that was talking with me. AI is becoming accessible to every company, that's pretty exciting. >> Well, the world is hybrid, oh you know the lab, the point you're making about labs is really important. I've talked to a number of individuals saying, "Hey I'm using this time to update my skills. I'm working longer hours, maybe different times of the day, but I'm going to skill up." And you know, the point about AI, 37 years ago, when I started in this business AI was all the buzz and it didn't happen. It's real this time and I'm really excited Rob, that you're at the heart of all this innovation, so really, I appreciate you taking the time. And, best of luck, stay safe, and hopefully we'll see you face to face. >> Offscreen Man: Sure. >> Thanks Dave, same to you and the whole team at theCube, take care. >> Thank you Rob, and thank you for watching everybody, this is Dave Vellante for theCube and our coverage of IBM Think 2020, the digital event experience and the post-event. We'll see you next time. (music)

>>live from Miami, Florida. It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, Everybody. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome everybody to the port of Miami. My name is Dave Vellante and you're watching the cube, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM data and AI form. The hashtag is data AI forum. This is IBM's. It's formerly known as the, uh, IBM analytics university. It's a combination of learning peer network and really the focus is on AI and data. And there are about 1700 people here up from, Oh, about half of that last year, uh, when it was the IBM, uh, analytics university, about 600 customers, a few hundred partners. There's press here, there's, there's analysts, and of course the cube is covering this event. We'll be here for one day, 128 hands-on sessions or ER or sessions, 35 hands on labs. As I say, a lot of learning, a lot of technical discussions, a lot of best practices. >>What's happening here. For decades, our industry has marched to the cadence of Moore's law. The idea that you could double the processor performance every 18 months, doubling the number of transistors, you know, within, uh, the footprint that's no longer what's driving innovation in the it and technology industry today. It's a combination of data with machine intelligence applied to that data and cloud. So data we've been collecting data, we've always talked about all this data that we've collected and over the past 10 years with the advent of lower costs, warehousing technologies in file stores like Hadoop, um, with activity going on at the edge with new databases and lower cost data stores that can handle unstructured data as well as structured data. We've amassed this huge amount of, of data that's growing at a, at a nonlinear rate. It's, you know, this, the curve is steepening is exponential. >>So there's all this data and then applying machine intelligence or artificial intelligence with machine learning to that data is the sort of blending of a new cocktail. And then the third piece of that third leg of that stool is the cloud. Why is the cloud important? Well, it's important for several reasons. One is that's where a lot of the data lives too. It's where agility lives. So cloud, cloud, native of dev ops, and being able to spin up infrastructure as code really started in the cloud and it's sort of seeping to to on prem, slowly and hybrid and multi-cloud, ACC architectures. But cloud gives you not only that data access, not only the agility, but also scale, global scale. So you can test things out very cheaply. You can experiment very cheaply with cloud and data and AI. And then once your POC is set and you know it's going to give you business value and the business outcomes you want, you can then scale it globally. >>And that's really what what cloud brings. So this forum here today where the big keynotes, uh, Rob Thomas kicked it off. He uh, uh, actually take that back. A gentleman named Ray Zahab, he's an adventure and ultra marathon or kicked it off. This Jude one time ran 4,500 miles in 111 days with two ultra marathon or colleagues. Um, they had no days off. They traveled through six countries, they traversed Africa, the continent, and he took two showers in a 111 days. And his whole mission is really talking about the power of human beings, uh, and, and the will of humans to really rise above any challenge would with no limits. So that was the sort of theme that, that was set for. This, the, the tone that was set for this conference that Rob Thomas came in and invoked the metaphor of superheroes and superpowers of course, AI and data being two of those three superpowers that I talked about in addition to cloud. >>So Rob talked about, uh, eliminating the good to find the great, he talked about some of the experiences with Disney's ward. Uh, ward Kimball and Stanley, uh, ward Kimball went to, uh, uh, Walt Disney with this amazing animation. And Walter said, I love it. It was so funny. It was so beautiful, was so amazing. Your work 283 days on this. I'm cutting it out. So Rob talked about cutting out the good to find, uh, the great, um, also talking about AI is penetrated only about four to 10% within organizations. Why is that? Why is it so low? He said there are three things that are blockers. They're there. One is data and he specifically is referring to data quality. The second is trust and the third is skillsets. So he then talked about, you know, of course dovetailed a bunch of IBM products and capabilities, uh, into, you know, those, those blockers, those challenges. >>He talked about two in particular, IBM cloud pack for data, which is this way to sort of virtualize data across different clouds and on prem and hybrid and and basically being able to pull different data stores in, virtualize it, combine join data and be able to act on it and apply a machine learning and AI to it. And then auto AI a way to basically machine intelligence for artificial intelligence. In other words, AI for AI. What's an example? How do I choose the right algorithm and that's the best fit for the use case that I'm using. Let machines do that. They've got experience and they can have models that are trained to actually get the best fit. So we talked about that, talked about a customer, a panel, a Miami Dade County, a Wunderman Thompson, and the standard bank of South Africa. These are incumbents that are using a machine intelligence and AI to actually try to super supercharge their business. We heard a use case with the Royal bank of Scotland, uh, basically applying AI and driving their net promoter score. So we'll talk some more about that. Um, and we're going to be here all day today, uh, interviewing executives, uh, from, uh, from IBM, talking about, you know, what customers are doing with a, uh, getting the feedback from the analysts. So this is what we do. Keep it right there, buddy. We're in Miami all day long. This is Dave Olanta. You're watching the cube. We'll be right back right after this short break..

[Music] when welcome to the special exclusive cube conversation here in Palo Alto in our studios I'm John for your host of the cube we have a very special guest speaking for the first time around some alleged alleged accusations and also innuendo around the Amazon Web Services Jedi contract and his firm c5 capital our guest as Andre Pienaar who's the founder of c5 capital Andre is here for the first time to talk about some of the hard conversations and questions surrounding his role his firm and the story from the BBC Andre thanks for a rat for meeting with me John great to have me thank you so you're at the center of a controversy and just for the folks who know the cube know we interviewed a lot of people I've interviewed you at Amazon web sources summit Teresa Carl's event and last year I met you and bought a rein the work you're doing there so I've met you a few times so I don't know your background but I want to drill into it because I was surprised to see the BBC story come out last week that was basically accusing you of many things including are you a spy are you infiltrating the US government through the Jedi contract through Amazon and knowing c-5 capital I saw no correlation when reading your article I was kind of disturbed but then I saw I said a follow-on stories it just didn't hang together so I wanted to press you on some questions and thanks for coming in and addressing them appreciate it John thanks for having me so first thing I want to ask you is you know it has you at the center this firm c5 capital that you the founder of at the center of what looks like to be the fight for the big ten billion dollar DoD contract which has been put out to multiple vendors so it's not a single source deal we've covered extensively on silicon angle calm and the cube and the government the government Accounting Office has ruled that there are six main benefits of going with a sole provider cloud this seems to be the war so Oracle IBM and others have been been involved we've been covering that so it kind of smells like something's going along with the story and I just didn't believe some of the things I read and I want to especially about you and see five capitals so I want to dig into what the first thing is it's c5 capital involved in the Jedi contract with AWS Sean not at all we have absolutely no involvement in the Jedi contract in any way we're not a bidder and we haven't done any lobbying as has been alleged by some of the people who've been making this allegation c5 has got no involvement in the general contract we're a venture capital firm with a British venture capital firm we have the privilege of investing here in the US as a foreign investor and our focus really is on the growth and the success of the startups that we are invested in so you have no business interest at all in the deal Department of Defense Jedi contract none whatsoever okay so to take a minute to explain c5 firm I read some of the stories there and some of the things were intricate structures of c5 cap made it sound like there was like a cloak-and-dagger situation I want to ask you some hard questions around that because there's a link to a Russian situation but before we get to there I want to ask you explain what is c5 capital your mission what are the things that you're doing c5 is a is a British venture capital firm and we are focused on investing into fast-growing technology companies in three areas cloud computing cyber security and artificial intelligence we have two parts our business c5 capital which invests into late stage companies so these are companies that typically already have revenue visibility and profitability but still very fast-growing and then we also have a very early stage startup platform that look at seed state investment and this we do through two accelerators to social impact accelerators one in Washington and one in Bahrain and it's just size of money involved just sort of order magnitude how many funds do you have how is it structure again just share some insight on that is it is there one firm is there multiple firms how is it knows it work well today the venture capital business has to be very transparent it's required by compliance we are a regulated regulated firm we are regulated in multiple markets we regulated here in the US the sec as a foreign investor in london by the financial conduct authority and in Luxembourg where Afonso based by the regulatory authorities there so in the venture capital industry today you can't afford to be an opaque business you have to be transparent at all levels and money in the Western world have become almost completely transparent so there's a very comprehensive and thorough due diligence when you onboard capital called know your client and the requirements standard requirement now is that whenever you're onboard capital from investor you're gonna take it right up to the level of the ultimate beneficial ownership so who actually owns this money and then every time you invest and you move your money around it gets diligence together different regulators and in terms of disclosure and the same applies often now with clients when our portfolio companies have important or significant clients they also want to know who's behind the products and the services they receive so often our boards our board directors and a shell team also get diligence by by important clients so explain this piece about the due diligence and the cross country vetting that goes on is I think it's important I want to get it out because how long has been operating how many deals have you done you mentioned foreign investor in the United States you're doing deals in the United States I know I've met one of your portfolio companies at an event iron iron on it iron net general Keith Alexander former head of the NSA you know get to just work with him without being vetted I guess so so how long a c5 capital been in business and where have you made your investments you mentioned cross jurisdiction across countries whatever it's called I don't know that so we've been and we've been in existence for about six years now our main focus is investing in Europe so we help European companies grow globally Europe historically has been underserved by venture capital we on an annual basis we invest about twenty seven billion dollars gets invested in venture capital in Europe as opposed to several multiples of that in the US so we have a very important part to play in Europe to how European enterprise software companies grow globally other important markets for us of course are Israel which is a major center of technology innovation and and the Middle East and then the u.s. the u.s. is still the world leader and venture capital both in terms of size but also in terms of the size of the market and of course the face and the excitement of the innovation here I want to get into me early career because again timing is key we're seeing this with you know whether it's a Supreme Court justice or anyone in their career their past comes back to haunt them it appears that has for you before we get there I want to ask you about you know when you look at the kind of scope of fraud and corruption that I've seen in just on the surface of government thing the government bit Beltway bandits in America is you got a nonprofit that feeds a for-profit and then what you know someone else runs a shell corporation so there's this intricate structures and that word was used which it kind of implies shell corporations a variety of backroom kind of smokey deals going on you mentioned transparency I do you have anything to hide John in in in our business we've got absolutely nothing to hide we have to be transparent we have to be open if you look at our social media profile you'll see we are communicating with the market almost on a daily basis every time we make an investment we press release that our website is very clear about who's involved enough who our partners are and the same applies to my own personal website and so in terms of the money movement around in terms of deploying investments we've seen Silicon Valley VCS move to China get their butts handed to them and then kind of adjust their scenes China money move around when you move money around you mentioned disclosure what do you mean there's filings to explain that piece it's just a little bit so every time we make an investment into a into a new portfolio company and we move the money to that market to make the investment we have to disclose who all the investors are who are involved in that investment so we have to disclose the ultimate beneficial ownership of all our limited partners to the law firms that are involved in the transactions and those law firms in turn have applications in terms of they own anti-money laundering laws in the local markets and this happens every time you move money around so I I think that the level of transparency in venture capital is just continue to rise exponentially and it's virtually impossible to conceal the identity of an investor this interesting this BBC article has a theme of national security risk kind of gloom and doom nuclear codes as mentioned it's like you want to scare someone you throw nuclear codes at it you want to get people's attention you play the Russian card I saw an article on the web that that said you know anything these days the me2 movement for governments just play the Russian card and you know instantly can discredit someone's kind of a desperation act so you got confident of interest in the government national security risk seems to be kind of a theme but before we get into the BBC news I noticed that there was a lot of conflated pieces kind of pulling together you know on one hand you know you're c5 you've done some things with your hat your past and then they just make basically associate that with running amazon's jedi project yes which i know is not to be true and you clarified that joan ends a problem joan so as a venture capital firm focused on investing in the space we have to work with all the Tier one cloud providers we are great believers in commercial cloud public cloud we believe that this is absolutely transformative not only for innovation but also for the way in which we do venture capital investment so we work with Amazon Web Services we work with Microsoft who work with Google and we believe that firstly that cloud has been made in America the first 15 companies in the world are all in cloud companies are all American and we believe that cloud like the internet and GPS are two great boons which the US economy the u.s. innovation economy have provided to the rest of the world cloud computing is reducing the cost of computing power with 50 percent every three years opening up innovation and opportunities for Entrepreneurship for health and well-being for the growth of economies on an unprecedented scale cloud computing is as important to the global economy today as the dollar ease as the world's reserve currency so we are great believers in cloud we great believers in American cloud computing companies as far as Amazon is concerned our relationship with Amazon Amazon is very Amazon Web Services is very clear and it's very defined we participate in a public Marcus program called AWS activate through which AWS supports hundreds of accelerators around the world with know-how with mentoring with teaching and with cloud credits to help entrepreneurs and startups grow their businesses and we have a very exciting focus for our two accelerators which is on in Washington we focus on peace technology we focus on taking entrepreneurs from conflict countries like Sudan Nigeria Pakistan to come to Washington to work on campus in the US government building the u.s. Institute for peace to scale these startups to learn all about cloud computing to learn how they can grow their businesses with cloud computing and to go back to their own countries to build peace and stability and prosperity their heaven so we're very proud of this mission in the Middle East and Bahrain our focus is on on female founders and female entrepreneurs we've got a program called nebula through which we empower female founders and female entrepreneurs interesting in the Middle East the statistics are the reverse from what we have in the West the majority of IT graduates in the Middle East are fimo and so there's a tremendous talent pool of of young dynamic female entrepreneurs coming out of not only the Gulf but the whole of the MENA region how about a relation with Amazon websites outside of their normal incubators they have incubators all over the place in the Amazon put out as Amazon Web Services put out a statement that said hey you know we have a lot of relationships with incubators this is normal course of business I know here in Silicon Valley at the startup loft this is this is their market filled market playbook so you fit into that is that correct as I'm I get that that's that's absolutely correct what we what is unusual about a table insists that this is a huge company that's focused on tiny startups a table started with startups it double uses first clients with startups and so here you have a huge business that has a deep understanding of startups and focus on startups and that's enormous the attractor for us and terrific for our accelerators department with them have you at c5 Capitol or individually have any formal or conversation with Amazon employees where you've had outside of giving feedback on products where you've tried to make change on their technology make change with their product management teams engineering you ever had at c5 capital whore have you personally been involved in influencing Amazon's product roadmap outside they're just giving normal feedback in the course of business that's way above my pay grade John firstly we don't have that kind of technical expertise in C 5 C 5 steam consists of a combination of entrepreneurs like myself people understand money really well and leaders we don't have that level of technical expertise and secondly that's what one our relationship with AWS is all about our relationship is entirely limited to the two startups and making sure that the two accelerators in making sure that the startups who pass through those accelerators succeed and make social impact and as a partner network component Amazon it's all put out there yes so in in a Barren accelerator we've we formed part of the Amazon partner network and the reason why we we did that was because we wanted to give some of the young people who come through the accelerator and know mastering cloud skills an opportunity to work on some real projects and real live projects so some of our young golf entrepreneurs female entrepreneurs have been working on building websites on Amazon Cloud and c5 capital has a relationship with former government officials you funded startups and cybersecurity that's kind of normal can you explain that positioning of it of how former government if it's whether it's US and abroad are involved in entrepreneurial activities and why that is may or may not be a problem certainly is a lot of kind of I would say smoke around this conversation around coffin of interest and you can you explain intelligence what that was it so I think the model for venture capital has been evolving and increasingly you get more and more differentiated models one of the key areas in which the venture capital model is changed is the fact that operating partners have become much more important to the success of venture capital firms so operating partners are people who bring real world experience to the investment experience of the investment team and in c-five we have the privilege of having a terrific group of operating partners people with both government and commercial backgrounds and they work very actively enough firm at all levels from our decision-making to the training and the mentoring of our team to helping us understand the way in which the world is exchanging to risk management to helping uh portfolio companies grow and Silicon Valley true with that to injuries in Horowitz two founders mr. friendly they bring in operating people that have entrepreneurial skills this is the new model understand order which has been a great source of inspiration to us for our model and and we built really believe this is a new model and it's really critical for the success of venture capitals to be going forward and the global impact is pretty significant one of things you mentioned I want to get your take on is as you operate a global transaction a lots happened a lot has to happen I mean we look at the ICO market on the cryptocurrency side its kind of you know plummeting obsoletes it's over now the mood security children's regulatory and transparency becomes critical you feel fully confident that you haven't you know from a regulatory standpoint c5 capital everything's out there absolutely risk management and regulated compliance and legal as the workstream have become absolutely critical for the success of venture capital firms and one of the reasons why this becomes so important John is because the venture capital world over the last few years have changed dramatically historically all the people involved in venture capital had very familiar names and came from very familiar places over the last few years with a diversification of global economic growth we've seen it's very significant amounts of money being invest invested in startups in China some people more money will invest in startups this year in China than in the US and we've seen countries like Saudi Arabia becoming a major source of venture capital funding some people say that as much as 70% of funding rounds this year in some way or another originated from the Gulf and we've seen places like Russia beginning to take an interest in technology innovation so the venture capital world is changing and for that reason compliance and regulation have become much more important but if Russians put 200 million dollars in face book and write out the check companies bright before that when the after 2008 we saw the rise of social networking I think global money certainly has something that I think a lot of people start getting used to and I want on trill down into that a little bit we talked about this BBC story that that hit and the the follow-on stories which actually didn't get picked up was mostly doing more regurgitation of the same story but one of the things that that they focus in on and the story was you and the trend now is your past is your enemy these days you know they try to drum up stuff in the past you've had a long career some of the stuff that they've been bringing in to paint you and the light that they did was from your past so I wanted to explore that with you I know you this is the first time you've talked about this and I appreciate you taking the time talk about your early career your background where you went to school because the way I'm reading this it sounds like you're a shady character I like like I interviewed on the queue but I didn't see that but you know I'm going to pressure here for that if you don't mind I'd like to to dig into that John thank you for that so I've had the I've had the privilege of a really amazingly interesting life and at the heart of at the heart of that great adventures been people and the privilege to work with really great people and good people I was born in South Africa I grew up in Africa went to school there qualified as a lawyer and then came to study in Britain when I studied international politics when I finished my studies international politics I got head hunted by a US consulting firm called crow which was a start of a 20 years career as an investigator first in crawl where I was a managing director in the London and then in building my own consulting firm which was called g3 and all of this led me to cybersecurity because as an investigator looking into organized crime looking into corruption looking into asset racing increasingly as the years went on everything became digital and I became very interested in finding evidence on electronic devices but starting my career and CRO was tremendous because Jules Kroll was a incredible mentor he could walk through an office and call everybody by their first name any Kroll office anywhere in the world and he always took a kindly interest in the people who work for him so it was a great school to go to and and I worked on some terrific cases including some very interesting Russian cases and Russian organized crime cases just this bag of Kroll was I've had a core competency in doing investigative work and also due diligence was that kind of focus yes although Kroll was the first company in the world to really have a strong digital practice led by Alan Brugler of New York Alan established the first computer forensics practice which was all focused about finding evidence on devices and everything I know about cyber security today started with me going to school with Alan Brolin crawl and they also focused on corruption uncovering this is from Wikipedia Kroll clients help Kroll helps clients improve operations by uncovering kickbacks fraud another form of corruptions other specialty areas is forensic accounting background screening drug testing electronic investigation data recovery SATA result Omar's McLennan in 2004 for 1.9 billion mark divested Kroll to another company I'll take credit risk management to diligence investigator in Falls Church Virginia over 150 countries call Kroll was the first CRO was the first household brand name in this field of of investigations and today's still is probably one of the strongest brand names and so it was a great firm to work in and was a great privilege to be part of it yeah high-end high-profile deals were there how many employees were in Kroll cuz I'd imagine that the alumni that that came out of Kroll probably have found places in other jobs similar to yes do an investigative work like you know they out them all over the world many many alumni from Kroll and many of them doing really well and doing great work ok great so now the next question want to ask you is when you in Kroll the South Africa connection came up so I got to ask you it says business side that you're a former South African spy are you a former South African spy no John I've never worked for any government agency and in developing my career my my whole focus has been on investigations out of the Kroll London office I did have the opportunity to work in South Africa out of the Kroll London office and this was really a seminal moment in my career when I went to South Africa on a case for a major international credit-card company immediately after the end of apartheid when democracy started to look into the scale and extent of credit card fraud at the request of this guy what year was there - how old were you this was in 1995 1996 I was 25 26 years old and one of the things which this credit card company asked me to do was to assess what was the capability of the new democratic government in South Africa under Nelson Mandela to deal with crime and so I had the privilege of meeting mr. Mandela as the president to discuss this issue with him and it was an extraordinary man the country's history because there was such an openness and a willingness to to address issues of this nature and to grapple with them so he was released from prison at that time I remember those days and he became president that's why he called you and you met with him face to face of a business conversation around working on what the future democracy is and trying to look at from a corruption standpoint or just kind of in general was that what was that conversation can you share so so that so the meeting involved President Mandela and and the relevant cabinet ministers the relevant secretaries and his cabinet - responsible for for these issues and the focus of our conversation really started with well how do you deal with credit card fraud and how do you deal with large-scale fraud that could be driven by organized crime and at the time this was an issue of great concern to the president because there was bombing in Kate of a Planet Hollywood cafe where a number of people got very severely injured and the president believed that this could have been the result of a protection racket in Cape Town and so he wanted to do something about it he was incredibly proactive and forward-leaning and in an extraordinary way he ended the conversation by by asking where the Kroll can help him and so he commissioned Kroll to build the capacity of all the black officers that came out of the ANC and have gone into key government positions on how to manage organized crime investigations it was the challenge at that time honestly I can imagine apartheid I remember you know I was just at a college that's not properly around the same age as you it was a dynamic time to say the least was his issue around lack of training old school techniques because you know that was right down post-cold-war and then did what were the concerns not enough people was it just out of control was it a corrupt I mean just I mean what was the core issue that Nelson wanted to hire Kroll and you could work his core issue was he wanted to ensure the stability of South Africa's democracy that was his core focus and he wanted to make South Africa an attractive place where international companies felt comfortable and confident in investing and that was his focus and he felt that at that time because so many of the key people in the ANC only had training in a cold war context that there wasn't a Nessy skill set to do complex financial or more modern investigations and it was very much focused he was always the innovator he was very much focused on bringing the best practices and the best investigative techniques to the country he was I felt in such a hurry that he doesn't want to do this by going to other governments and asking for the help he wanted to Commission it himself and so he gave he gave a crawl with me as the project leader a contract to do this and my namesake Francois Pienaar has become very well known because of the film Invictus and he's been he had the benefit of Mandela as a mentor and as a supporter and that changed his career the same thing happened to me so what did he actually asked you to do was it to train build a force because there's this talk that and was a despite corruption specifically it was it more both corruption and or stability because they kind of go hand in hand policy and it's a very close link between corruption and instability and and president Ellis instructions were very clear to Crowley said go out and find me the best people in the world the most experienced people in the world who can come to South Africa and train my people how to fight organized crime so I went out and I found some of the best people from the CIA from mi6 the British intelligence service from the Drug Enforcement Agency here in the US form officers from the Federal Bureau of Investigation's detectives from Scotland Yard prosecutors from the US Justice Department and all of them for a number of years traveled to South Africa to train black officers who were newly appointed in key roles in how to combat organized crime and this was you acting as an employee he had crow there's not some operative this is he this was me very much acting as a as an executive and crow I was the project leader Kroll was very well structured and organized and I reported to the chief executive officer in the London office nor Garret who was the former head of the CIA's Near East Division and Nelson Mandela was intimately involved in this with you at Krall President Mandela was the ultimate support of this project and he then designated several ministers to work on it and also senior officials in the stories that had been put out this past week they talked about this to try to make it sound like you're involved on two sides of the equation they bring up scorpions was this the scorpions project that they referred to so it was the scorpions scorpion sounds so dangerous and a movie well there's a movie a movie does feature this so at the end of the training project President Mandela and deputy president Thabo Mbeki who subsequently succeeded him as president put together a ministerial committee to look at what should they do with the capacity that's been built with this investment that they made because for a period of about three years we had all the leading people the most experienced people that have come out of some of the best law enforcement agencies and some of the best intelligence services come and trained in South Africa and this was quite this was quite something John because many of the senior officers in the ANC came from a background where they were trained by the opponents of the people came to treat trained them so so many of them were trained by the Stasi in East Germany some of them were trained by the Russian KGB some of them were trained by the Cubans so we not only had to train them we also had to win their trust and when we started this that's a diverse set of potential dogma and or just habits a theory modernised if you will right is that what the there was there was a question of of learning new skills and there was a question about also about learning management capabilities there was also question of learning the importance of the media for when you do difficult and complex investigations there was a question about using digital resources but there was also fundamentally a question of just building trust and when we started this program none of the black officers wanted to be photographed with all these foreign trainers who were senior foreign intelligence officers when we finished that everyone wanted to be in the photograph and so this was a great South African success story but the President and the deputy president then reflected on what to do with his capacity and they appointed the ministerial task force to do this and we were asked to make recommendations to this Minister ministerial task force and one of the things which we did was we showed them a movie because you referenced the movie and the movie we showed them was the untouchables with Kevin Costner and Sean Connery which is still one of my favorite and and greatest movies and the story The Untouchables is about police corruption in Chicago and how in the Treasury Department a man called Eliot Ness put together a group of officers from which he selected from different places with clean hands to go after corruption during the Probie and this really captured the president's imagination and so he said that's what he want and Ella yeah okay so he said della one of the untouchables he wanted Eliot Ness exactly Al Capone's out there and and how many people were in that goodness so we asked that we we established the government then established decided to establish and this was passed as a law through Parliament the director of special operations the DSO which colloquy became known as the scorpions and it had a scorpion as a symbol for this unit and this became a standalone anti-corruption unit and the brilliant thing about it John was that the first intake of scorpion officers were all young black graduates many of them law graduates and at the time Janet Reno was the US Attorney General played a very crucial role she allowed half of the first intake of young cratchits to go to Quantico and to do the full FBI course in Quantico and this was the first group of foreign students who've ever been admitted to Quantico to do the full Quantico were you involved at what score's at that time yes sir and so you worked with President Mandela yes the set of the scorpions is untouchable skiing for the first time as a new democracy is emerging the landscape is certainly changing there's a transformation happening we all know the history laugh you don't watch Invictus probably great movie to do that you then worked with the Attorney General United States to cross-pollinate the folks in South Africa black officers law degrees Samar's fresh yes this unit with Quantico yes in the United States I had the privilege of attending the the graduation ceremony of the first of South African officers that completed the Quantico course and representing crow they on the day you had us relationships at that time to crawl across pollen I had the privilege of working with some of the best law enforcement officers and best intelligence officers that has come out of the u.s. services and they've been tremendous mentors in my career they've really shaped my thinking they've shaped my values and they've they've shaved my character so you're still under 30 at this time so give us a is that where this where are we in time now just about a 30 so you know around the nine late nineties still 90s yeah so client-server technologies there okay so also the story references Leonard McCarthy and these spy tapes what is this spy tape saga about it says you had a conversation with McCarthy me I'm thinking that a phone tap explain that spy tape saga what does it mean who's Lennon McCarthy explain yourself so so so Leonard McCarthy it's a US citizen today he served two terms as the vice president for institutional integrity at the World Bank which is the world's most important anti-corruption official he started his career as a prosecutor in South Africa many years ago and then became the head of the economic crimes division in the South African Justice Department and eventually became the head of the scorpions and many years after I've left Kroll and were no longer involved in in the work of the scorpions he texted me one evening expressing a concern and an anxiety that I had about the safety of his family and I replied to him with two text messages one was a Bible verse and the other one was a Latin saying and my advice name was follow the rule of law and put the safety of your family first and that was the advice I gave him so this is how I imagined the year I think of it the internet was just there this was him this was roundabout 2000 December 2007 okay so there was I phone just hit so text messaging Nokia phones all those big yeah probably more text message there so you sitting anywhere in London you get a text message from your friend yep later this past late tonight asking for help and advice and I gave him the best advice I can he unfortunately was being wiretapped and those wiretaps were subsequently published and became the subject of much controversy they've now been scrutinized by South Africa's highest court and the court has decided that those wiretaps are of no impact and of importance in the scheme of judicial decision-making and our unknown provenance and on and on unknown reliability they threw it out basically yeah they're basically that's the president he had some scandals priors and corruption but back to the tapes you the only involvement on the spy tapes was friend sending you a text message that says hey I'm running a corruption you know I'm afraid for my life my family what do I do and you give some advice general advice and that's it as there was there any more interactions with us no that's it that's it okay so you weren't like yeah working with it hey here's what we get strategy there was nothing that going on no other interactions just a friendly advice and that's what they put you I gave him my I gave him my best advice when you when you work in when you work as an investigator very much as and it's very similar in venture capital it's all about relationships and you want to preserve relationships for the long term and you develop deep royalties to its people particularly people with whom you've been through difficult situations as I have been with Leonard much earlier on when I was still involved in Kroll and giving advice to South African government on issues related to the scorpius so that that has a lot of holes and I did think that was kind of weird they actually can produce the actual tax I couldn't find that the spy tapes so there's a spy tape scandal out there your name is on out on one little transaction globbed on to you I mean how do you feel about that I mean you must've been pretty pissed when you saw that when you do it when when you do when you do investigative work you see really see everything and all kinds of things and the bigger the issues that you deal with the more frequently you see things that other people might find unusual I are you doing any work right now with c5 at South Africa and none whatsoever so I've I retired from my investigative Korea in 2014 I did terrific 20 years as an investigator during my time as investigator I came to understood the importance of digital and cyber and so at the end of it I saw an opportunity to serve a sector that historically have been underserved with capital which is cyber security and of course there are two areas very closely related to cyber security artificial intelligence and cloud and that's why I created c5 after I sold my investigator firm with five other families who equally believed in the importance of investing private capital to make a difference invest in private capital to help bring about innovation that can bring stability to the digital world and that's the mission of c-5 before I get to the heart news I want to drill in on the BBC stories I think that's really the focal point of you know why we're talking just you know from my standpoint I remember living as a young person in that time breaking into the business you know my 20s and 30s you had Live Aid in 1985 and you had 1995 the internet happened there was so much going on between those that decade 85 to 95 you were there I was an American so I didn't really have a lot exposure I did some work for IBM and Europe in 1980 says it's co-op student but you know I had some peak in the international world it must been pretty dynamic the cross-pollination the melting pot of countries you know the Berlin Wall goes down you had the cold war's ending you had apartheid a lot of things were going on around you yes so in that dynamic because if if the standard is you had links to someone you know talked about why how important it was that this melting pot and how it affected your relationships and how it looks now looking back because now you can almost tie anything to anything yes so I think the 90s was one of the most exciting periods of time because you had the birth of the internet and I started working on Internet related issues yet 20 million users today we have three and a half billion users and ten billion devices unthinkable at the time but in the wake of the internet also came a lot of changes as you say the Berlin Wall came down democracy in South Africa the Oslo peace process in the time that I worked in Kroll some of them made most important and damaging civil wars in Africa came to an end including the great war in the Congo peace came to Sudan and Angola the Ivory Coast so a lot of things happening and if you have a if you had a an international career at that time when globalization was accelerating you got to no a lot of people in different markets and both in crow and in my consulting business a key part of what it but we did was to keep us and Western corporations that were investing in emerging markets safe your credibility has been called in questions with this article and when I get to in a second what I want to ask you straight up is it possible to survive in the international theatre to the level that you're surviving if what they say is true if you if you're out scamming people or you're a bad actor pretty much over the the time as things get more transparent it's hard to survive right I mean talk about that dynamic because I just find it hard to believe that to be successful the way you are it's not a johnny-come-lately firms been multiple years operating vetted by the US government are people getting away in the shadows is it is is it hard because I almost imagine those are a lot of arbitrage I imagine ton of arbitrage that you that are happening there how hard or how easy it is to survive to be that shady and corrupt in this new era because with with with investigated with with intelligence communities with some terrific if you follow the money now Bitcoin that's a whole nother story but that's more today but to survive the eighties and nineties and to be where you are and what they're alleging I just what's your thoughts well to be able to attract capital and investors you have to have very high standards of governance and compliance because ultimately that's what investors are looking for and what investors will diligence when they make an investment with you so to carry the confidence of investors good standards of governance and compliance are of critical importance and raising venture capital and Europe is tough it's not like the US babe there's an abundance of venture capital available it's very hard Europe is under served by capital the venture capital invested in the US market is multiple of what we invest in Europe so you need to be even more focused on governance and compliance in Europe than you would be perhaps on other markets I think the second important point with Gmail John is that technology is brought about a lot of transparency and this is a major area of focus for our piece tech accelerator where we have startups who help to bring transparency to markets which previously did not have transparency for example one of the startups that came through our accelerator has brought complete transparency to the supply chain for subsistence farmers in Africa all the way to to the to the shelf of Walmart or a big grocery retailer in in the US or Europe and so I think technology is bringing a lot more more transparency we also have a global anti-corruption Innovation Challenge called shield in the cloud where we try and find and recognize the most innovative corporations governments and countries in the space so let's talk about the BBC story that hit 12 it says is a US military cloud the DoD Jedi contractor that's coming to award the eleventh hour safe from Russia fears over sensitive data so if this essentially the headline that's bolded says a technology company bidding for a Pentagon contract that's Amazon Web Services to store sensitive data has close partnerships with a firm linked to a sanctioned Russian oligarch the BBC has learned goes on to essentially put fear and tries to hang a story that says the national security of America is at risk because of c5u that's what we're talking about right now so so what's your take on this story I mean did you wake up and get an email said hey check out the BBC you're featured in and they're alleging that you have links to Russia and Amazon what Jon first I have to go I first have to do a disclosure I've worked for the BBC as an investigator when I was in Kroll and in fact I let the litigation support for the BBC in the biggest libel claim in British history which was post 9/11 when the BBC did a broadcast mistakenly accusing a mining company in Africa of laundering money for al-qaeda and so I represented the BBC in this case I was the manager hired you they hired me to delete this case for them and I'm I helped the BBC to reduce a libel claim of 25 million dollars to $750,000 so I'm very familiar with the BBC its integrity its standards and how it does things and I've always held the BBC in the highest regard and believed that the BBC makes a very important contribution to make people better informed about the world so when I heard about the story I was very disappointed because it seemed to me that the BBC have compromised the independence and the independence of the editorial control in broadcasting the story the reason why I say that is because the principal commentator in this story as a gentleman called John Wheeler who's familiar to me as a someone who's been trolling our firm on internet for the last year making all sorts of allegations the BBC did not disclose that mr. Weiler is a former Oracle executive the company that's protesting the Jedi bidding contract and secondly that he runs a lobbying firm with paid clients and that he himself often bid for government contracts in the US government context you're saying that John Wheeler who's sourced in the story has a quote expert and I did check him out I did look at what he was doing I checked out his Twitter he seems to be trying to socialise a story heavily first he needed eyes on LinkedIn he seems to be a consultant firm like a Beltway yes he runs a he runs a phone called in interoperability Clearing House and a related firm called the IT acquisition Advisory Council and these two organizations work very closely together the interoperability Clearing House or IC H is a consulting business where mr. Weiler acts for paying clients including competitors for this bidding contract and none of this was disclosed by the BBC in their program the second part of this program that I found very disappointing was the fact that the BBC in focusing on the Russian technology parks cocuwa did not disclose the list of skok of our partners that are a matter of public record on the Internet if you look at this list very closely you'll see c5 is not on there neither Amazon Web Services but the list of companies that are on there are very familiar names many of them competitors in this bidding process who acted as founding partners of skok about Oracle for example as recently as the 28th of November hosted what was described as the largest cloud computing conference in Russia's history at Skolkovo this is the this is the place which the BBC described as this notorious den of spies and at this event which Oracle hosted they had the Russian presidential administration on a big screen as one of their clients in Russia so some Oracle is doing business in Russia they have like legit real links to Russia well things you're saying if they suddenly have very close links with Skolkovo and so having a great many other Khayyam is there IBM Accenture cisco say Microsoft is saying Oracle is there so Skolkovo has a has a very distinguished roster of partners and if the BBC was fair and even-handed they would have disclosed us and they would have disclosed the fact that neither c5 nor Amazon feature as Corcovado you feel that the BBC has been duped the BBC clearly has been duped the program that they broadcasted is really a parlor game of six degrees of separation which they try to spun into a national security crisis all right so let's tell us John while ago you're saying John Wyler who's quoted in the story as an expert and by the way I read in the story my favorite line that I wanted to ask you on was there seems to be questions being raised but the question is being raised or referring to him so are you saying that he is not an expert but a plant for the story what's what's his role he's saying he works for Oracle or you think do you think he's being paid by Oracle like I can't comment on mr. Wireless motivation what strikes me is the fact that is a former Oracle executive what's striking is that he clearly on his website for the IC H identifies several competitors for the Jedi business clients and that all of this should have been disclosed by the BBC rather than to try and characterize and portray him as an independent expert on this story well AWS put out a press release or a blog post essentially hum this you know you guys had won it we're very clear and this I know it goes to the top because that's how Amazon works nothing goes out until it goes to the top which is Andy chassis and the senior people over there it says here's the relationship with c5 and ATS what school you use are the same page there but also they hinted the old guard manipulation distant I don't think they use the word disinformation campaign they kind of insinuate it and that's what I'm looking into I want to ask you are you part are you a victim of a disinformation campaign do you believe that you're not a victim being targeted with c5 as part of a disinformation campaign put on by a competitor to AWS I think what we've seen over the course of this last here is an enormous amount of disinformation around this contract and around this bidding process and they've a lot of the information that has been disseminated has not only not been factual but in some cases have been patently malicious well I have been covering Amazon for many many years this guy Tom Wyler is in seems to be circulating multiple reports invested in preparing for this interview I checked Vanity Fair he's quoted in Vanity Fair he's quoted in the BBC story and there's no real or original reporting other than those two there's some business side our article which is just regurgitating the Business Insider I mean the BBC story and a few other kind of blog stories but no real original yes no content don't so in every story that that's been written on this subject and as you say most serious publication have thrown this thrown these allegations out but in the in those few instances where they've managed to to publish these allegations and to leverage other people's credibility to their advantage and leverage other people's credibility for their competitive advantage John Wheeler has been the most important and prominent source of the allegations someone who clearly has vested commercial interests someone who clearly works for competitors as disclosed on his own website and none of this has ever been surfaced or addressed I have multiple sources have confirmed to me that there's a dossier that has been created and paid for by a firm or collection of firms to discredit AWS I've seen some of the summary documents of that and that is being peddled around to journalists we have not been approached yet I'm not sure they will because we actually know the cloud what cloud computing is so I'm sure we could debunk it by just looking at it and what they were putting fors was interesting is this an eleventh-hour a desperation attempt because I have the Geo a report here that was issued under Oracle's change it says there are six conditions why we're looking at one sole cloud although it's not a it's a multiple bid it's not an exclusive to amazon but so there's reasons why and they list six service levels highly specialized check more favorable terms and conditions with a single award expected cause of administration of multiple contracts outweighs the benefits of multiple awards the projected orders are so intricately related that only a single contractor can reasonably be perform the work meaning that Amazon has the only cloud that can do that work now I've reported on the cube and it's looking angle that it's true there's things that other clouds just don't have anyone has private they have the secret the secret clouds the total estimated value of the contract is less than the simplified acquisition threshold or multiple awards would not be in the best interest this is from them this is a government report so it seems like there's a conspiracy against Amazon where you are upon and in in this game collect you feel that collateral damage song do you do you believe that to be true collateral damage okay well okay so now the the John Wheeler guys so investigate you've been an investigator so you mean you're not you know you're not a retired into this a retired investigator you're retired investigated worked on things with Nelson Mandela Kroll Janet Reno Attorney General you've vetted by the United States government you have credibility you have relationships with people who have have top-secret clearance all kinds of stuff but I mean do you have where people have top-secret clearance or or former people who had done well we have we have the privilege of of working with a very distinguished group of senior national security leaders as operating partisan c5 and many of them have retained their clearances and have been only been able to do so because c5 had to pass through a very deep vetting process so for you to be smeared like this you've been in an investigative has you work at a lot of people this is pretty obvious to you this is like a oh is it like a deep state conspiracy you feel it's one vendor - what is your take and what does collateral damage mean to you well I recently spoke at the mahkum conference on a session on digital warfare and one of the key points I made there was that there are two things that are absolutely critical for business leaders and technology leaders at this point in time one we have to clearly say that our countries are worth defending we can't walk away from our countries because the innovation that we are able to build and scale we're only able to do because we live in democracies and then free societies that are governed by the rule of law the second thing that I think is absolutely crucial for business leaders in the technology community is to accept that there must be a point where national interest overrides competition it must be a point where we say the benefit and the growth and the success of our country is more important to us than making commercial profits and therefore there's a reason for us either to cooperate or to cease competition or to compete in a different way what might takes a little bit more simple than that's a good explanation is I find these smear campaigns and fake news and I was just talking with Kara Swisher on Twitter just pinging back and forth you know either journalists are chasing Twitter and not really doing the original courting or they're being fed stories if this is truly a smear campaign as being fed by a paid dossier then that hurts people when families and that puts corporate interests over the right thing so I think I a personal issue with that that's fake news that's just disinformation but it's also putting corporate inches over over families and people so I just find that to be kind of really weird when you say collateral damage earlier what did you mean by that just part of the campaign you personally what's what's your view okay I think competition which is not focused on on performance and on innovation and on price points that's competition that's hugely destructive its destructive to the fabric of innovation its destructive of course to the reputation of the people who fall in the line of sight of this kind of competition but it's also hugely destructive to national interest Andrae one of the key stories here with the BBC which has holes in it is that the Amazon link which we just talked about but there's one that they bring up that seems to be core in all this and just the connections to Russia can you talk about your career over the career from whether you when you were younger to now your relationship with Russia why is this Russian angle seems to be why they bring into the Russia angle into it they seem to say that c-5 Cable has connections they call deep links personal links into Russia so to see what that so c5 is a venture capital firm have no links to Russia c5 has had one individual who is originally of Russian origin but it's been a longtime Swiss resident and you national as a co investor into a enterprise software company we invested in in 2015 in Europe we've since sold that company but this individual Vladimir Kuznetsov who's became the focus of the BBC's story was a co investor with us and the way in which we structure our investment structures is that everything is transparent so the investment vehicle for this investment was a London registered company which was on the records of Companies House not an offshore entity and when Vladimir came into this company as a co investor for compliance and regulatory purposes we asked him to make his investment through this vehicle which we controlled and which was subject to our compliance standards and completely transparent and in this way he made this investment now when we take on both investors and Co investors we do that subject to very extensive due diligence and we have a very robust and rigorous due diligence regime which in which our operating partners who are leaders of great experience play an important role in which we use outside due diligence firms to augment our own judgment and to make sure we have all the facts and finally we also compare notes with other financial institutions and peers and having done that with Vladimir Kuznetsov when he made this one investment with us we reached the conclusion that he was acting in his own right as an independent angel investor that his left renova many years ago as a career executive and that he was completely acceptable as an investor so that you think that the BBC is making an inaccurate Association the way they describe your relationship with Russia absolutely the the whole this whole issue of the provenance of capital has become of growing importance to the venture capital industry as you and I discussed earlier with many more different sources of capital coming out of places like China like Russia Saudi Arabia other parts of the world and therefore going back again to you the earlier point we discussed compliance and due diligence our critical success factors and we have every confidence in due diligence conclusions that we reached about vladimir quits net source co-investment with us in 2015 so I did some digging on c5 razor bidco this was the the portion of the company in reference to the article I need to get your your take on this and they want to get you on the record on this because it's you mentioned I've been a law above board with all the compliance no offshore entities this is a personal investment that he made Co investment into an entity you guys set up for the transparency and compliance is that true that's correct no side didn't see didn't discover this would my my children could have found this this this company was in a transparent way on the records in Companies House and and Vladimir's role and investment in it was completely on the on the public record all of this was subject to financial conduct authority regulation and anti money laundering and no your client standards and compliance so there was no great big discovery this was all transparent all out in the open and we felt very confident in our due diligence findings and so you feel very confident Oh issue there at all special purpose none whatsoever is it this is classic this is international finance yes sir so in the venture capital industry creating a special purpose vehicle for a particular investment is a standard practice in c-five we focus on structuring those special-purpose vehicles in the most transparent way possible and that was his money from probably from Russia and you co invested into this for this purpose of doing these kinds of deals with Russia well we just right this is kind of the purpose of that no no no this so in 2015 we invested into a European enterprise software company that's a strategic partner of Microsoft in Scandinavian country and we invested in amount of 16 million pounds about at the time just more than 20 million dollars and subsequent in August of that year that Amir Kuznetsov having retired for nova and some time ago in his own right as an angel investor came in as a minority invest alongside us into this investment but we wanted to be sure that his investment was on our control and subject to our compliance standards so we requested him to make his investment through our special purpose vehicle c5 raised a bit co this investment has since been realized it's been a great success and this business is going on to do great things and serve great clients it c5 taking russian money no see if I was not taking Russian money since since the onset of sanctions onboarding Russian money is just impossible sanctions have introduced complexity and have introduced regulatory risk related to Russian capital and so we've taken a decision that we will not and we can't onboard Russian capital and sanctions have also impacted my investigative career sanctions have also completely changed because what the US have done very effectively is to make sanctions a truly global regime and in which ever country are based it doesn't really matter you have to comply with US sanctions this is not optional for anybody on any sanctions regime including the most recent sanctions on Iran so if there are sanctions in place you can't touch it have you ever managed Russian oligarchs money or interests at any time I've never managed a Russian oligarchs money at any point in time I served for a period of a year honest on the board of a South African mining company in which Renova is a minority invest alongside an Australian company called South 32 and the reason why I did this was because of my support for African entrepreneurship this was one of the first black owned mining companies in South Africa that was established with a British investment in 2004 this business have just grown to be a tremendous success and so for a period of a year I offered to help them on the board and to support them as they as they looked at how they can grow and scale the business I have a couple more questions Gabe so I don't know if you wanna take a break you want to keep let's take a break okay let's take a quick break do a quick break I think that's great that's the meat of it great job by the way fantastic lady here thanks for answering those questions the next section I want to do is compliment

(digital music) >> From Yorktown Heights, New York It's theCUBE! Covering IBM Cloud Innovation Day. Brought to you by IBM. >> Hi, it's Wikibon's Peter Burris again. We're broadcasting on The Cube from IBM Innovation Day at the Thomas J Watson Research Laboratory in Yorktown Heights, New York. Have a number of great conversations, and we got a great one right now. Rob Thomas, who's the General Manager of IBM Analytics, welcome back to theCUBE. >> Thanks Peter, great to see you. Thanks for coming out here to the woods. >> Oh, well it's not that bad. I actually live not to far from here. Interesting Rob, I was driving up the Taconic Parkway and I realized I hadn't been on it in 40 years, so. >> Is that right? (laugh) >> Very exciting. So Rob let's talk IBM analytics and some of the changes that are taking place. Specifically, how are customers thinking about achieving their AI outcomes. What's that ladder look like? >> Yeah. We call it the AI ladder. Which is basically all the steps that a client has to take to get to get to an AI future, is the best way I would describe it. From how you collect data, to how you organize your data. How you analyze your data, start to put machine learning into motion. How you infuse your data, meaning you can take any insights, infuse it into other applications. Those are the basic building blocks of this laddered AI. 81 percent of clients that start to do something with AI, they realize their first issue is a data issue. They can't find the data, they don't have the data. The AI ladder's about taking care of the data problem so you can focus on where the value is, the AI pieces. >> So, AI is a pretty broad, hairy topic today. What are customers learning about AI? What kind of experience are they gaining? How is it sharpening their thoughts and their pencils, as they think about what kind of outcomes they want to achieve? >> You know, its... For some reason, it's a bit of a mystical topic, but to me AI is actually quite simple. I'd like to say AI is not magic. Some people think it's a magical black box. You just, you know, put a few inputs in, you sit around and magic happens. It's not that, it's real work, it's real computer science. It's about how do I put, you know, how do I build models? Put models into production? Most models, when they go into production, are not that good, so how do I continually train and retrain those models? Then the AI aspect is about how do I bring human features to that? How do I integrate that with natural language, or with speech recognition, or with image recognition. So, when you get under the covers, it's actually not that mystical. It's about basic building blocks that help you start to achieve business outcomes. >> It's got to be very practical, otherwise the business has a hard time ultimately adopting it, but you mentioned a number of different... I especially like the 'add the human features' to it of the natural language. It also suggests that the skill set of AI starts to evolve as companies mature up this ladder. How is that starting to change? >> That's still one of the biggest gaps, I would say. Skill sets around the modern languages of data science that lead to AI: Python, AR, Scala, as an example of a few. That's still a bit of a gap. Our focus has been how do we make tools that anybody can use. So if you've grown up doing SPSS or SaaS, something like that, how do you adopt those skills for the open world of data science? That can make a big difference. On the human features point, we've actually built applications to try to make that piece easy. Great example is with Royal Bank of Scotland where we've created a solution called Watson Assistant which is basically how do we arm their call center representatives to be much more intelligent and engaging with clients, predicting what clients may do. Those types of applications package up the human features and the components I talked about, makes it really easy to get AI into production. >> Now many years ago, the genius Turing, noted the notion of the Turing machine where you couldn't tell the difference between the human and a machine from an engagement standpoint. We're actually starting to see that happen in some important ways. You mentioned the call center. >> Yep. >> How are technologies and agency coming together? By that I mean, the rate at which businesses are actually applying AI to act as an agent for them in front of customers? >> I think it's slow. What I encourage clients to do is, you have to do a massive number of experiments. So don't talk to me about the one or two AI projects you're doing, I'm thinking like hundreds. I was with a bank last week in Japan, and they're comment was in the last year they've done a hundred different AI projects. These are not one year long projects with hundreds of people. It's like, let's do a bunch of small experiments. You have to be comfortable that probably half of your experiments are going to fail, that's okay. The goal is how do you increase your win rate. Do you learn from the ones that work, and from the ones that don't work, so that you can apply those. This is all, to me at this stage, is about experimentation. Any enterprise right now, has to be thinking in terms of hundreds of experiments, not one, not two or 'Hey, should we do that project?' Think in terms of hundreds of experiments. You're going to learn a lot when you do that. >> But as you said earlier, AI is not magic and it's grounded in something, and it's increasingly obvious that it's grounded in analytics. So what is the relationship between AI analytics, and what types of analytics are capable of creating value independent of AI? >> So if you think about how I kind of decomposed AI, talked about human features, I talked about, it kind of starts with a model, you train the model. The model is only as good as the data that you feed it. So, that assumes that one, that your data's not locked into a bunch of different silos. It assumes that your data is actually governed. You have a data catalog or that type of capability. If you have those basics in place, once you have a single instantiation of your data, it becomes very easy to train models, and you can find that the more that you feed it, the better the model's going to get, the better your business outcomes are going to get. That's our whole strategy around IBM Cloud Private for Data. Basically, one environment, a console for all your data, build a model here, train it in all your data, no matter where it is, it's pretty powerful. >> Let me pick up on that where it is, 'cause it's becoming increasingly obvious, at least to us and our clients, that the world is not going to move all the data over to a central location. The data is going to be increasingly distributed closer to the sources, closer to where the action is. How does AI and that notion of increasing distributed data going to work together for clients. >> So we've just released what's called IBM Data Virtualization this month, and it is a leapfrog in terms of data virtualization technology. So the idea is leave your data where ever it is, it could be in a data center, it could be on a different data center, it could be on an automobile if you're an automobile manufacturer. We can federate data from anywhere, take advantage of processing power on the edge. So we're breaking down that problem. Which is, the initial analytics problem was before I do this I've got to bring all my data to one place. It's not a good use of money. It's a lot of time and it's a lot of money. So we're saying leave your data where it is, we will virtualize your data from wherever it may be. >> That's really cool. What was it called again? >> IBM Data Virtualization and it's part of IBM Cloud Private for Data. It's a feature in that. >> Excellent, so one last question Rob. February's coming up, IBM Think San Francisco thirty plus thousand people, what kind of conversations do you anticipate having with you customers, your partners, as they try to learn, experiment, take away actions that they can take to achieve their outcomes? >> I want to have this AI experimentation discussion. I will be encouraging every client, let's talk about hundreds of experiments not 5. Let's talk about what we can get started on now. Technology's incredibly cheap to get started and do something, and it's all about rate and pace, and trying a bunch of things. That's what I'm going to be encouraging. The clients that you're going to see on stage there are the ones that have adopted this mentality in the last year and they've got some great successes to show. >> Rob Thomas, general manager IBM Analytics, thanks again for being on theCUBE. >> Thanks Peter. >> Once again this is Peter Buriss of Wikibon, from IBM Innovation Day, Thomas J Watson Research Center. We'll be back in a moment. (techno beat)

(upbeat music) >> Announcer: Live from San Francisco, it's theCUBE, covering DevNet Create 2017. Brought to you by Cisco. >> Hello, everyone, and welcome back to our live coverage from theCUBE exclusive, two days with Cisco's inaugural DevNet Create event. I'm John Furrier, with my co-host, Peter Burris, who's the general manager of Wikibon.com, and head of research for SiliconANGLE Media. We're talking with Susie Wee, who is the vice president and CTO of Cisco's DevNet, the creator of DevNet, the developer program that was started as grassroots, now a full-blown Cisco developer program. Now starting another foray into the cloud-native open-source community with this new event, DevNet Create. Welcome to theCUBE, thanks for joining us. >> Thank you, John. >> Thanks for having us. We love going to the inaugural events because they're always the first, and you know, being bloggers, and media, you got to be first. First news, first comments. >> Susie: Always first. >> Always first, and we're the only media here, so thank you. >> Susie: Thank you. >> So tell us about the event (Susie chuckles). You're the host and the creator, with your team. >> Susie: Yes. >> How did this come together, why DevNet Create? You have DevNet, this event is going extremely well, tell us. >> Awesome, so, yeah, so we have DevNet, we've had DevNet for about three years. It was actually exactly three years ago that we had our first DevNet Zone, a developer conference at Cisco Live, three years ago. And there, we felt like we pretty squarely hit... We've had successes there, we've had a pretty strong handle on our infrastructure audience, but what we see is that there's this huge transition, transformation going on in the industry, with IoT and cloud, that changes the definition of how applications meet infrastructure. And so this whole thing with, you know, applications, what is an application? What is the infrastructure? The infrastructure is now programmable, how can apps interact? It opens up a whole new world, and so what we did was we created DevNet Create as a standalone developer conference focused on IoT and cloud to focus on that transformation. >> And a lot of industry trends kind of going on, and moves you're making, it's the company, or you, Cisco is making, AppDynamics, big acquisition, kind of speaks to that, but also, there's always a natural progression for Cisco to have moving up the stack with software, but IoT gives you guys a unique opportunity with the network concept. So, making it network programmable, infrastructure as code, as some say in the DevOps world, is the ethos. >> Absolutely. >> How do you guys see yourselves engaging with the community, and what are some of the plans, and what's some of the feedback you're getting here at the event? >> So what we've done here at the event is that, you know, as you've seen from the channel is that, our content is 90% from the community, maybe 10% from Cisco, 90% from the community, because we believe it is all about the ecosystem. It's about how applications meet the infrastructure, it's the systems people are building together. And there's a lot of movement in developing these technologies. We don't know the final form of how an IoT app... Like, who's going to build the app, who's going to build the users, who's going to run the service, who's going to run the infrastructure? It's all still evolving, and we think that the community needs to come together to solve this to make the most of the opportunity. And so that's what, really, this is all about. And then, we think it actually involves learning the languages, making sure that the app folks know the language of the infrastructure folks. They don't have to become experts in it, but just knowing the language. Understand what part's programmable, what part's not, what benefit can you derive from the infrastructure. And then, by really having knowledge of what you can get across, and creating a forum for people to get together to have this conversation, we can make those breakthroughs. >> So just a clarification, you said that 90% of the sessions are non-Cisco, or from the community, and only 10% from Cisco? >> Susie: That's right. >> Is that by design? >> That is absolutely by design. So, when we have the DevNet Zone at Cisco Live, that's all about all of Cisco's products, platforms, APIs, bringing in the community to come and learn about those, but DevNet Create was really, squarely for IoT and app developers, IoT app developers, cloud developers, people working on DevOps, to look at that intersection. So we didn't go into all the gory details of networking, like we very much like to do, but we were really trying to focus on, "What's the value to application developers, "and what are the opportunities?" >> Well, it's interesting because, Susie, we're in the midst, as you said, of a pretty significant transformation, and there's a lot of turbulence, not only in business and how business conceives of digital technology, and the role it's going to play, the developer world, cloud-this, cloud-that, different suppliers, but one of the anchor points is the network, even though the network itself is changing, >> It is. >> in the midst of a transformation, but it's a step function. So, you go from, on the wireless, go outside, 1G to 3G, to 5G, et cetera, that kind of thing, but how is the developer going to inform that next step function in the network, the next big transformation in the network, and to what degree is this kind of a session going to really catalyze that kind of a change? >> Absolutely. So, what happens is, you're right, it's something that we all know, all app developers know, and actually, every person in the world knows, the network is important. The network provides connectivity, the network is what provides Internet, data, and everything there. That's critical to apps, but the thing that's been heard about it is it's not programmable. Like, you kind of get that thing configured, it's working now, you leave it. Don't touch it. >> It's still wires. In the minds of a lot of people, (Susie laughs) it's still wires, right? >> It is, it's wires, or even if it's wireless, once you can get it configured, you leave it. You're not playing with it again, it's too, kind of, dangerous or fragile to change it. >> Because of the sensitivity to operational... >> Because of the sensitivity to operations. The big change that's happening is the network is becoming programmable. The network has APIs, and then, we have things like automation and controller-based networking coming into play, so you don't actually configure it by going one network device at a time, you feed these into a controller, and then, now you're actually doing network-wide commands. That takes out the human error, it actually makes it easy to configure and reconfigure. And when you have that ability to provision resources, to kind of reset configurations, when you can do that quickly through APIs, you suddenly have a tool that you never had before. So let me give you an example. So let's say that you're in a building, you have your badging systems, your automated elevators, you have your surveillance cameras, you want to put out a new security system with surveillance cameras. You don't want to put that on the same network segment as your vending machines. You have a different level of security required. Could put in a work order to say... >> Unless you're really worried about who's stealing from the vending machines. (all laugh) >> So what you can do, now that it's programmable, is use infrastructure as code, is basically say, "Boom, give me a new network segment, "let me drop these new devices onto it, "let the programmable network automatically create "a separate network segment that has "all of these devices together." Then you can start to use group-based policy to now set, you know, the rules that you want, for how those cameras are accessed, who they're accessible by, what kind of data can come in and out of it. You can actually do that with infrastructure as code. That was not a knob that app developers had before. So they don't need to become networking experts, but now they have these knobs that they can use to give you that next level of security, to give you that next level of programmability, and to do it at the speed that an app developer needs. >> So I was talking to Steve Post-y earlier this morning, and he's from Redhead, he's a lead developer, he's not a network guy, he's self-proclaimed, "Hey, I'm not a networking person, I care about apps," and he's a developer, and he brought up something interesting I want to get your thoughts on. I think you're onto something really big with your vision, which is why we're so pumped about it, and he brought up an example of ecosystem's edges, and margins of the edge of these, that when they come together, creates innovation opportunities. And he used the example of data science meets cloud. And what he was using in particular was the example of most data people in the old days were data jocks, they did data, they did things, and they weren't really computer scientists, but as those two communities came together, the computer scientist saying, "Hey, I don't know about data," and the data guy's like, "Hey, you know about algorithms," "I know about algorithms," so innovation happened when that came together. What you're doing here, if I got this right, is you're saying, "Hey, DevNet's doing great," from a Cisco perspective, "but now this whole new creative innovation world "in the cloud is happening in real time. "Bring 'em together, "so best of Cisco knowledge to the guys who don't want to be (chuckles) "experts in that can share information." Is that kind of where this is going? >> Yeah, that's exactly where it's going, and same example, earlier in my career, I was working on sending video over networks, and then you had the networking people doing networking, you had the video people doing video compression, but then video networking, or streaming media, kind of, oh, you can put, you know, your knowledge of the compression and the network all together, so that kind of emerged as a field. The same thing, so, so far, the applications, and the infrastructure, and IT departments have been completely separate. You would just do the best you can, it was the job of IT to provide it, but now, suddenly there's an opportunity to bring these together. And it's, again, it's because the infrastructure's becoming programmable, and now it has knobs and can work quickly. So, yes, this is kind of new ground. And things could continue the way they are, right? And it's okay, we're getting by, but you just won't be realizing the potential of the real kind of... >> Well, open-source has clearly demonstrated that the collective intelligence of communities can really move fast, and share, and it's now tier one, so you're seeing companies go public, MuleSoft, Cloudera, and the list goes on and on. So now you have the dynamic of open-source, so I got to ask you the question, as you go out with DevNet Create, as this creation, the builders that are out there building apps are going to have programmable networks, how do you see this next leg of the journey? Because you have the foray now with DevNet Create, looks good, really well done, what's next? >> What's next is going on and making the real instances that show the application and infrastructure synergy. So let me just give you a really simple example of something that we're doing, which is that Apple and Cisco have had a partnership, and this partnership is coming together in that we have iOS developers who are writing mobile apps. So you have your mobile apps people are writing, we have iOS 10, your app developers are writing these apps. But everybody knows you run into a situation where your app gets congested on the network. Let's say that we're here in Westfield Mall, and they want to put out an AR/VR app, and you want that traffic to work, right? 'Cause if the mall wants to offer an AR/VR service, it takes a lot of bandwidth to get that data through, but through this partnership, what we have is an ability we have to use an iOS 10 SDK to, basically, business optimize your app so that it can run well on a Cisco infrastructure. So basically, it's just saying, "Hey, this is important, "put it in the highest QoS (John laughs) level setting, "and make your AR/VR work." So it's just having these real instances where these work together. >> I mean, I used to be a plumber back in my day when I used to work at HP, and I know how hard it is, and so I'm going to bring this up, because networks used to be stable and fragile/brittle, and then that would determine what you could do on top of it. But there are things like DNS, we hear about DNS, we hear about configuration management, setting ports, and doing this, to your point, I want dynamic provisioning or policy at any given moment, yet the network's got to be ready to do that. >> You don't want to submit a work order for that. (laughs) >> You don't want to have to say, "Hey, can you provision port, whatever, "I need to send a bunch of bandwidth." This is what we're talking about when we say programmable infrastructure, just letting the apps interface with network APIs, right? >> Absolutely, and I think that, you heard earlier, that with CNCF, the Cloud Native Computing Foundation, just announced CNI, so that what they're doing is now offering an ability to take your kind of container orchestration and take into consideration what's going on in the network, right? So if this link is more congested than that, then make sure that you're doing your orchestration in the right ways, that the network is informing the cloud layer, that the cloud platform's informing the network, so that's going to be huge. >> But do you think, I'm curious, Susie, do you think that we're going to see a time when we start bringing conventions at layer 7 in the network, so we start to parse layer 7 down a little bit, so developers can think in terms of some of those higher-level services that previously have been presentation? Are we likely to see that kind of a thing? As the pain of the network starts to go away, and an explicit knowledge of layer 1-6 become a lot less important, are we going to see a natural expansion at layer 7, and think about distributed data, distributed applications, distributed services, more coherence to how that happens on an industry-wide basis? What do you think? >> Yeah, so let's see, I don't know if I have a view on which layers go away, or which layers compress... >> But the knowledge, the focal point of those? >> But the knowledge, absolutely. So it comes into play, and what happens is, like, what is the infrastructure? In the Internet of things, things are a part of your infrastructure. That's just different. As you're going to microservices, applications aren't applications, they're being written as microservices, and then once you put those microservices in containers, they can move around. So you actually have a pretty different paradigm for thinking about the architecture of applications, of how they're orchestrated, what resources they sit on, and how you provision, so you get a very new paradigm for that. And then the key is... >> But they're inherently networked? >> That's right, that's right. It's all about connectivity, it's all about, you know, they don't do anything without the network. And we're pushing the boundaries of the network. >> These aren't function calls over memory like we used to think about things, these things are inherently networked. We know we have network SOAs, and service levels, and whatnot... >> Susie: There is. >> It sounds like we have... I was wondering, here, at this conference, are developers starting to talk about, "Geez, I would like to look at Kubernetes "as a lower-level feature in layer 7," >> Susie: They are. (laughs) >> "where there's a consistent approach to thinking about "how that orchestration layer is going to work, "and how containers work above that, "because I don't have to worry about session anymore, I don't have to worry about transmission." >> Susie: Absolutely. >> That goes away, so give me a little bit more visibility into some of that higher-level stuff, where, really, the connectivity issues are becoming more obvious. >> Absolutely, and an interesting example is that, you know, we actually talked about AppDynamics in the keynote, and so, with AppDynamics, what kind of information can you get from these bits of code that are running in different places? And it comes into where we have the Royal Bank of Scotland, who's saying, "What's my busiest bank branch "where people are doing mobile banking in the country?" And they're like, "Well, how do I answer that question?" And then you see that, oh, someone has their mobile phone, they take an app, then you actually break it down to how is that request, that API, how is that being, kind of, operated throughout your network. And when you take a look, you say, "Okay, well, this called this "piece of code that's running here. "This piece of code used this API to talk to this other service, to talk to this other," you can map that out, get back the calls of, "Hey, this is how many times this API has been called, "this is how many times this service has been called, "this is the ones that are talking to who," then they came up with the answer, saying that our busiest bank branch is the 9 a.m. Paddington Train Station. >> And that's a great example, because now you gain visibility >> Exactly >> into where the dependencies are, which even if you don't explicitly render it that way, starts to build a picture of what the layers of function might look like based on the dependencies and the sharing of the underlying services. >> That's right, and that's where you're saying, like, "What? The infrastructure just gave me business value (John laughs) "in a very direct way. "How did that happen?" >> John: That's a huge opportunity for Cisco. >> So it's a big... >> Well, let's get in the studio and let's break down the Kubernetes and the containers, 'cause Docker's here, a lot of other folks are here. We've had, also, Abby Kearns, the executive director of Cloud Foundry. We've had the executive director from the Cloud Native Compute Foundation, Dan was here, a lot of folks here in the industry kind of validating >> Yeah, Craig was here. >> your support. Sun used to have an expression, the network is the computer, but now, maybe Chuck Robbins should go for network is the app, or the app is the network, (Susie laughs) I mean, that's what's happening here. The interplay between the two is happening big time. >> It is happening here, yeah. Just every element, every piece of code, what we saw is that this year, developers will write 111 billion lines of code. You think about that, every piece of... >> Peter: That we know about. (chuckles) >> That we know about, there's probably more. (chuckles) and all of that, you're right, these are broken up into pieces that are inherently networked, right? They have data, it's all about data and information that they're sharing to give interesting experiences. So this is absolutely a new paradigm. >> Well, congratulations on your success. What a great journey, I know it's been a short time, but I noticed after our in-studio interview, when you came in to share with us, the show, as a preview, Chuck Robbins retweeted one of the tweets. >> Susie: He did. >> And so I got to ask you, internally at Cisco, I know you put this together kind of as a entrepreneurial inside the company, and had support for that, what is the conversation you have with Chuck and the executive team about this effort? Because they got to see a clear line of sight that the value of the network is creating business value. What are some of the internal conversations, can you give us a little bit of color without giving away all the trade secrets? >> Yeah, well, internally, we're getting huge support. Chuck Robbins checks in on this, he actually has been checking in saying, "How's it going?" Rowan Trollope sending, "Hey, how's it going? "I heard it's going great." >> Did he text you today? >> Chuck did a couple days ago. >> John: Okay. (chuckles) >> And then Rowan, today, so, yeah, so we have a lot of conversation. >> Rowan's a CUBE alumni, Chuck's got to get on theCUBE, (Susie laughs) Rowan's been on before. >> Yeah, so they're all kind of checking in on it. We have the IoT World Forum going on in parallel, in London, so, otherwise, they would be here as well. But they understand... >> John: There's a general excitement? This is not a rogue event? >> There's huge excitement. >> This is not, like, a rogue event? >> It's not, it's not, and what happens is... They also understand that we're talking about bringing in the ecosystem. It's not just a Cisco conversation, it is a community... >> Yeah, you're doing it right, you're not trying to take over the sandbox. You're coming in with respect and actually putting out content, and learning. >> Putting out content, and really, it's all about letting people interact and create this new area. It's breaking new ground, it's facilitating a conversation. I mean, where apps meet infrastructure, it's controversial as well. Some people should say, "They should never meet. "Why would they ever meet?" (Susie and John laugh) >> So, we do a lot of shows, I was telling Peter that, you know, we were at the first Hadoop Summit, second Hadoop World, with Cloudera, when they were a small startup, Docker's first event, CubeCon's first event, we do a lot of firsts, and I got to tell you, the energy here feels a lot like those events, where it's just so obvious that (chuckles) "Okay, finally, programmable infrastructure." >> Well, I'll be honest, I'm relieved, because, you know, we were taking a bet. So, you know, when I was bouncing this idea off of you, we were talking about it, it was a risk. So the question is, will it appeal to the app developers, will it appeal to the cloud developers, will it appeal overall? And I'm very relieved and happy to see that the vibe is very positive. >> Very positive. >> So people are very receptive to these ideas. >> Well, you know community, give more than you take has always been a great philosophy. >> I'm always a little paranoid and (John laughs) nervous but I'm very pleased, 'cause people seem to be really happy. There's a lot of action. >> There are a lot of PCs with Docker stickers on them here. (John laughs) >> There are. (laughs) There are, yes, yes. We have the true cloud, IoT, we have the hardcore developers here, and they seem to be very engaged and really embracing... >> Well, we've always been covering DevOps, again, from the beginning, and cloud-native is, to me, it's just a semantic word for DevOps. It's happening, it's going mainstream, and great to see Cisco, and congratulations on all your work, and thanks for including theCUBE in your inaugural event. >> Susie: Thank you. >> Susie Wee, Vice President and CTO at Cisco's DevNet. We're here for the inaugural event, DevNet Create, with the community, two great communities coming together. I'm John Furrier with Peter Burris, stay tuned for more coverage from our exclusive DevNet Create coverage, stay with us. (upbeat music) >> Hi, I'm April Mitchell, and I'm the senior director of strategy.