>> Narrator: From around the globe, it's theCUBE with digital coverage of Exascale day, made possible by Hewlett Packard Enterprise. >> Hey, welcome back Jeff Frick here with the cube come due from our Palo Alto studios, for their ongoing coverage in the celebration of Exascale day 10 to the 18th on October 18th, 10 with 18 zeros, it's all about big powerful giant computing and computing resources and computing power. And we're excited to invite back our next guest she's been on before. She's Dr. Arti Garg, head of advanced AI solutions and technologies for HPE. Arti great to see you again. >> Great to see you. >> Absolutely. So let's jump into before we get into Exascale day I was just looking at your LinkedIn profile. It's such a very interesting career. You've done time at Lawrence Livermore, You've done time in the federal government, You've done time at GE and industry, I just love if you can share a little bit of your perspective going from hardcore academia to, kind of some government positions, then into industry as a data scientist, and now with originally Cray and now HPE looking at it really from more of a vendor side. >> Yeah. So I think in some ways, I think I'm like a lot of people who've had the title of data scientists somewhere in their history where there's no single path, to really working in this industry. I come from a scientific background. I have a PhD in physics, So that's where I started working with large data sets. I think of myself as a data scientist before the term data scientist was a term. And I think it's an advantage, to be able to have seen this explosion of interest in leveraging data to gain insights, whether that be into the structure of the galaxy, which is what I used to look at, or whether that be into maybe new types of materials that could advance our ability to build lightweight cars or safety gear. It's allows you to take a perspective to not only understand what the technical challenges are, but what also the implementation challenges are, and why it can be hard to use data to solve problems. >> Well, I'd just love to get your, again your perspective cause you are into data, you chose that as your profession, and you probably run with a whole lot of people, that are also like-minded in terms of data. As an industry and as a society, we're trying to get people to do a better job of making database decisions and getting away from their gut and actually using data. I wonder if you can talk about the challenges of working with people who don't come from such an intense data background to get them to basically, I don't know if it's understand the value of more of a data kind decision making process or board just it's worth the effort, cause it's not easy to get the data and cleanse the data, and trust the data and get the right context, working with people that don't come from that background. And aren't so entrenched in that point of view, what surprises you? How do you help them? What can you share in terms of helping everybody get to be a more data centric decision maker? >> So I would actually rephrase the question a little bit Jeff, and say that actually I think people have always made data driven decisions. It's just that in the past we maybe had less data available to us or the quality of it was not as good. And so as a result most organizations have developed organize themselves to make decisions, to run their processes based on a much smaller and more refined set of information, than is currently available both given our ability to generate lots of data, through software and sensors, our ability to store that data. And then our ability to run a lot of computing cycles and a lot of advanced math against that data, to learn things that maybe in the past took, hundreds of years of experiments in scientists to understand. And so before I jumped into, how do you overcome that barrier? Just I'll use an example because you mentioned, I used to work in industry I used to work at GE. And one of the things that I often joked about, is the number of times I discovered Bernoulli's principle, in data coming off a GE jet engines you could do that overnight processing these large data but of course historically that took hundreds of years, to really understand these physical principles. And so I think when it comes to how do we bridge the gap between people who are adapt at processing large amounts of data, and running algorithms to pull insights out? I think it's both sides. I think it's those of us who are coming from the technical background, really understanding the way decisions are currently made, the way process and operations currently work at an organization. And understanding why those things are the way they are maybe their security or compliance or accountability concerns, that a new algorithm can't just replace those. And so I think it's on our end, really trying to understand, and make sure that whatever new approaches we're bringing address those concerns. And I think for folks who aren't necessarily coming from a large data set, and analytical background and when I say analytical, I mean in the data science sense, not in the sense of thinking about things in an abstract way to really recognize that these are just tools, that can enhance what they're doing, and they don't necessarily need to be frightening because I think that people who have been say operating electric grids for a long time, or fixing aircraft engines, they have a lot of expertise and a lot of understanding, and that's really important to making any kind of AI driven solution work. >> That's great insight but that but I do think one thing that's changed you come from a world where you had big data sets, so you kind of have a big data set point of view, where I think for a lot of decision makers they didn't have that data before. So we won't go through all the up until the right explosions of data, and obviously we're talking about Exascale day, but I think for a lot of processes now, the amount of data that they can bring to bear, is so dwarfs what they had in the past that before they even consider how to use it they still have to contextualize it, and they have to manage it and they have to organize it and there's data silos. So there's all this kind of nasty processes stuff, that's in the way some would argue has been kind of a real problem with the promise of BI, and does decision support tools. So as you look at at this new stuff and these new datasets, what are some of the people in process challenges beyond the obvious things that we can think about, which are the technical challenges? >> So I think that you've really hit on, something I talk about sometimes it was kind of a data deluge that we experienced these days, and the notion of feeling like you're drowning in information but really lacking any kind of insight. And one of the things that I like to think about, is to actually step back from the data questions the infrastructure questions, sort of all of these technical questions that can seem very challenging to navigate. And first ask ourselves, what problems am I trying to solve? It's really no different than any other type of decision you might make in an organization to say like, what are my biggest pain points? What keeps me up at night? or what would just transform the way my business works? And those are the problems worth solving. And then the next question becomes, if I had more data if I had a better understanding of something about my business or about my customers or about the world in which we all operate, would that really move the needle for me? And if the answer is yes, then that starts to give you a picture of what you might be able to do with AI, and it starts to tell you which of those data management challenges, whether they be cleaning the data, whether it be organizing the data, what it, whether it be building models on the data are worth solving because you're right, those are going to be a time intensive, labor intensive, highly iterative efforts. But if you know why you're doing it, then you will have a better understanding of why it's worth the effort. And also which shortcuts you can take which ones you can't, because often in order to sort of see the end state you might want to do a really quick experiment or prototype. And so you want to know what matters and what doesn't at least to that. Is this going to work at all time. >> So you're not buying the age old adage that you just throw a bunch of data in a data Lake and the answers will just spring up, just come right back out of the wall. I mean, you bring up such a good point, It's all about asking the right questions and thinking about asking questions. So again, when you talk to people, about helping them think about the questions, cause then you've got to shape the data to the question. And then you've got to start to build the algorithm, to kind of answer that question. How should people think when they're actually building algorithm and training algorithms, what are some of the typical kind of pitfalls that a lot of people fall in, haven't really thought about it before and how should people frame this process? Cause it's not simple and it's not easy and you really don't know that you have the answer, until you run multiple iterations and compare it against some other type of reference? >> Well, one of the things that I like to think about just so that you're sort of thinking about, all the challenges you're going to face up front, you don't necessarily need to solve all of these problems at the outset. But I think it's important to identify them, is I like to think about AI solutions as, they get deployed being part of a kind of workflow, and the workflow has multiple stages associated with it. The first stage being generating your data, and then starting to prepare and explore your data and then building models for your data. But sometimes I think where we don't always think about it is the next two phases, which is deploying whatever model or AI solution you've developed. And what will that really take especially in the ecosystem where it's going to live. If is it going to live in a secure and compliant ecosystem? Is it actually going to live in an outdoor ecosystem? We're seeing more applications on the edge, and then finally who's going to use it and how are they going to drive value from it? Because it could be that your AI solution doesn't work cause you don't have the right dashboard, that highlights and visualizes the data for the decision maker who will benefit from it. So I think it's important to sort of think through all of these stages upfront, and think through maybe what some of the biggest challenges you might encounter at the Mar, so that you're prepared when you meet them, and you can kind of refine and iterate along the way and even upfront tweak the question you're asking. >> That's great. So I want to get your take on we're celebrating Exascale day which is something very specific on 1018, share your thoughts on Exascale day specifically, but more generally I think just in terms of being a data scientist and suddenly having, all this massive compute power. At your disposal yoy're been around for a while. So you've seen the development of the cloud, these huge data sets and really the ability to, put so much compute horsepower against the problems as, networking and storage and compute, just asymptotically approach zero, I mean for as a data scientist you got to be pretty excited about kind of new mysteries, new adventures, new places to go, that we just you just couldn't do it 10 years ago five years ago, 15 years ago. >> Yeah I think that it's, it'll--only time will tell exactly all of the things that we'll be able to unlock, from these new sort of massive computing capabilities that we're going to have. But a couple of things that I'm very excited about, are that in addition to sort of this explosion or these very large investments in large supercomputers Exascale super computers, we're also seeing actually investment in these other types of scientific instruments that when I say scientific it's not just academic research, it's driving pharmaceutical drug discovery because we're talking about these, what they call light sources which shoot x-rays at molecules, and allow you to really understand the structure of the molecules. What Exascale allows you to do is, historically it's been that you would go take your molecule to one of these light sources and you shoot your, x-rays edit and you would generate just masses and masses of data, terabytes of data it was each shot. And being able to then understand, what you were looking at was a long process, getting computing time and analyzing the data. We're on the precipice of being able to do that, if not in real time much closer to real time. And I don't really know what happens if instead of coming up with a few molecules, taking them, studying them, and then saying maybe I need to do something different. I can do it while I'm still running my instrument. And I think that it's very exciting, from the perspective of someone who's got a scientific background who likes using large data sets. There's just a lot of possibility of what Exascale computing allows us to do in from the standpoint of I don't have to wait to get results, and I can either stimulate much bigger say galaxies, and really compare that to my data or galaxies or universes, if you're an astrophysicist or I can simulate, much smaller finer details of a hypothetical molecule and use that to predict what might be possible, from a materials or drug perspective, just to name two applications that I think Exascale could really drive. >> That's really great feedback just to shorten that compute loop. We had an interview earlier in some was talking about when the, biggest workload you had to worry about was the end of the month when you're running your financial, And I was like, why wouldn't that be nice to be the biggest job that we have to worry about? But now I think we saw some of this at animation, in the movie business when you know the rendering for whether it's a full animation movie, or just something that's a heavy duty three effects. When you can get those dailies back to the, to the artist as you said while you're still working, or closer to when you're working versus having this, huge kind of compute delay, it just changes the workflow dramatically and the pace of change and the pace of output. Because you're not context switching as much and you can really get back into it. That's a super point. I want to shift gears a little bit, and talk about explainable AI. So this is a concept that a lot of people hopefully are familiar with. So AI you build the algorithm it's in a box, it runs and it kicks out an answer. And one of the things that people talk about, is we should be able to go in and pull that algorithm apart to know, why it came out with the answer that it did. To me this just sounds really really hard because it's smart people like you, that are writing the algorithms the inputs and the and the data that feeds that thing, are super complex. The math behind it is very complex. And we know that the AI trains and can change over time as you you train the algorithm it gets more data, it adjusts itself. So it's explainable AI even possible? Is it possible at some degree? Because I do think it's important. And my next question is going to be about ethics, to know why something came out. And the other piece that becomes so much more important, is as we use that output not only to drive, human based decision that needs some more information, but increasingly moving it over to automation. So now you really want to know why did it do what it did explainable AI? Share your thoughts. >> It's a great question. And it's obviously a question that's on a lot of people's mind these days. I'm actually going to revert back to what I said earlier, when I talked about Bernoulli's principle, and just the ability sometimes when you do throw an algorithm at data, it might come the first thing it will find is probably some known law of physics. And so I think that really thinking about what do we mean by explainable AI, also requires us to think about what do we mean by AI? These days AI is often used anonymously with deep learning which is a particular type of algorithm that is not very analytical at its core. And what I mean by that is, other types of statistical machine learning models, have some underlying theory of what the population of data that you're studying. And whereas deep learning doesn't, it kind of just learns whatever pattern is sitting in front of it. And so there is a sense in which if you look at other types of algorithms, they are inherently explainable because you're choosing your algorithm based on what you think the is the sort of ground truth, about the population you're studying. And so I think we going to get to explainable deep learning. I think it's kind of challenging because you're always going to be in a position, where deep learning is designed to just be as flexible as possible. I'm sort of throw more math at the problem, because there may be are things that your sort of simpler model doesn't account for. However deep learning could be, part of an explainable AI solution. If for example, it helps you identify what are important so called features to look at what are the important aspects of your data. So I don't know it depends on what you mean by AI, but are you ever going to get to the point where, you don't need humans sort of interpreting outputs, and making some sets of judgments about what a set of computer algorithms that are processing data think. I think it will take, I don't want to say I know what's going to happen 50 years from now, but I think it'll take a little while to get to the point where you don't have, to maybe apply some subject matter understanding and some human judgment to what an algorithm is putting out. >> It's really interesting we had Dr. Robert Gates on a years ago at another show, and he talked about the only guns in the U.S. military if I'm getting this right, that are automatic, that will go based on what the computer tells them to do, and start shooting are on the Korean border. But short of that there's always a person involved, before anybody hits a button which begs a question cause we've seen this on the big data, kind of curve, i think Gartner has talked about it, as we move up from kind of descriptive analytics diagnostic analytics, predictive, and then prescriptive and then hopefully autonomous. So I wonder so you're saying will still little ways in that that last little bumps going to be tough to overcome to get to the true autonomy. >> I think so and you know it's going to be very application dependent as well. So it's an interesting example to use the DMZ because that is obviously also a very, mission critical I would say example but in general I think that you'll see autonomy. You already do see autonomy in certain places, where I would say the States are lower. So if I'm going to have some kind of recommendation engine, that suggests if you look at the sweater maybe like that one, the risk of getting that wrong. And so fully automating that as a little bit lower, because the risk is you don't buy the sweater. I lose a little bit of income I lose a little bit of revenue as a retailer, but the risk of I make that turn, because I'm going to autonomous vehicle as much higher. So I think that you will see the progression up that curve being highly dependent on what's at stake, with different degrees of automation. That being said you will also see in certain places where there's, it's either really expensive or it's humans aren't doing a great job. You may actually start to see some mission critical automation. But those would be the places where you're seeing them. And actually I think that's one of the reasons why you see actually a lot more autonomy, in the agriculture space, than you do in the sort of passenger vehicle space. Because there's a lot at stake and it's very difficult for human beings to sort of drive large combines. >> plus they have a real they have a controlled environment. So I've interviewed Caterpillar they're doing a ton of stuff with autonomy. Cause they're there control that field, where those things are operating, and whether it's a field or a mine, it's actually fascinating how far they've come with autonomy. But let me switch to a different industry that I know is closer to your heart, and looking at some other interviews and let's talk about diagnosing disease. And if we take something specific like reviewing x-rays where the computer, and it also brings in the whole computer vision and bringing in computer vision algorithms, excuse me they can see things probably fast or do a lot more comparisons, than potentially a human doctor can. And or hopefully this whole signal to noise conversation elevate the signal for the doctor to review, and suppress the noise it's really not worth their time. They can also review a lot of literature, and hopefully bring a broader potential perspective of potential diagnoses within a set of symptoms. You said before you both your folks are physicians, and there's a certain kind of magic, a nuance, almost like kind of more childlike exploration to try to get out of the algorithm if you will to think outside the box. I wonder if you can share that, synergy between using computers and AI and machine learning to do really arduous nasty things, like going through lots and lots and lots and lots of, x-rays compared to and how that helps with, doctor who's got a whole different kind of set of experience a whole different kind of empathy, whole different type of relationship with that patient, than just a bunch of pictures of their heart or their lungs. >> I think that one of the things is, and this kind of goes back to this question of, is AI for decision support versus automation? And I think that what AI can do, and what we're pretty good at these days, with computer vision is picking up on subtle patterns right now especially if you have a very large data set. So if I can train on lots of pictures of lungs, it's a lot easier for me to identify the pictures that somehow these are not like the other ones. And that can be helpful but I think then to really interpret what you're seeing and understand is this. Is it actually bad quality image? Is it some kind of some kind of medical issue? And what is the medical issue? I think that's where bringing in, a lot of different types of knowledge, and a lot of different pieces of information. Right now I think humans are a little bit better at doing that. And some of that's because I don't think we have great ways to train on, sort of sparse datasets I guess. And the second part is that human beings might be 40 years of training a model. They 50 years of training a model as opposed to six months, or something with sparse information. That's another thing that human beings have their sort of lived experience, and the data that they bring to bear, on any type of prediction or classification is actually more than just say what they saw in their medical training. It might be the people they've met, the places they've lived what have you. And I think that's that part that sort of broader set of learning, and how things that might not be related might actually be related to your understanding of what you're looking at. I think we've got a ways to go from a sort of artificial intelligence perspective and developed. >> But it is Exascale day. And we all know about the compound exponential curves on the computing side. But let's shift gears a little bit. I know you're interested in emerging technology to support this effort, and there's so much going on in terms of, kind of the atomization of compute store and networking to be able to break it down into smaller, smaller pieces, so that you can really scale the amount of horsepower that you need to apply to a problem, to very big or to very small. Obviously the stuff that you work is more big than small. Work on GPU a lot of activity there. So I wonder if you could share, some of the emerging technologies that you're excited about to bring again more tools to the task. >> I mean, one of the areas I personally spend a lot of my time exploring are, I guess this word gets used a lot, the Cambrian  explosion of new AI accelerators. New types of chips that are really designed for different types of AI workloads. And as you sort of talked about going down, and it's almost in a way where we were sort of going back and looking at these large systems, but then exploring each little component on them, and trying to really optimize that or understand how that component contributes to the overall performance of the whole. And I think one of the things that just, I don't even know there's probably close to a hundred active vendors in the space of developing new processors, and new types of computer chips. I think one of the things that that points to is, we're moving in the direction of generally infrastructure heterogeneity. So it used to be when you built a system you probably had one type of processor, or you probably had a pretty uniform fabric across your system you usually had, I think maybe storage we started to get tearing a little bit earlier. But now I think that what we're going to see, and we're already starting to see it with Exascale systems where you've got GPUs and CPUs on the same blades, is we're starting to see as the workloads that are running at large scales are becoming more complicated. Maybe I'm doing some simulation and then I'm running I'm training some kind of AI model, and then I'm inferring it on some other type, some other output of the simulation. I need to have the ability to do a lot of different things, and do them in at a very advanced level. Which means I need very specialized technology to do it. And I think it's an exciting time. And I think we're going to test, we're going to break a lot of things. I probably shouldn't say that in this interview, but I'm hopeful that we're going to break some stuff. We're going to push all these systems to the limit, and find out where we actually need to push a little harder. And I some of the areas I think that we're going to see that, is there We're going to want to move data, and move data off of scientific instruments, into computing, into memory, into a lot of different places. And I'm really excited to see how it plays out, and what you can do and where the limits are of what you can do with the new systems. >> Arti I could talk to you all day. I love the experience and the perspective, cause you've been doing this for a long time. So I'm going to give you the final word before we sign out and really bring it back, to a more human thing which is ethics. So one of the conversations we hear all the time, is that if you are going to do something, if you're going to put together a project and you justify that project, and then you go and you collect the data and you run that algorithm and you do that project. That's great but there's like an inherent problem with, kind of data collection that may be used for something else down the road that maybe you don't even anticipate. So I just wonder if you can share, kind of top level kind of ethical take on how data scientists specifically, and then ultimately more business practitioners and other people that don't carry that title. Need to be thinking about ethics and not just kind of forget about it. That these are I had a great interview with Paul Doherty. Everybody's data is not just their data, it's it represents a person, It's a representation of what they do and how they lives. So when you think about kind of entering into a project and getting started, what do you think about in terms of the ethical considerations and how should people be cautious that they don't go places that they probably shouldn't go? >> I think that's a great question out a short answer. But I think that I honestly don't know that we have a great solutions right now, but I think that the best we can do is take a very multifaceted, and also vigilant approach to it. So when you're collecting data, and often we should remember a lot of the data that gets used isn't necessarily collected for the purpose it's being used, because we might be looking at old medical records, or old any kind of transactional records whether it be from a government or a business. And so as you start to collect data or build solutions, try to think through who are all the people who might use it? And what are the possible ways in which it could be misused? And also I encourage people to think backwards. What were the biases in place that when the data were collected, you see this a lot in the criminal justice space is the historical records reflect, historical biases in our systems. And so is I there are limits to how much you can correct for previous biases, but there are some ways to do it, but you can't do it if you're not thinking about it. So I think, sort of at the outset of developing solutions, that's important but I think equally important is putting in the systems to maintain the vigilance around it. So one don't move to autonomy before you know, what potential new errors you might or new biases you might introduce into the world. And also have systems in place to constantly ask these questions. Am I perpetuating things I don't want to perpetuate? Or how can I correct for them? And be willing to scrap your system and start from scratch if you need to. >> Well Arti thank you. Thank you so much for your time. Like I said I could talk to you for days and days and days. I love the perspective and the insight and the thoughtfulness. So thank you for sharing your thoughts, as we celebrate Exascale day. >> Thank you for having me. >> My pleasure thank you. All right she's Arti I'm Jeff it's Exascale day. We're covering on the queue thanks for watching. We'll see you next time. (bright upbeat music)

>>live from Washington, D. C. It's the Cube covering Bumi World 19 Do you buy movie? >>Welcome back to the Cubes. Coverage of Bumi World 19 I'm Lisa Martin with John Ferrier. John and I have a couple of gents joining us. To my right is Jeff Emerson, the global managing director for Custom Application Engineering Ethics Center. Hey, just hey, welcome. Thank you were glad to tell you, and we've got Steve would back with us. The CPO up. Hey, Steve, >>Hayley says. Ages >>minutes. You sticking into some research about that? The history of Bumi and Ipads and just looking at how the consumer ization effect has infected by a bad word has really infiltrated has that every industry organizations went from having enterprise applications. Legacy applications, cloud applications, custom applications. Let's talk about custom applications. What are you seeing in the customer marketplace for the demand for having this level of customization, whether it's a retail or or, you know, utilities company, >>it really doesn't matter what industry it is these days. Custom applications were going through a renaissance. It is. It is truly the renaissance of custom, where there was once a swing towards enterprise applications and the packages and so on. And now it's realized that oh gosh t separate ourselves from our competition. We have two great something that doesn't exist well, that is, by its nature, a custom application. And so these air coming up, Maura and Maur across the industry, and it's really starting to dominate the value chain for software. >>You were here in D. C. And public sector is going through a modernization as well. He looked at government procurement. I mean, essentially with data, everything's instrumental. You have unlimited resource with cloud computing. So essentially personalization. Hot trend. So applications air being personalized. They're customized. So every app should be not general purpose unless it's either under the covers. So this is the country's We've been having you guys. Bumi has a platform. You enable APS? You guys are deploying it. How are customers responded to this? Because to me they might go Will custom haps me, feels expensive. It feels one off the old adage. It's a one off, but it seems to be coming back. >>It does end. Fact is, you're able to do things so much more quickly today than you ever have been able to in the fast in the past and three ability to create new experiences quickly and react in an agile fashion to how those applications are being received in the marketplace. React to the data that is generated both as the primary data and the data exhaust from those systems to determine what your customers need, what they want, how they're going to act, what they're going to buy. All of those things are things that we can pull together so much more quickly today than we could ever in the past. And so it's great. >>Steve, We were talking earlier about how the data's real big part of the equation. Now everything about the application world it used to be the infrastructure would dictate what you could build. Yeah, now you have application developers saying, This is what I want now the infrastructures so programmable it's kind of flipped around that they're dictating kind of terms. >>Well, there's suddenly being this sort of emergence of these low code platforms to kind of help manage that. I mean, and they're kind of taking care of a lot of the infrastructure so you can kind of skill them is needed, but yeah, I mean, there's been a him. It's been a huge birthday. I couldn't agree more. There's like the demand for applications. We're seeing a lot. Sure there's the mega applications. We tend to leave those to our sister company Pivotal toe code those with this whole other ecosystem of applications everywhere. The personalization sze of the line of business needs to improve. Their business processes were going after that layer. We have to do it in the right way to make it super easy to do on the infrastructure that people expected to be with the architecture they expect to see. So they're highly customizable, so get exactly what they want. >>Jeff, you know, we always talk on the industry joke on the Cube, and the game is changed, but it's still the same. And every time a new trend comes, you know it's the death of something. A meteorite media to say something's dying when something new starts right. But nothing really changes everything about applications. It's the same gain just with a different twist. Do it with cloud. How are customers were spouting this? Because obviously his benefits business benefits cost benefits lot of mount up line with how they're attacking the application development. Then they got a data tsunami happening. But they gotta build APS, right, Not anything, right? >>It was once said that absolute in the world, and now it's really that data is feeding the world right? And so the amount of data that's out there and accessible and usable within applications is absolutely incredible. And so, with the emergence of the cloud in order, Thio support those massive amounts of data and Dr Rapid Development and then lo code to make that development much easier. These things all time come together, and you talked about the death of X, y or Z. We talk now about living systems, right on living systems are things that are easy to modify, their absolutely attainable and usable and expandable for for any kind of use and ultimately adaptable. >>So John mentioned the word one off a minute ago, and it reminded me of something where, you know, whatever industry that you're in, Not too long ago it was customers got some one off. Whether it's an application or part of the infrastructure, that's expensive, and it's not something that can be monetized but down to your point it's it's really custom. Applications are a big part of a business. Is competitive advantage. So what is it about the customized app? Is it Is it the fact that it's driven by an A P I? That's programmable that allows it to be customized at scale toe, where it's not a one off from a support perspective, it's something that really a company can use as that competitive leg up. >>Right in the this livings living systems world. We really have agile engineering, agile methods and so that we're doing development quickly. And we're doing this in an engineering fashion that has micro service's and small pieces of functionality that could be grabbed and plugged and played together. Thio great, different experiences. And so that granular ization of software is something that drives his flexibility and enables us to make modifications and updates quickly. >>Actually, ive your customer example that it was something we'd done, which is absence of term it like how the oil and gas industry saved nurses in Africa are saved people in Africa, which is we built, a solution that allowed them nurses in sub Saharan Africa to visit patients out in the field. They built it on a loco pa from witches. Flow part of me connected through a P eyes connected to all the infrastructure. But a lot of the work was on, uh, android tablets offline. So with the loco PA from that could deliver this solution with all offline capabilities, all the connectivity, all the integration, all built in without writing. Really any code? The only code there rose to customize the look and feel so looked exactly what they want. They delivered that on early version of our offline framework and then latterly the oil and gas industry origin energy deployed a similar solution to their rigs. The lot of you seem really complicated things of form, validation and better validation rules and better data synchronization that really forced us to improve our offline framework to something which is a Steve a big jump ahead of where it was before. And then lo and behold, the nurses nafta came back to us and said, Well, actually went up, Did or are we gonna run on desktops as well as ipads? Funnily enough, and we're like, Well, good news. We've actually already added that support. And so literally from three days of that phone call to them going live within our laptops and ipads. That was all it took. They didn't have to write any CO. They literally We just give them access to the new you. I will find framework. They installed it, turn off the wet. And that's kind of the power of this kind of next gen of up building that for this kind of line of business applications where you just need to innovate, how you work, you don't have to spend three years rebuilding those for iPad and >>Jeff houses. That dynamic, which is pretty much, I think, consisted a lot of these new APS. How's it changed your business? Because you know the theme that we've been identifying the mega trend is that there's more project work going on fast time to value, agile. You guys been doing exceptional work there and following Madu Center's been doing talking to Paul Doherty amongst others. Get a huge data science team you guys are on. I know you guys have transformed but big project and now a bunch of little projects going on, so it's kind of have to make you guys more agile as a practice because you've got to go out and solve the business problems with the customers. How is this dynamic changed? >>You're right. We absolutely do. And we have to assume muchas anything. It's helping our customers get into that mode of thinking as well. What was once a six months of gathering and documenting requirements is now done in a handful of ours. At first to get the first small bit of what's gonna be valuable functionality to put out there. And you keep doing that irritably. Overtime is instead of in a six month period, but then gets thrown over the wall. Thio have other people do this for another build stuff for six or nine >>months. I mean, the federation and getting those winds early gets proof points, gets mo mentum validation. You're not waiting for a gestation period. >>You make good decisions about what to do next on DDE. What to not do that you were planning on doing but turns out, doesn't mean >>I want to get your thoughts on something important you mentioned humanization. We see that big trend because Avery people centric and you're thinking at Bumi and we've had this debate in the queue we? We didn't come in on either side yet, but you know it orations great, great, fast. But the old days of software was a lot of craftsmanship involved, you know, crafting the product, getting it right now it's ship be embarrassed, ship it fast and then injury, which is great for efficiency. But there's a trend coming back to crafting product. They're absolutely. What is your thoughts on this? Because craft Manship is now design thinking. Would it be calling in different names? But this is a new thing. It's happening all the time. >>That software software craftsmanship is something that is more important today than it ever has been. Because you're going fast. And because you're putting things out into the market very quickly, you can't afford to make big mistakes, right? You could make functional decision mistakes, right? Oh, that wasn't the right thing for the customer, but having it not work or creating it bad experiences, right? Very bad, right? And so that craftsmanship building in all the Dev Ops pipelines and the error check in the testing and gateways and security checking all that happens automatically every time you check in code that is critical and it drives that craftsmanship back to the developer, right? Pushing left so that you make a mistake. You fix it within minutes, as opposed to >>you. Run private engineering, Smile on your face. Come on, What's your angle on this >>time? And craftsmanship is obviously huge mean? When we thought about like Bumi, we kind of wanted to make sure that yeah, that way we used to talk a little bit. No code platforms. And I think that what they did was they left out the craftsmanship that developers could do. And I've kind of thought it was like, Hey, if you can put like the business or the person who really understands the process of the application into the beating heart of the creation process so they could be on the right side of the soft waiting the world like they could be a creator and producer as much as they could be a consumer of applications, you allow them to do that and then let developers have radiate that that out with new engage with models, coding out new experiences that really hyper specific to the EU's case of the user. That's kind of the ultimate you get the core business value, and then you get the craftsmanship of the engineers together, I think, >>and I'm glad you said that because there's so many cases where here, we want to push it so that we don't even need software engineers for our software. And that's an interesting idea. Yeah, but it's actually not a good idea. Ll know or idea, Yes, simply because you there's an important aspect of software and and how I t runs that even if you have low coach, uh, components in order to drive the functionality right, these things that have to be done. But frankly, professional software engineers know how to do. >>It's better and faster and easier to do it that way. >>I think the federation certainly makes the problem that you're trying to solve. Solvable, right? Don't take your eye off the main ball, which is saw the problem, but get it elegantly designed all right, but I think that's a good This is a big discussion. You're seeing a lot with loco. So again, this is back. The custom maps custom APS just means a targeted app that solves a specific problem because it is a unique problem and it's different than the other one, right? That's the speed game. It's a speed game to nose in it that fast, fast, fast. >>And the engineering methods have changed really over the last couple of decades. While I've been doing this. Where way talked a moment about ago about the waterfall ways and then the agile ways. And the simple fact of the matter is that you're developing small pieces of software to get out into the market quickly, and you can do this in a matter of days and weeks. A supposed to months and quarters >>right, which many businesses don't have that time for company competitors gonna get in there. I'm curious as the development methods have changed so dramatically have the customer conversations like Are you guys talking more with business leaders vs You know the Guys and Girls and Dev Ops is that is this movie business little conversation that a CEO CFO, a CEO is involved in? >>So from our perspective at Accenture that the technology is always there to drive a business need, and so that conversation is first with the business owners, and that was true 20 years ago as well. The a CZ much as we do. I t transformation. It's a business lead. I transformation and, more often, technology supported business transformation. >>Excellent. Well, guys, thank you for joining John and me on the program today. Talking about all the things that you guys are seeing out in the field. Exciting stuff. >>Thanks for having us. >>We appreciate your time. Thank you for John Ferrier. I'm Lisa Martin. You're watching the Cube from Bhumi World 19. Thanks for watching.

>> Narrator: From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE. Covering When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back here everybody Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel. At a little event it's When IoT Met AI: The Intelligence of Things. As we hear about the Internet of Things all the time this is really about the data elements behind AI, and machine learning, and IoT. And we're going to get into it with some of the special guests here. We're excited to get the guy that's going to kick off this whole program shortly is Tom Stuermer. He is the I got to get the new title, the Global Managing Director, Ecosystem and Partnership, from Accenture. Tom, welcome-- >> Thank you, Jeff. >> And congrats on the promotion. >> Thank you. >> So IoT, AI, buzz words, a lot of stuff going on but we're really starting to see stuff begin to happen. I mean there's lots of little subtle ways that we're seeing AI work its way in to our lives, and machine learning work our way into its life, but obviously there's a much bigger wave that's about to crest here, shortly. So as you kind of look at the landscape from your point of view, you get to work with a lot of customers, you get to see this stuff implemented in industry, what's kind of your take on where we are? >> Well, I would say that we're actually very early. There are certain spaces with very well-defined parameters where AI's been implemented successfully, industrial controls on a micro level where there's a lot of well-known parameters that the systems need to operate in. And it's been very easy to be able to set those parameters up. There's been a lot of historical heuristic systems to kind of define how those work, and they're really replacing them with AI. So in the industrial spaces a lot of take up and we'll even talk a little bit later about Siemens who's really created a sort of a self-managed factory. Who's been able to take that out from a tool level, to a system level, to a factory level, to enable that to happen at those broader capabilities. I think that's one of the inflection points we're going to see in other areas where there's a lot more predictability and a lot of other IoT systems. To be able to take that kind of system level and larger scale factors of AI and enable prediction around that, like supply chains for example. So we're really not seeing a lot of that yet, but we're seeing some of the micro pieces being injected in where the danger of it going wrong is lower, because the training for those systems is very difficult. >> It's interesting, there's so much talk about the sensors, and the edge, and edge computing, and that's interesting. But as you said it's really much more of a system approach is what you need. And it's really kind of the economic boundaries of the logical system by which you're trying to make a decision in. We talk all the time, we optimizing for one wind turbine? Are you optimizing for one field that contains so many wind turbines? Are you optimizing for the entire plant? Or are you optimizing for a much bigger larger system that may or may not impact what you did on that original single turbine? So a systems approach is a really critical importance. >> It is and what we've seen is that IoT investments have trailed a lot of expectations as to when they were going to really jump in the enterprise. And what we're finding is that when we talk to our customers a lot of them are saying, look I've already got data. I've got some data. Let's say I'm a mining company and I've got equipment down in mines, I've got sensors around oxygen levels, I just don't get that much value from it. And part of the challenge is that they're looking at it from a historical data perspective. And they're saying well I can see the trajectory over time of what's happening inside of my mind. But I haven't really been able to put in prediction. I haven't been able to sort of assess when equipment might fail. And so we're seeing that when we're able to show them the ability to affect an eventual failure that might shut down revenue for a day or two when some significant equipment fails, we're able to get them to start making those investments and they're starting to see the value in those micro pockets. And so I think we're going to see it start to propagate itself through in a smaller scale, and prove itself, because there's a lot of uncertainty. There's a lot of work that's got to be done to stitch them together, and IoT infrastructure itself is already a pretty big investment as it is. >> Short that mine company, because we had Caterpillar on a couple weeks ago and you know their driving fleets of autonomous vehicles, they're talking about some of those giant mining trucks who any unscheduled downtime the economic impact is immense well beyond worrying about a driver being sick, or had a fight with his wife, or whatever reason is bringing down the productivity of those vehicles. So it's actually amazing the little pockets where people are doing it. I'm curious to get your point of view too on kind of you managed to comment the guy's like I'm not sure what the value is because the other kind of big topic that we see is when will the data and the intelligence around the data actually start to impact the balance sheet? Because data used to be kind of a pain, right? You had to store it, and keep it, and it cost money, and you had to provision servers, and storage, but really now and the future the data that you have, the algorithms you apply to it will probably be an increasing percentage of your asset value if not the primary part of you asset value, you seeing some people start to figure that out? >> Well they are. So if you look, if step back away from IoT for a minute and you look at how AI is being applied more broadly, we're finding some transformational value propositions that are delivering a lot of impacts to the bottom line. And it's anywhere from where people inside of a company interact with their customers, being able to anticipate their next move, being able to predict given these parameters of this customer what kind of customer care agent should I put on the phone with them before you even pick up the phone to anticipate some of those expectations. And we're seeing a lot of value in things like that. And so, excuse me, and so when you zoom it back in to IoT some of the challenges are that the infrastructure to implement IoT is very fragmented. There's 360 some IoT platform providers out in the world and the places where we're seeing a lot of traction in using predictive analytics and AI for IoT is really coming in the verticals like industrial equipment manufacturers where they've kind of owned the stack and they can define everything from the bottom up. And what they're actually being able to do is to start to sell product heavy equipment by the hour, by the use, because they're able to get telemeter off of that product, see what's happening, be able to see when a failure is about to come, and actually sell it as a service back to a customer and be able to predictably analyze when something fails and get spares there in time. And so those are some of the pockets where it's really far ahead because they've got a lot of vertical integration of what's happening. And I think the challenge on adoption of broader scale for companies that don't sell very expensive assets into the market is how do I as a company start to stitch my own assets that are for all kinds of different providers, and all kinds of the different companies, into a single platform? And what the focus has really been in IoT lately for the past couple of years is what infrastructure should I place to get the data? How do I provision equipment? How do I track it? How do I manage it? How do I get the data back? And I think that's necessary but completely insufficient to really get a lot of value IoT, because really all your able to do then is get data. What do you do with it? All the value is really in the data itself. And so the alternative approach a lot of companies are taking is starting to attack some of these smaller problems. And each one of them tends to have a lot of value on its own, and so they're really deploying that way. And some of them are looking for ways to let the battles of the platforms, let's at least get from 360 down to 200 so that I can make some bets. And it's actually proving to be a value, but I think that is one of the obstacles that we have to adoption. >> The other thing you mentioned interesting before we turned on the cameras is really thinking about AI as a way to adjust the way that we interact with the machines. There's two views of the machines taking over the world, is it the beautiful view, or we can freeze this up to do other things? Or certainly nobody has a job, right? The answer is probably somewhere in the middle. But clearly AI is going to change the way, and we're starting to see just the barely the beginnings with Alexa, and Siri, and Google Home, with voice interfacing and the way that we interact with these machines which is going to change dramatically with the power of, as you said, prescriptive analytics, presumptive activity, and just change that interaction from what's been a very rote, fixed, hard to change to putting as you said, some of these lighter weight, faster to move, more agile layers on the top stack which can still integrate with some of those core SAP systems, and systems of record in a completely different way. >> Exactly, you know I often use the metaphor of autonomous driving and people seem to think that that's kind of way far out there. But if you look at how driving an autonomous vehicle's so much different from driving a regular car, right? You have to worry about at the minutia of executing the driving process. You don't have to worry about throttle, break. You'd have to worry about taking a right turn on red. You'd have to worry about speeding. What you have to worry about is the more abstract concepts of source, destination, route that I might want to take. You can offload that as well. And so it changes what the person interacting with the AI system is actually able to do, and the level of cognitive capability that they're able to exercise. We're seeing similar things in medical treatment. We're using AI to do predictive analytics around injury coming off of medical equipment. It's not only starting to improve diagnoses in certain scenarios, but it's also enabling the techs and the doctors involved in the scans to think on a more abstract level about what the broader medical issues are. And so it's really changing sort of the dialogue that's happening around what's going on. And I think this is a good metaphor for us to look at when we talk about societal impacts of AI as well. Because there are some people who embrace moving forward to those higher cognitive activities and some who resist it. But I think if you look at it from a customer standpoint as well, no matter what business you're in if you're a services business, if you're a product business, the way you interact with your employees and the way you interact with your customers can fundamentally be changed with AI, because AI can enable the technology to bend it to your intentions. Someone at the call center that we talked about. I mean those are subtle activities. It's not just AI for voice recognition, but it's also using AI to alter what options are given to you, and what scenarios are going to be most beneficial. And more often than not you get it right. >> Well the other great thing about autonomous vehicles, it's just a fun topic because it's something that people can understand, and they can see, and they can touch in terms of a concept to talk about, some of these higher level concepts. But the second order impacts which most people don't even begin to think, they're like I want to drive my car is, you don't need parking lots anymore because the cars can all park off site. Just Like they do at airports today at the rental car agency. You don't need to build a crash cage anymore, because the things are not going to crash that often compared to human drivers. So how does the interior experience of a car change when you don't have to build basically a crash cage? I mean there's just so many second order impacts that people don't even really begin to think about. And we see this time and time again, we saw it with cloud innovation where it's not just is it cheaper to rent a server from Amazon than to buy one from somebody else? It's does the opportunity for innovation enable more of your people to make more contributions than they could before because they were too impatient to wait to order the server from the IT guy? So that's where I think too people so underestimate kind of the big Moore's Law my favorite, we overestimate in the short term and completely underestimate in the long term, the impacts of these things. >> It's the doubling function, exactly. >> Jeff: Yeah, absolutely. >> I mean it's hard for people, human kind is geared towards linear thinking, and so when something like Moore's Law continues to double every 18 months price performance continues to increase. Storage, compute, visualization, display. >> Networking, 5G. >> You know the sensors in MEMS, all of these things have gotten so much cheaper. It's hard for human of any intelligence to really comprehend what happens when that doubling occurs for the next 20 years. Which we're now getting on the tail end of that fact. And so those manifest themselves in ways that are a little bit unpredictable, and I think that's going to be one of our most exciting challenges over the next five years is what does an enterprise look like? What does a product look like? One of the lessons that, I spent a lot of time in race car engineering in my younger days and actually did quants and analytics, what we learned from that point is as you learned about the data you started to fundamentally change the architecture of the product. And I think that's going to be a whole new series of activities that are going to have to happen in the marketplace. Is people rethinking fundamental product. There's a great example of a company that's completely disrupted an industry. On the surface of it it's been disrupted because of the fact that they essentially disassociated the consumption from the provision of the product. And didn't have to own those assets so they could grow rapidly. But what they fundamentally did was to use AI to be able to broker when should I get more cars, where should the cars go? And because they're also we're on the forefront of being able to drive, this whole notion of consumption of cars, and getting people's conceptual mindset shifted to having owned a car to I know an Uber's going to be there. It becomes like a power outlet. I can just rely on it. And now people are actually starting to double think about should I even own a car? >> Whole different impact of the autonomous vehicles. And if I do own a car why should it be sitting in the driveway when I'm not driving it? Or I send it out to go work for me make it a performing asset. Well great conversation. You guys Accenture's in a great spot. You're always at the cutting edge. I used to tease a guy I used to work with at Accenture you've got to squeeze out all the fat in the supply chain (laughs) your RP days and again a lot of these things are people changing the lens and seeing fat and inefficiency and then attacking it in a different way whether it's Uber, Airbnb, with empty rooms in people's houses. We had Paul Doherty on at the GE Industrial Internet launch a few years back, so you guys are in a great position because you get to sit right at the forefront and help these people make those digital transformations. >> I appreciate that. >> I will tell you I mean supply chains is another one of those high level systems opportunities for AI where being able to optimize, think about it a completely automated distribution chain from factory all the way to the drone landing at your front doorstep as a consumer. That's a whole nother level of efficiency that we can't even contemplate right now. >> Don't bet against Bezos that's what I always say. All right, Tom Stuermer thanks for spending a few minutes and good luck with the keynote. >> I appreciate it Jeff. >> All right, I'm Jeff Frick you're watching theCUBE. We are at The Intelligence of Things, When IoT met AI. You're watching theCUBE. Thanks for watching. (upbeat music)