(upbeat music) >> Welcome back, everyone, to theCUBE's coverage here live on the floor at AWS re:MARS 2022. I'm John Furrier, host for theCUBE. Great event, machine learning, automation, robotics, space, that's MARS. It's part of the re-series of events, re:Invent's the big event at the end of the year, re:Inforce, security, re:MARS, really intersection of the future of space, industrial, automation, which is very heavily DevOps machine learning, of course, machine learning, which is AI. We have Luis Ceze here, who's the CEO co-founder of OctoML. Welcome to theCUBE. >> Thank you very much for having me in the show, John. >> So we've been following you guys. You guys are a growing startup funded by Madrona Venture Capital, one of your backers. You guys are here at the show. This is a, I would say small show relative what it's going to be, but a lot of robotics, a lot of space, a lot of industrial kind of edge, but machine learning is the centerpiece of this trend. You guys are in the middle of it. Tell us your story. >> Absolutely, yeah. So our mission is to make machine learning sustainable and accessible to everyone. So I say sustainable because it means we're going to make it faster and more efficient. You know, use less human effort, and accessible to everyone, accessible to as many developers as possible, and also accessible in any device. So, we started from an open source project that began at University of Washington, where I'm a professor there. And several of the co-founders were PhD students there. We started with this open source project called Apache TVM that had actually contributions and collaborations from Amazon and a bunch of other big tech companies. And that allows you to get a machine learning model and run on any hardware, like run on CPUs, GPUs, various GPUs, accelerators, and so on. It was the kernel of our company and the project's been around for about six years or so. Company is about three years old. And we grew from Apache TVM into a whole platform that essentially supports any model on any hardware cloud and edge. >> So is the thesis that, when it first started, that you want to be agnostic on platform? >> Agnostic on hardware, that's right. >> Hardware, hardware. >> Yeah. >> What was it like back then? What kind of hardware were you talking about back then? Cause a lot's changed, certainly on the silicon side. >> Luis: Absolutely, yeah. >> So take me through the journey, 'cause I could see the progression. I'm connecting the dots here. >> So once upon a time, yeah, no... (both chuckling) >> I walked in the snow with my bare feet. >> You have to be careful because if you wake up the professor in me, then you're going to be here for two hours, you know. >> Fast forward. >> The average version here is that, clearly machine learning has shown to actually solve real interesting, high value problems. And where machine learning runs in the end, it becomes code that runs on different hardware, right? And when we started Apache TVM, which stands for tensor virtual machine, at that time it was just beginning to start using GPUs for machine learning, we already saw that, with a bunch of machine learning models popping up and CPUs and GPU's starting to be used for machine learning, it was clear that it come opportunity to run on everywhere. >> And GPU's were coming fast. >> GPUs were coming and huge diversity of CPUs, of GPU's and accelerators now, and the ecosystem and the system software that maps models to hardware is still very fragmented today. So hardware vendors have their own specific stacks. So Nvidia has its own software stack, and so does Intel, AMD. And honestly, I mean, I hope I'm not being, you know, too controversial here to say that it kind of of looks like the mainframe era. We had tight coupling between hardware and software. You know, if you bought IBM hardware, you had to buy IBM OS and IBM database, IBM applications, it all tightly coupled. And if you want to use IBM software, you had to buy IBM hardware. So that's kind of like what machine learning systems look like today. If you buy a certain big name GPU, you've got to use their software. Even if you use their software, which is pretty good, you have to buy their GPUs, right? So, but you know, we wanted to help peel away the model and the software infrastructure from the hardware to give people choice, ability to run the models where it best suit them. Right? So that includes picking the best instance in the cloud, that's going to give you the right, you know, cost properties, performance properties, or might want to run it on the edge. You might run it on an accelerator. >> What year was that roughly, when you were going this? >> We started that project in 2015, 2016 >> Yeah. So that was pre-conventional wisdom. I think TensorFlow wasn't even around yet. >> Luis: No, it wasn't. >> It was, I'm thinking like 2017 or so. >> Luis: Right. So that was the beginning of, okay, this is opportunity. AWS, I don't think they had released some of the nitro stuff that the Hamilton was working on. So, they were already kind of going that way. It's kind of like converging. >> Luis: Yeah. >> The space was happening, exploding. >> Right. And the way that was dealt with, and to this day, you know, to a large extent as well is by backing machine learning models with a bunch of hardware specific libraries. And we were some of the first ones to say, like, know what, let's take a compilation approach, take a model and compile it to very efficient code for that specific hardware. And what underpins all of that is using machine learning for machine learning code optimization. Right? But it was way back when. We can talk about where we are today. >> No, let's fast forward. >> That's the beginning of the open source project. >> But that was a fundamental belief, worldview there. I mean, you have a world real view that was logical when you compare to the mainframe, but not obvious to the machine learning community. Okay, good call, check. Now let's fast forward, okay. Evolution, we'll go through the speed of the years. More chips are coming, you got GPUs, and seeing what's going on in AWS. Wow! Now it's booming. Now I got unlimited processors, I got silicon on chips, I got, everywhere >> Yeah. And what's interesting is that the ecosystem got even more complex, in fact. Because now you have, there's a cross product between machine learning models, frameworks like TensorFlow, PyTorch, Keras, and like that and so on, and then hardware targets. So how do you navigate that? What we want here, our vision is to say, folks should focus, people should focus on making the machine learning models do what they want to do that solves a value, like solves a problem of high value to them. Right? So another deployment should be completely automatic. Today, it's very, very manual to a large extent. So once you're serious about deploying machine learning model, you got a good understanding where you're going to deploy it, how you're going to deploy it, and then, you know, pick out the right libraries and compilers, and we automated the whole thing in our platform. This is why you see the tagline, the booth is right there, like bringing DevOps agility for machine learning, because our mission is to make that fully transparent. >> Well, I think that, first of all, I use that line here, cause I'm looking at it here on live on camera. People can't see, but it's like, I use it on a couple couple of my interviews because the word agility is very interesting because that's kind of the test on any kind of approach these days. Agility could be, and I talked to the robotics guys, just having their product be more agile. I talked to Pepsi here just before you came on, they had this large scale data environment because they built an architecture, but that fostered agility. So again, this is an architectural concept, it's a systems' view of agility being the output, and removing dependencies, which I think what you guys were trying to do. >> Only part of what we do. Right? So agility means a bunch of things. First, you know-- >> Yeah explain. >> Today it takes a couple months to get a model from, when the model's ready, to production, why not turn that in two hours. Agile, literally, physically agile, in terms of walk off time. Right? And then the other thing is give you flexibility to choose where your model should run. So, in our deployment, between the demo and the platform expansion that we announced yesterday, you know, we give the ability of getting your model and, you know, get it compiled, get it optimized for any instance in the cloud and automatically move it around. Today, that's not the case. You have to pick one instance and that's what you do. And then you might auto scale with that one instance. So we give the agility of actually running and scaling the model the way you want, and the way it gives you the right SLAs. >> Yeah, I think Swami was mentioning that, not specifically that use case for you, but that use case generally, that scale being moving things around, making them faster, not having to do that integration work. >> Scale, and run the models where they need to run. Like some day you want to have a large scale deployment in the cloud. You're going to have models in the edge for various reasons because speed of light is limited. We cannot make lights faster. So, you know, got to have some, that's a physics there you cannot change. There's privacy reasons. You want to keep data locally, not send it around to run the model locally. So anyways, and giving the flexibility. >> Let me jump in real quick. I want to ask this specific question because you made me think of something. So we're just having a data mesh conversation. And one of the comments that's come out of a few of these data as code conversations is data's the product now. So if you can move data to the edge, which everyone's talking about, you know, why move data if you don't have to, but I can move a machine learning algorithm to the edge. Cause it's costly to move data. I can move computer, everyone knows that. But now I can move machine learning to anywhere else and not worry about integrating on the fly. So the model is the code. >> It is the product. >> Yeah. And since you said, the model is the code, okay, now we're talking even more here. So machine learning models today are not treated as code, by the way. So do not have any of the typical properties of code that you can, whenever you write a piece of code, you run a code, you don't know, you don't even think what is a CPU, we don't think where it runs, what kind of CPU it runs, what kind of instance it runs. But with machine learning model, you do. So what we are doing and created this fully transparent automated way of allowing you to treat your machine learning models if you were a regular function that you call and then a function could run anywhere. >> Yeah. >> Right. >> That's why-- >> That's better. >> Bringing DevOps agility-- >> That's better. >> Yeah. And you can use existing-- >> That's better, because I can run it on the Artemis too, in space. >> You could, yeah. >> If they have the hardware. (both laugh) >> And that allows you to run your existing, continue to use your existing DevOps infrastructure and your existing people. >> So I have to ask you, cause since you're a professor, this is like a masterclass on theCube. Thank you for coming on. Professor. (Luis laughing) I'm a hardware guy. I'm building hardware for Boston Dynamics, Spot, the dog, that's the diversity in hardware, it's tends to be purpose driven. I got a spaceship, I'm going to have hardware on there. >> Luis: Right. >> It's generally viewed in the community here, that everyone I talk to and other communities, open source is going to drive all software. That's a check. But the scale and integration is super important. And they're also recognizing that hardware is really about the software. And they even said on stage, here. Hardware is not about the hardware, it's about the software. So if you believe that to be true, then your model checks all the boxes. Are people getting this? >> I think they're starting to. Here is why, right. A lot of companies that were hardware first, that thought about software too late, aren't making it. Right? There's a large number of hardware companies, AI chip companies that aren't making it. Probably some of them that won't make it, unfortunately just because they started thinking about software too late. I'm so glad to see a lot of the early, I hope I'm not just doing our own horn here, but Apache TVM, the infrastructure that we built to map models to different hardware, it's very flexible. So we see a lot of emerging chip companies like SiMa.ai's been doing fantastic work, and they use Apache TVM to map algorithms to their hardware. And there's a bunch of others that are also using Apache TVM. That's because you have, you know, an opening infrastructure that keeps it up to date with all the machine learning frameworks and models and allows you to extend to the chips that you want. So these companies pay attention that early, gives them a much higher fighting chance, I'd say. >> Well, first of all, not only are you backable by the VCs cause you have pedigree, you're a professor, you're smart, and you get good recruiting-- >> Luis: I don't know about the smart part. >> And you get good recruiting for PhDs out of University of Washington, which is not too shabby computer science department. But they want to make money. The VCs want to make money. >> Right. >> So you have to make money. So what's the pitch? What's the business model? >> Yeah. Absolutely. >> Share us what you're thinking there. >> Yeah. The value of using our solution is shorter time to value for your model from months to hours. Second, you shrink operator, op-packs, because you don't need a specialized expensive team. Talk about expensive, expensive engineers who can understand machine learning hardware and software engineering to deploy models. You don't need those teams if you use this automated solution, right? Then you reduce that. And also, in the process of actually getting a model and getting specialized to the hardware, making hardware aware, we're talking about a very significant performance improvement that leads to lower cost of deployment in the cloud. We're talking about very significant reduction in costs in cloud deployment. And also enabling new applications on the edge that weren't possible before. It creates, you know, latent value opportunities. Right? So, that's the high level value pitch. But how do we make money? Well, we charge for access to the platform. Right? >> Usage. Consumption. >> Yeah, and value based. Yeah, so it's consumption and value based. So depends on the scale of the deployment. If you're going to deploy machine learning model at a larger scale, chances are that it produces a lot of value. So then we'll capture some of that value in our pricing scale. >> So, you have direct sales force then to work those deals. >> Exactly. >> Got it. How many customers do you have? Just curious. >> So we started, the SaaS platform just launched now. So we started onboarding customers. We've been building this for a while. We have a bunch of, you know, partners that we can talk about openly, like, you know, revenue generating partners, that's fair to say. We work closely with Qualcomm to enable Snapdragon on TVM and hence our platform. We're close with AMD as well, enabling AMD hardware on the platform. We've been working closely with two hyperscaler cloud providers that-- >> I wonder who they are. >> I don't know who they are, right. >> Both start with the letter A. >> And they're both here, right. What is that? >> They both start with the letter A. >> Oh, that's right. >> I won't give it away. (laughing) >> Don't give it away. >> One has three, one has four. (both laugh) >> I'm guessing, by the way. >> Then we have customers in the, actually, early customers have been using the platform from the beginning in the consumer electronics space, in Japan, you know, self driving car technology, as well. As well as some AI first companies that actually, whose core value, the core business come from AI models. >> So, serious, serious customers. They got deep tech chops. They're integrating, they see this as a strategic part of their architecture. >> That's what I call AI native, exactly. But now there's, we have several enterprise customers in line now, we've been talking to. Of course, because now we launched the platform, now we started onboarding and exploring how we're going to serve it to these customers. But it's pretty clear that our technology can solve a lot of other pain points right now. And we're going to work with them as early customers to go and refine them. >> So, do you sell to the little guys, like us? Will we be customers if we wanted to be? >> You could, absolutely, yeah. >> What we have to do, have machine learning folks on staff? >> So, here's what you're going to have to do. Since you can see the booth, others can't. No, but they can certainly, you can try our demo. >> OctoML. >> And you should look at the transparent AI app that's compiled and optimized with our flow, and deployed and built with our flow. That allows you to get your image and do style transfer. You know, you can get you and a pineapple and see how you look like with a pineapple texture. >> We got a lot of transcript and video data. >> Right. Yeah. Right, exactly. So, you can use that. Then there's a very clear-- >> But I could use it. You're not blocking me from using it. Everyone's, it's pretty much democratized. >> You can try the demo, and then you can request access to the platform. >> But you get a lot of more serious deeper customers. But you can serve anybody, what you're saying. >> Luis: We can serve anybody, yeah. >> All right, so what's the vision going forward? Let me ask this. When did people start getting the epiphany of removing the machine learning from the hardware? Was it recently, a couple years ago? >> Well, on the research side, we helped start that trend a while ago. I don't need to repeat that. But I think the vision that's important here, I want the audience here to take away is that, there's a lot of progress being made in creating machine learning models. So, there's fantastic tools to deal with training data, and creating the models, and so on. And now there's a bunch of models that can solve real problems there. The question is, how do you very easily integrate that into your intelligent applications? Madrona Venture Group has been very vocal and investing heavily in intelligent applications both and user applications as well as enablers. So we say an enable of that because it's so easy to use our flow to get a model integrated into your application. Now, any regular software developer can integrate that. And that's just the beginning, right? Because, you know, now we have CI/CD integration to keep your models up to date, to continue to integrate, and then there's more downstream support for other features that you normally have in regular software development. >> I've been thinking about this for a long, long, time. And I think this whole code, no one thinks about code. Like, I write code, I'm deploying it. I think this idea of machine learning as code independent of other dependencies is really amazing. It's so obvious now that you say it. What's the choices now? Let's just say that, I buy it, I love it, I'm using it. Now what do I got to do if I want to deploy it? Do I have to pick processors? Are there verified platforms that you support? Is there a short list? Is there every piece of hardware? >> We actually can help you. I hope we're not saying we can do everything in the world here, but we can help you with that. So, here's how. When you have them all in the platform you can actually see how this model runs on any instance of any cloud, by the way. So we support all the three major cloud providers. And then you can make decisions. For example, if you care about latency, your model has to run on, at most 50 milliseconds, because you're going to have interactivity. And then, after that, you don't care if it's faster. All you care is that, is it going to run cheap enough. So we can help you navigate. And also going to make it automatic. >> It's like tire kicking in the dealer showroom. >> Right. >> You can test everything out, you can see the simulation. Are they simulations, or are they real tests? >> Oh, no, we run all in real hardware. So, we have, as I said, we support any instances of any of the major clouds. We actually run on the cloud. But we also support a select number of edge devices today, like ARMs and Nvidia Jetsons. And we have the OctoML cloud, which is a bunch of racks with a bunch Raspberry Pis and Nvidia Jetsons, and very soon, a bunch of mobile phones there too that can actually run the real hardware, and validate it, and test it out, so you can see that your model runs performant and economically enough in the cloud. And it can run on the edge devices-- >> You're a machine learning as a service. Would that be an accurate? >> That's part of it, because we're not doing the machine learning model itself. You come with a model and we make it deployable and make it ready to deploy. So, here's why it's important. Let me try. There's a large number of really interesting companies that do API models, as in API as a service. You have an NLP model, you have computer vision models, where you call an API and then point in the cloud. You send an image and you got a description, for example. But it is using a third party. Now, if you want to have your model on your infrastructure but having the same convenience as an API you can use our service. So, today, chances are that, if you have a model that you know that you want to do, there might not be an API for it, we actually automatically create the API for you. >> Okay, so that's why I get the DevOps agility for machine learning is a better description. Cause it's not, you're not providing the service. You're providing the service of deploying it like DevOps infrastructure as code. You're now ML as code. >> It's your model, your API, your infrastructure, but all of the convenience of having it ready to go, fully automatic, hands off. >> Cause I think what's interesting about this is that it brings the craftsmanship back to machine learning. Cause it's a craft. I mean, let's face it. >> Yeah. I want human brains, which are very precious resources, to focus on building those models, that is going to solve business problems. I don't want these very smart human brains figuring out how to scrub this into actually getting run the right way. This should be automatic. That's why we use machine learning, for machine learning to solve that. >> Here's an idea for you. We should write a book called, The Lean Machine Learning. Cause the lean startup was all about DevOps. >> Luis: We call machine leaning. No, that's not it going to work. (laughs) >> Remember when iteration was the big mantra. Oh, yeah, iterate. You know, that was from DevOps. >> Yeah, that's right. >> This code allowed for standing up stuff fast, double down, we all know the history, what it turned out. That was a good value for developers. >> I could really agree. If you don't mind me building on that point. You know, something we see as OctoML, but we also see at Madrona as well. Seeing that there's a trend towards best in breed for each one of the stages of getting a model deployed. From the data aspect of creating the data, and then to the model creation aspect, to the model deployment, and even model monitoring. Right? We develop integrations with all the major pieces of the ecosystem, such that you can integrate, say with model monitoring to go and monitor how a model is doing. Just like you monitor how code is doing in deployment in the cloud. >> It's evolution. I think it's a great step. And again, I love the analogy to the mainstream. I lived during those days. I remember the monolithic propriety, and then, you know, OSI model kind of blew it. But that OSI stack never went full stack, and it only stopped at TCP/IP. So, I think the same thing's going on here. You see some scalability around it to try to uncouple it, free it. >> Absolutely. And sustainability and accessibility to make it run faster and make it run on any deice that you want by any developer. So, that's the tagline. >> Luis Ceze, thanks for coming on. Professor. >> Thank you. >> I didn't know you were a professor. That's great to have you on. It was a masterclass in DevOps agility for machine learning. Thanks for coming on. Appreciate it. >> Thank you very much. Thank you. >> Congratulations, again. All right. OctoML here on theCube. Really important. Uncoupling the machine learning from the hardware specifically. That's only going to make space faster and safer, and more reliable. And that's where the whole theme of re:MARS is. Let's see how they fit in. I'm John for theCube. Thanks for watching. More coverage after this short break. >> Luis: Thank you. (gentle music)

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome back to Vegas, Lisa Martin with John farrier. We are live at AWS reinvent in the expo hall at the sands convention center. There's tons of people in here. You could probably hear some of the background AWS expecting 65,000 or so folks. John, how many of those 65,000 and have you talked to in the last two days? >>Well, I can hear all the conversations happening at once. It's about hybrid cloud, IOT edge data, machine learning. my head's going to come. >>I was going to say lots of cool stuff. John and I are pleased to be joined by Greg Coralville, the VP of products and strategy for Blackberry Q. Next group. Welcome to the program >>to be here with 65,000 of our closest friends. >>His friends. Exactly. So Blackberry, cute X. What's it all about? >>What's it all about? Well, we do software. We do embedded software for mission critical systems at this event, at the AWS reinvent over showing a software and a really cool car, a karma, and we're connecting it to the AWS IOT backend services and showing some really, really cool use cases. Some of which are near term summer, which are a bit longer term are pretty exciting. Take a quick minute to describe Kunis. Is background acquired by Blackberry system history legacy? Exactly. Just take a quick minute to explain that. So we were founded in 1980 and then developing software for mission critical devices and medical, industrial. And then we started developing software for automotive in 1998 so we've been in automotive for about 20 years and developing originally an infotainment and then digital instrument clusters, telematic systems, gateways, safety systems, acoustics systems, pretty much becoming the software platform in the car because in the car, the car, the software is to be reliable, safe, secure. >>So we're trusted to deliver that. In automotive, we were acquired by Blackberry in 2010 and we're bringing the best of Blackberry and automotive and all of our other markets. So Lisa and I always talk about IOT is RPA automation. All this stuff's going on. But one of the things that comes up is we're trying to grok what's the software development environment in the cloud, in the car, and a Amazon one by having great API APIs. Yep. That was one of their core design principles. Is there a similar design principle from a car standpoint? Because if I'm an app developer, I just love, I have my mobile app sit on the car, right? But I don't want to have to become an expert on all the nuances of is there a connector? So is there going to be multiple platforms? What's the, what's the principle? Can you explain that a great question and great observation. >>So cars traditionally have been proprietary, pretty much closed systems and started open up with CarPlay and Android auto or all of a sudden you saw your mobile device being able to communicate with the car and now I could run Android apps, I could run iOS apps and started to open it up a bit. And now what you've seen is cars are becoming more connected, they're becoming more automated, eventually autonomous. Um, they're definitely, and what you're seeing in the car is in order for that car to really evolve and to offer connected services and shared mobility and the electrification that's occurring, the automotive industry is going through a disruption. We've all heard that and it really is true. So to the point where the electronics in the car, the networks in the car, the software in the car, it's getting completely redesigned and you're seeing a lot more high end processors. >>You're seeing safety critical systems, which have always been in cars, but now you're seeing a lot more complexity. And that speaks to exactly what we do. So where that car's going, if you think about it, is moving to more of a software platform. You have applications and mobile devices. Why? Because you've got Android and you've got iOS. That car is moving to that sort of a common platform where with the help of AWS connected services, the cubix Blackberry Punic software platform in the car, all of a sudden that'll open the door to that kind of environment to applications, to connected services. And that's exactly where it's going. So connectivities, it's here and it's going to be predominant through a pretty much all the vehicles coming off the line in the coming years. So you're going to see the connectivity and now we can bring the services and the apps to that vehicle. But at the same time you got to keep it safe, got to keep it secure. Gotta keep it reliable. You know, it's the classic mobile device, bingo literal device on wheels, right of two ton mobile device on wheels. >>Doc disruption sounds really cool and it's consumers. We just had this expectation that we can have whatever I want, the whole experience I want. And obviously as everything evolves, we want it to be safer and safer. And as there's laws and regulations that govern, Hey, you're going to get hefty fines if you're seeing with this device and you're driving. But disruption is really challenging, right? We talked, we got some great examples yesterday on stage with Andy Jassy of Goldman Sachs, right? How many years old are they and how they have leveraged disruption to revolutionize their consumer business or healthcare revolutionizing. I'd love to get your perspective on what are some of the automakers that are bleeding edge going, we get it. We want to work with you guys so that they understand that this the, you know, the, the mobile devices, the connected device on wheels is going to be transformative for their business. >>Good point. So first of all, every automaker we work with and we work, we work with almost 50 auto makers and we're over a hundred. We're in over 150 million vehicles and multiple systems in the cars. They're all putting safety first. That's never really changed. But that remains primary, primary objective. And to your point is how do you maintain that safety net reliability while at the same time opening the door to connectivity, making sure that vehicle is secure and resilient to attacks and whatnot. And you've seen some of those attacks in the past. And the industry is learning. Um, but that's, that's exactly what, that's what speaks to us and what we do. Same thing with AWS. If you think about what we do, we're plumbers. We, we build plumbing in the car, AWL splits, plumbing in the cloud. And I've had that call, those conversations with AWS and they're like, yeah, we're plumbers. >>And I said, so are we, we're going to get along great. But to your point, we have to keep our eye on security. Our definitely our eye on privacy and safety. And that's exactly what we do. As much as we all want the consumer apps and the connected experience at the same time, we can't compromise on that. So the good thing in automotive is there's a automotive safety standards, ISO two, six, two, six, two and whatnot, which we've certified our products to and we're going to keep doing that and keep delivering that software in the car. But that's awesome for 0.2 ton mobile device on wheels. So we got to always be aware of that. Great opportunity. People want more conduct and safety too. And that's a huge thing. Security and safety. I want to get to that in a second, but I got to ask you, um, what is the relationship that you guys have with Amazon? >>Could you explain that? And what are you guys doing at reinvent this year? Is your leg a presentation demo? Take a minute to explain the relationship between queen Nixon and Amazon web services and what you're showing here. Well, we're in the connected home exhibit. In fact, we're in the quote unquote garage where we've got a vehicle, a beautiful karma Rivero GT. And I was told it's the first time there's actually a car at reinvent. So that was pretty cool. And it's a cool car if you get a chance, come on over. And what we've done is we've taken the karma vehicle and we've actually connected it to AWS IOT. So if you think about what we do, we do software in the car, as I was saying earlier. And then we worked with the Amazon team, with the AWS team to say, okay, what can we do? So one of the things we're doing is we're doing battery monitoring and prediction in terms of the life of the battery. >>That's one of the things that we're doing. The other thing we're doing is personalized cockpit, which is, which is pretty exciting. And, and the last thing we're doing is kind of a business to business demonstration, um, where it's data orchestrations. If you think about the vehicle, there's a lot of sensors on the vehicle, a lot of information available on the vehicle. And what we're doing with AWS is pulling information from the vehicle, putting it in the cloud. And then we've got a few examples that we're using. So one of them is an application for an auto detailing company where they might want, you might want to have your vehicle detailed where we can make the position of your vehicle available, GPS, the VIN number. So the identify the identification of the vehicle. Um, and then you could actually contract with that expert detailings what we called them to come to your vehicle, clean the vehicle, detail your vehicle within a finite period of time securely. >>And then you'll get notified when it's done and whatnot. We're doing facial recognition in the vehicle and we also put some ML in machine learning in the car. We're actually showing gesture recognition where I can fold the mirrors with a, with a peace sign or victory signs. I could have the mirrors fold in. Uh, I can, I can interact with the infotainment system. I can personalize the music and whatnot. So really personalizing the cockpit. But all through the power of AWS. Sorry, what are we going to have to the car flying cars? Come on Jetsons flyers. I love this coming. Maybe not the flying carpet. Wow. Okay. Flying cars. Fine. I mean, I always say anything else that's in star Trek or star Wars will be invented. So I'm respecting some flying vehicles. All fun aside. Yeah. Now the serious conversation is safety and security. >>Worst case scenario, my car is hacked. Take over. This is a fear. Again, it's the worst. It's a doom season here. Those stories are straight. All IOT device. It's a car. How do you guys view the security posture? Um, good question. This is concerned. It might be on people's mind. Yeah. And that's what really speaks to where our company has been for almost four decades now. You know, when people would ask me, Hey, where would I find Punic software? Blackberry Punic software, I'd say almost everywhere, but the desktop. So where things have to be reliable, safe, secure work all the time. That's where you'll find our software. So factory floor, we're in laser eye surgery. Machines are in patient monitoring devices, MRI machines. And so essentially those areas which are safety critical, where safety, security and reliability, you know, our top real really industrial IOT thing, big time, big time. >>And that's the cool thing about walking around reinvent. There's all kinds of industrial devices and control. So if you go to the car now, if you think about the vehicle, same fundamental needs, reliability, safety, security, and we're trusted to deliver an automotive. So security is one of those things. It's not static. So when you, when you, when you make something that's secure, you're really building something that's resilient to attacks. So you'd be as resilient as possible to prevent attacks. And then you do whatever you can to prevent any malicious act or actions on that. So we will monitor what's going on in the system. We'll monitor any communications going to the car, for instance. So the minute we detect something a bit of normal, we can take action based on that. So that, that's absolutely key, especially given the cars connected and more and more becoming connected. >>What's the opportunity is in a trucking industry, when I think of the number of sensors on trucks, the regulations that you know for drivers safety in terms of how many hours they actually have to be able to can drive. What's the opportunity there for Q next? >>Good question. So everything we're doing in the car, which I should generalize and say a vehicle applies to trucks. So if you think about trucking or vehicles or drones or anything like that, you have multiple sensors that you have to interact with. You have to interpret that information, you have to take action based on that information. So if we look at trucking specifically, everybody knows a major shortage of truck truck, truck drivers. So when people ask me about autonomous cars and Hey, when are we going to see autonomy's vehicles? I always look at trucking and we're working with companies, trucking companies that are using our technology. And one of the first use cases that they're putting forward is something called platooning, where you'll actually have the first truck on the road with a driver and any other trucks on the road. We'll be operating autonomously essentially following like a train if you want on a highway, and then they'll have a starting location and a drop off location and that all of a sudden becomes a real world scenario, which makes use of the same sensors, LIDAR, radar cameras, et cetera. >>So from a trucking perspective, we look at it very similar to a car and automotive perspective because they need the same fundamental technologies. So pretty exciting. Like I said, what we do applies all over the place and again, all going to be connected. But grant, thanks for coming on. I really appreciate, I want to get your final thoughts, at least from my perspective on developers. When you see deep racer, you see that trend. It's kind of, they've got LIDAR, it's kind of a toy, but people geeking out on this. And so I would imagine that we're going to see an emergence of a software development environment where as a controlled sandboxes, cause yeah, they've got the concern with the industrial equipment. Exactly. Yeah. How do you balance that old school industrial mindset of, you know, IOT with the new rapid agile product development? Yeah. And to your point, we're going through that transition now. >>So this is where things like Sage maker come into play where I can develop out and develop and refine machine learning models in the cloud. You still have those tight control loops that you need and there's tools for that. So that's the deeply embedded stuff that's controlling actuators and whatnot. You still need that. But to your point, you need to be more iterative. You need to be more agile, need to develop according to the safety standards and the various industries that they might be in. So it's that is evolving and it's evolving at exactly the right pace. Really glad to see that evolution. But to your point, all of these devices are going to become interconnected. There's going to be new opportunities. And from a developer perspective, you know, we can't hire enough developers. No one can. It's really exciting whether it's IOT cloud developers or embedded developers. >>There's such an exciting future ahead. And I got to ask, this is just popped in my head. So I want to ask, cause I'm curious, um, spectrum and RF power is great, but you need connectivity to make an IOT device work, right? How do you guys, how does the car folks look at conductivity? Just when they get to a spot they can connect. So is it managing the spectrum? How are cars thinking about the connectivity? So we work very closely with the modem vendors. For instance, in today in cars you'll see Bluetooth, you'll see wifi, you'll see 4g. Obviously there's the emergence of 5g. Um, vehicle to vehicle communications is through something called DSRC. Essentially wifi 5g is going to come along, so now you're going to be able to have throughput and also what's called low latency. So quick turn around on your messages and the information being exchanged. >>So that too is evolving from a, from a QA software perspective, we'll make use of whatever modems there. But to your point, we also have to deal with the cases where I've lost connectivity. I still need that V vehicle to operate safely. And especially if you consider that the systems might be, um, uh, the systems might be connected or we don't want to make, make it such that they're dependent on that connectivity. So you have to have fail over scenarios and whatnot, but cars will become connected, devices will become connected. We're going to take advantage of that connectivity, but not be dependent on that connectivity. >>Well, Greg, please let me know when that, uh, personalized service is available so that my car can be found and detailed. They'd find it right in my driveway going lady, please. It's been a pleasure, a really cool stuff. Blackberry Kunis thank you for joining John. We'll be, we'll have to go check out that car for John furrier. I'm Lisa Martin. You're watching the cube live in Vegas at AWS. Reinvent 19. Thanks for watching.

>>live from Las Vegas. It's the two covering AWS executive Something >>brought to you by Accenture >>everyone to the ex Center Executive Summit here in AWS. Reinvent I'm your host, Rebecca Knight. I'm joined by John. Match it. He is the managing director. Applied Intelligence, North America Attic Center Thank you so much for coming on the Q. So we're gonna have a fun conversation about a I today. We tend to think of a I as this futuristic Star Trek Jetsons kind of thing. But in fact, a i a. I is happening here and now >>it's all around us. I think it's intricate zoologist, sort of blood into the fabric girl of our lives without really even knowing about, I mean, just to get here, Let me lives took a new burst. There's a I in the route navigation. We may have listened to Spotify, and there's a I and the recommendation engine. And if you want to check the weather with Alexa, there's a lot of agents in the natural language processing, and none of that was really impossible 10 years ago. So without even trying, just wake up and I sort of like in your system in your blood. >>So as consumers, we deal with a I every day. But it's all but businesses are also using a I, and it's already having an impact. >>I think >>what is absolutely true it and really interesting is that information is just the new basis of competition. Like like you know, companies used to compete with physical objects and look better cars and blenders and stereos and, you know, thermometers. But today, you know, they're all like on a device, and so information is how they compete. And what's interesting to me about that for our clients is that if you have a good idea, you can probably do it. And so you're limited, really by your own imagination on. So I just as an example of like how things are playing out a lover classroom, the farmer space to make better drugs, and every every form of company I know of is using some sort of machine learning a I to create better pharmaceuticals, the big ones, but also the new entrance. One of the companies that we followed numerator really issued company. What they've been able to do is like in just just a massive amount of data like all day, like good data, bad bias on buying >>its ingesting, this kind of data the data is about. >>It's about like drug efficacy, human health, the human genome like like like doctors visits like all this diverse information. And historically, if you put all that data together just to have a way to actually examine it, there's no way that was too much. Humans can't deal with it, but but But machine learning can. And so what? We just all this date up and we let the robots decided sort of less meaningful. And what's happened is you can now deal with instead, just a very fraction that data, but all of it. And the result, like in pharmaceuticals. Is it wearable? Come with new HIV drugs in six months? It used to be years and millions of dollars, tens of millions of dollars. But now it's, you know, it's months, and so it's really changing the way humans live. And certainly the associated industries. They're producing the drugs. >>So it's as you said, I was already being used to reimagine medicine. So many of the high tech jobs openings today are not necessarily in technology there in pharmaceuticals and automotive's. And these and these involved artificial intelligence, their skills in artificial intelligence. What can you tell us about how a eyes having an impact? And that's what I think. >>This is a really good question. What is interesting is that industry she wouldn't think, or digital companies are now actually digital competitors. I'll give you two examples. One is a lot of clients make liquefied natural gas. Now that that is a mucky business. It's full of science, like geology and chemistry and chemical engineering, and they work with these like small refineries. But the questions like, how we gonna get better if you make you know Ellen G. And so what they do is they use a I, and the way they do that is likely have these small refineries. Each piece of equipment has a sensor on it, so there may be 5000 sensors, and each sensor has three or four like bots looking at it, and one might be looking at vibration heat and and what they're doing is they're making predictions. Millions of predictions every every day about you know whether quality is good. The machine's about to have a problem that safety is jeopardise something like that. And so So you've gone from a place where, you know, the best competitors were chemists to the best competitors are actually using machine learning to make the plants work better. You know, another entry. We see this really was brewing. You know, you don't think no one would think brewing is like a digital business like his beer? The Egyptians may be right, like so everyone knows how to do it. So But think about if you make beer like how you're gonna get better and again do what you do is you begin to touch customers more effectively with better digital marketing, you know? Hey, I tow target to understand who your best customers are, how to make offers to them, had a price head of both new product introduction, and even had a formulate new brands of beer that might appeal to different segments of society. So brewing, like they're all about, like ml in the eye. And they really are, like a digital competitive these days, which I think it's interesting, like no one would have thought about that, you know, is they were consuming beer on a Friday with their friends >>and craft brewing is so hot right now. I mean, it is one of those things. As you said, it is attracting new, different kinds of segments of customers. >>Right? And so the questions like if you are a craft brewer like, how do you go find the people that that you want? So what we're doing is we're way have new digital ways to go touch them very personalized offer like, if you like running, you know we can We can give you an offer like fun run followed by a brew. But we know who you are and what you like your friends like to do to get very specific A CZ we like examined the segments of society to do very personal marketing. It's actually fun, like, you know, it gives you things to go Dio we did one event where he looked at cos we we had a a beer tasting with barbecue teach you no instruction. So if you wanna learn how to cook barbecue and also do a beer tasting can get 20 people together and you have a social experience and you you buy more the product. But what's interesting is like, Well, how do you find those people? How do you reach them? How do you identify these of the right folks? That'll actually participate? And that's where a I comes into play. >>So this is fascinating, and you just you just described a number of different industries and companies beer, brewers, liquefied natural gas, pharmaceuticals that are using a I to transform themselves. What is your What do you recommend for the people out there watching and say, I want to do that? How could I get on >>board or what we advise Companies are clients to really get good at three things, and the first is just to do things differently. So you got to go into your core operations and figure out how you can extract more cash and more profit from your existing operations. And so that's like we talked about natural gas, right? Like you could produce it more profitably and effectively, but that's not enough. The next thing you do step to would be to actually grow your core business. Everyone wants to leave to the new right away, but but you're getting all your cash and your legacy businesses and so like like we saw in the brewing history. If you can find new customers, more profitable customers interact with them, create a better digital experience with them, then you'll grow both your top line in your bottom line. But for our from our perspective, the reason you do both of those things is cash. Then make investments into New Net new businesses on DSO. The last thing you do is to do different things, so find in adjacency and grow. And it's important to talk about the role of a I and that because that's the way you develop outcomes with speed, right? Like you're not gonna build a factory and we're gonna build a service or some sort of, you know, information centric offerings. And so what we like to do is talk about like the wise pivot from your old legacy businesses. We generate cash and you make selective investments in the new and how you regulate that is a really important question, because you're too fast and you start the Lexie businesses like to slow, and you're gonna be sort of left out of the new economy. So doing those three things correctly with the right sort of managing processes is what we advise our clients to focus on. >>So I see all of this from the business side. But do you because you're also a consumer? Do you ever see any sort of concerns about privacy and security in the sense of why does anyone need to know if I like to run or I like barbecue with my beer? I mean, how do you How do you sort of think about those things and and talk to clients about those issues >>too? Well, I think, you know, actually, for censure. Ah, large part of our focus is what we call just ethical a eye on. And so it's important to us to actually have offerings that we think that we're comfortable with that are legally comfortable, but also just societally are acceptable. And it's actually like there's a lot of focus in this area, right, how you do it. And there's actually a lot to learn. Like like what we see, for example, is there could be biased in the data which effects the actual algorithm. So a lot of times were the folks in the algorithm, you need to go back to the data and look at that. But it's something we spend a lot of time on. Its important us because we to our consumers and we care about our privacy. >>So when you talk about the wise pivot and the regulation, this is a This is a big question. There's a lot of bills on the table in Washington. It's certainly dominating our national conversation, how we think about regulating thes new emerging technologies that that present a lot of opportunities, but also a lot of risks. So how how are you, how you are you a tech center thinking about regulation and working with regulators on these issues >>way get involved with talking to the government. They seek independent counsel, so we participate when they're seeking guidance and we'll give our offer. So we're a voice at the table. But you know, what I would say is there's a lot of discussion about privacy and ask. But if you look at, like, at a national level, particularly government, I think there used to be more focused just on the parts that are incontrovertibly not problematic with privacy. So I gave you the example of working with liquefied natural gas. Okay, we need better, eh? I'd run our factories better. There's a lot of a I that goes into those kind of problems or supply chain planning. Like, how do I predict demand more effectively, or where should I put my plants? And A. I is the new way supply chain is done right? And so there's There's very few of the consumer centric problems I think, actually is. A society like 90% of the use cases are gonna be in areas where they don't actually influence for privacy and a lot of art. Our time is actually working on those kind of use cases just to make you know the operations of our organization's Maur more effective than more efficient. >>So we talked about the very beginning of this conversation about the companies that are disrupting old industries. Using a lot of these technologies, I mean, is this is a I A case where you need to be using this you need to be using >>you need to be using it. My view, my personal view is that there is going to be no basis of competition in the future, except for a digital. It just is going to be the case. And so all of our clients, you know, they're at some state of maturity and they're all asking the question like, How did I grow up? I don't get more profitable. Like certainly the street. Once more results on DSO if you want to move quickly in the new space, is you. You you you only have 11 choice. Really? And that that is to get really, really, really good at managing in harnessing digital technologies, inclusive of >>a I >>two to compete in a different way. And so I mean, we're seeing really interesting examples were like, you know, like, retailers are getting into health care, right? Like, you see this like you go into Wal Mart and they have our Walgreens. They have, like a doc in the box, right? So we're seeing. But lots of companies that are making physical things that then turn around and use the developing service and what they used to use their know how they take everything they know about, like like something you know about, like healthcare or how to like, you know, offer service is to customers and retail setting, but then they need to do something different. And now how do I get the data and the know how to then offer, like a new differentiated health service? And so to do that, you know, you have a lot. You have a lot of understanding about your customers, but you need to get all the data sources in place. You may need certain help desk. You know you need ways to aggregate it on, and so you probably need a new partnerships that don't have. You probably need toe manage skill sets that you don't have. You may need to get involved with open source communities. You may need to be involved with universities that where they do research, so you'll need a different kind of partnerships to move a speed then companies have probably used in the past. But when they put all those those eco systems together, onda new emphasis on the required skill sets, they can take their legacy knowledge that's probably physically oriented and then create a service that can create. They can monetize their experience with the new service. What what we find usually doesn't work is just a monetized data. If you have a lot of data, it's not usually worth that much. But if you take the data and you create a new service that people care about, then you can monetize your legacy information that that that's what a lot of our class they're trying to do, think they've very mature and now, like Where do you go? And where they go is something may be nearby to their existing business, but it's not. It's not the same legacy business of the path for years. >>I want to take a little deeper on something you brought up about the skills, and there's a real skills gap in Silicon Valley and in companies in this area. How are you working with companies to make sure that they are attracting the right talent pool and retaining those workers once they have? Um, >>well, so this is, I think, one of the most important questions because, like what? What happened with technology in the past? We would put in these like ear piece systems, and that was a big part of our business, like 15 years ago. And once you learned one of those things, that's a P or oracle or, you know, like whatever your skill set was good for 10 years, You probably you were good. You could just, like, go to the work. But today it just just go down to like the convention center. Look at this vast array of like like >>humanity, humanity >>and new technologies. I mean, half these companies didn't even exist, like, five years ago, right? And so you're still set today is probably only good for a year. So I think the first thing you've got to realise is that there's got to be a new focus on actually cultivating talent as a strategy. It's it's the way to compete like people is your product, if you wanna look at that way. But we're doing actually starting very, uh, where we can very early in the process, like much beyond a corporation. So we work with charter schools over kids, we get them into college, we work with universities, we do a lot of internship. So we're trying to start, like, really early on when you ask a question like, what would our recommendation to the government be were actually advising, like, get kids involved in I t. Like earlier and so so we can get that problem resolved but otherwise, once companies work. I think you know you need your own talent strategy. But part of that might be again, like an eco system play like maybe you don't want all of those people and you'd rather sort of borrow on. And so I think, I think figuring out what your eco system is because I think I think in the future like competition will be like my eco system versus your eco system. And that's that is the way I think it's gonna work. And so thinking in an eco system way is, is what most of our clients need to do. >>Well, it's like you said about the old ways of it was a good idea for a good product versus good ideas. And I just keep looking. Thank you so much, John, for coming on the Cuba Really fascinating conversation >>was my pleasure. Thank you so much. >>I'm Rebecca Knight. Stay tuned for more of the cubes. Live coverage of the Accenture Executive Summit coming up in just a little bit

>>Bach from Las Vegas. It's the cube covering AWS executive summit brought to you by extension. >>Welcome back everyone to the cubes live coverage of the Accenture executive summit here at the Venetian in Las Vegas. We are part of AWS reinvent. I'm your host, Rebecca Knight. We are joined by two guests for this segment. We have Prisaad Sanker and he is a senior managing director global ICI lead. Thank you so much for coming on the show. Personal and Larry soccer, global managing director ICI offerings. Thank you so much for coming on Larry. So present to minister with you this week marks the one year anniversary of intelligent cloud infrastructure, a group that you lead at Accenture. Tell us a little bit more about, about, first of all why this group was formed and the journey you've had this year, the highest, the highs and lows. >>Sure, sure. So first, first of all, thank you for having us. Um, so as you mentioned, December 1st will be one year of having formed this group. And the reason we did that was because all of our clients are going through a journey of digital transformation. And it's very important for us to be able to support that journey. So there are different elements that we have to bring together around cloud as well as infrastructure. So we brought together this group, which was actually in different parts of Accenture as one particular group, and we call it intelligent cloud. And infrastructure consists of 30,000 people pretty much in every part of the world supporting all different industries. And this is a way for us to bring together not just cloud computing, but also areas like networking, workplace, digital, other digital businesses that we need to be able to support in order to be able to help our clients through their journey of transformation. >>So this, this group was formed at a time of tremendous change and upheaval and the landscape. Talk to us a little bit about, we hear so much about digital transformation, our company's ready. What's the, let us into the client mindset. >>Yeah. So what happens is, you know, different industries obviously are progressing at different speeds. All of our clients are always worried about being disrupted within their industries, either by an existing competitor all by a completely new competitor that doesn't exist. You know, all the stories about, you know, the big companies that existed and almost vanished overnight. So that's something that keeps CEOs and CIO is awake at night just worrying about that. And so digital transformation is very important for them to be relevant to their client. It's all about bringing new products to their clients and also the speed with which they can actually do that. It's no longer enough to be a fast follower. You have to be an innovator. And cloud is the way that this innovation will happen for our clients. And so it's very important for us to be able to bring our group together. We are able to support that journey for our clients. Leary >>want to bring you into this conversation a little bit. It'd be what will be required for enterprises to make this big transition. I mean, he was talking about how you need to be an innovator. You can't just be a fast follower. >> Well, I mean a lot of times I look at it just given the size, the scale of most of our clients who are really up market, most of them don't have the option to just do a rip and replace and just reinvent themselves completely. So it really is how do I very rapidly modernize and transform my business to take advantage of it? And it really needs to start with your application landscape and end data. So how do I start to look at all the possibilities of the AWS is and start to re-imagine, reinvent Duke, use cloud native technologies. Also a significant amount of their estates are already running in legacy environments. >>We get the mainframe or other environments. How do you digitally decouple those so that you can extract value out of that? And ultimately those decisions of apps and data that are going to drive cloud deployments and architectures and data gravity really becomes the key decision factor to decide where do I place this day? And it was a great example today if you saw Jesse's keynote, he announced Achla where they're actually starting to look at how do I move compute and the processing closer to the actual datasets. So actually inverting the problem and moving closer to the data. And then we see that trend starting to proliferate on the other part of the keynote that was very interesting was the five G announcement. And first you heard about AWS pushing into local zones where they were getting much distributing it out closer to them, reduce latency and really starting to push out. >>So ultimately we seen the whole landscape being transformed by data, these new application architectures and where that data resides and out to traditional world that we've known of hybrid with public and private is really transforming with the Amazon outputs, with the BMCs and stuff like that into much more one about shared and dedicated infrastructure. Then the big, the next real big thing that starts to happen then is this whole explosion of IOT. So as price performance goes down with Moore's law, we can start to see a lot more cost effective IOT solutions. And all of a sudden a world that was very centralized, you know, running up in the, in the world of the Amazons had the public cloud is not going to be much more distributed to a lot more of that compute over time gets moved out there. So we've seen a very rapidly evolving landscape. Apps and data are ultimately driving our cloud clients cloud and infrastructure investments. And they're really just trying to figure out how they can rapidly transform their environments to take advantage of this new landscape. >> So both of you are describing this exceedingly complex environment that is changing dizzying speed. I mean, just even this morning, but the Andy Jassy on stage for three hours with all of the new products and services that AWS has coming out with. What is AWS? What is ICI and Accenture doing to help clients navigate this, this, this, this landscape? Prisaad you know, our >>team is, it's not just enough for infrastructure and cloud to be a horizontal function as it used to be. We feel that, you know, one of the things that Accenture really brings to the table is our industry differentiation. Spent a lot of time analyzing the industries that our clients are in. So we've actually changed the team of ICI to be three different things. The first is to be industry led, so it's no longer good enough to be a horizontal function. We have to understand the needs of each industry and really look at how cloud and infrastructure will support that industry. The second is all about intelligence. And Larry just talked about the proliferation of data, but it's also bringing artificial intelligence, making networks much more smart, you know, really infusing intelligence into everything we do. And the third is the concept of being invisible because our clients are expecting infrastructure to just be there all the time. >>They don't really have to understand how it works, but it has to be there. It's just like going to into room and turning on a switch and you expect electricity to be there. So infrastructure has to be very much like, because it has to be ubiquitous, it has to be just available all the time. So those are the things that we are trying to bring to our clients to make it very specific for and very industry specific for for our clients. And this goes into areas like cloud computing. It goes into 5g edge is going to be a big part of what is going to happen in various industries. And as Larry talked about, IOT devices are going to be just proliferating. It's going to be billions of IOT devices. There's trillions of dollars being spent. In fact, I think the spend on IOT is probably bigger than any other area that I have seen probably in my working lifetime. So it's going to be an exciting time to come for us. >>I mean, we tend to think about artificial intelligence as this futuristic Jetsons kind of thing, but really it's, it's here. And now, Larry, can you talk a little bit about how companies are using AI and having an impact already on their businesses? I mean obviously you see a lot of AI being used for different use cases. We saw some great examples today in Jesse's keynote and we're seeing a use for video analytics for example. And AI to try to figure out predictive maintenance type activity. So there's obviously a lot of business use cases. I think what's interesting from our perspective as well is a lot of the operational use cases. So if you take a look at it with all these new innovations, the rapid pace of change that we're seeing with cloud infrastructure, that application landscape, we've started to rely pretty heavily first on analytics to how do we, how do we figure out what's going on, how do we operate efficiently, how do we make sure we don't put the businesses grist, you know, really pivoting from reactive to proactive and predictive operations. >>We've obviously automated everything as much as we can. I've see AI actually playing a very interesting role in how we optimize these environments over time. So as you get a much more complex environment, much more dynamic, and with containers, Coobernetti's, serverless compute dynamic networks that Prisaad was talking about with software defined networking, AI is going to be the only way we can tune and optimize that over time. So you've obviously got all the business use cases that we see in healthcare that we see in mining, predictive operations and stuff like that. But how we actually use AI internally is going to be critical to how we actually be able to manage cloud and infrastructure and really optimize it over time. >>W what is the client? What's, what's on your minds of your customers right now? We know that only 20% of companies out there have really adopted the cloud. Two thirds have really yet to capture the benefits of the cloud. What are you hearing from them? What are they saying to you? What are their pain points? >>So I think, you know, all of our clients realize that ultimately the cloud is going to be where they will be at. You know, data centers are existing today, but at some point, you know, everybody's going to move to the cloud. Most of our clients have taken the easier workloads and you know, the easy part has already been done. That's the first 20% but 80% of the work still remains. And that's the more complicated work that has to come. So they're looking to us to give them the right solutions. And then there's a variety of other factors to be considered. For example, they have to look at security issues. They have to understand that, you know, there are data privacy aspects to be considered. So really it's a question of matching the right private and public options. And as Larry also mentioned, probably only 30 40% of the data will actually sit in the central cloud. Most of the other data is actually gonna move out to the edge with IOT devices and so on. So data gravity, where does your dataset, where does your compute sit? And Andy talked about it as well today in his keynote address. These are all things that are going to keep evolving and I think that's going to really change the landscape. >>I think they, I think they all see the power of cloud. I mean, which in my mind it's really around the innovation cycles. You know, what you look at the pace that they're innovating with with RDS and Redshift. So they all see that power. I think the biggest thing, they struggle with our skills. And culture because how do you upskill, retrain the organization, everything from the new technologies, how to architect in the new world where it's very ephemeral, dynamic, a serverless world. How do you start to adopt those technologies? How do you operationalize it? How do you go beyond just agile and really do true dev sec ops where you're integrating security and operations built in from the ground floor. And a lot of times he's a cultural change is one of the things we see in cloud and infrastructure operations for example, is how do you take develop operators who used to be eyes on glass, looking at console's turn them into developers where they, you know, they're writing the next analytic algorithms to get to predictive there they're automating automation scripts to improve operations and ultimately tuning the AI engines that optimize it. >>And I think that skills and culture barrier is probably the hardest thing for them to overcome. And how do you just, you can't just go to the cloud, you've got to behave differently. It really have to transform how you use it, how you operate and really transform the organization and culture. >>So these change management challenges, where do you even start? Because as you said, the adopting the technology is almost the easy part, or at least the most straightforward, but really getting everyone on board and really changing people's mindsets and mentalities and dispositions and the way that they collaborate with each other and collaborate cross-functionally. So what have you learned within ICI to, to help companies? And what's your advice? >>I think, I think there are three aspects that you have to get right. In fact, I was talking to one of the CEOs of a very large client of ours, and I think you have to get three things right and you've got to get them aligned and moving at the same time. The first obviously is the technology. So you have to understand what makes sense for you, for your industry. Make the right bets because if you make a wrong decision, then you know you're going to set yourself back. So getting the technology right obviously is important. The second is operating model, making sure that you get that the right operating model in place and kicked off right, right upfront. And the third, like Larry said, is transforming your workforce. So making sure that people are, you know, have all the right skill sets when you actually have the operating model and the technology ready. So it's very important to bring all those three aspects together and a company like Accenture, with our background around consulting, around change management, around technology, we're uniquely positioned understanding our client's industries and really bringing all of those three aspects together so that we're able to position our clients to take that journey forward. >>Larry, in terms of next year's Excenture executive summit, look into your crystal ball. You've already talked about a lot of emerging technologies, IOT, edge computing have talked a lot about AI. Of course. What do you think are going to be the hot topics? Looking ahead this this year in ice with an ICI, you >>touched on earlier, I think everyone's going to be talking about data gravity. As you get these bigger and bigger data sets, it becomes, you know, the network's always going to be the bottleneck. So even with Moore's law, stretching from 18 months to 24 the amount of data we produce, particularly with IOT and edge, is really going to transform things. And even though we've got massive network upgrades like 5g coming along, it will never be enough. I mean, that comes along every 12 years. We're seeing a doubling of price performance who competed? I think data gravity, you can start to see a very different landscape where it used to be public and private and now edge is really going to be obliterated to much more seamless architecture. Then there was a lot of the keynote today, and if you start to take a look at local zones and some of the announcements today, they were ready. Amazon was heading there with green Greengrass so you can have much more seamlessness. And how do I get compute closer to the processing? You're gonna be talking a lot about clustering, clustering, compute around datasets versus the other way around. So I think we're gonna see, and I think that's going to happen pretty fast. Usually a lot of this stuff we've been talking about IOT for years. I do think we're on the tipping point. I think we're about to see exponential growth just as price performance >>comes together. Some of the technologies had gotten gotten there, but, but I think that the whole focus on data and data gravity is what you're going to hear a lot about next year. I can't wait to hear the AWS reinvent band. Do a little pink Floyd or something like that for data gravity. We'll Larry and Prisaad. Thank you so much for coming on the cube. It was a pleasure having you on. Thanks for Brooke. I'm Rebecca night's stay tuned for more of the cubes live coverage of the Accenture executive summit.

>>Live from Orlando, Florida. It's the cube covering Microsoft ignite brought to you by Cohesity. >>Welcome back everyone. You are watching the cube. We are the cube, the ESPN of tech, and we are here at the orange County convention center for Microsoft ignite. I'm your host, Rebecca Knight, sitting alongside of my co host Stu Miniman. We are joined by Dave Cahill. He is the principal PM Bonzai at Microsoft. Thank you so much for coming on the cube. Thanks for having me. It's been a while. Has been by your back. That's right. So you are now, you were the COO of Bonzai. You are now part of Microsoft. There was an acquisition about a year ago. Tell us a little bit about bonsai. It's the AI business system. Got a shout out from Satya on the main stage yesterday. Tell us a little bit about bonsai and then about the transition about now being part of Microsoft. >>Yeah, sure. So the, the big vision for Bonzai from the founders, Mark and Keene was how do you build a set of tools? This makes AI more accessible than to just data scientists. How do you open up, ended up to developers and subject matter experts. And so from day one they've been focusing on building this abstraction layer of platform set of tools. They really enables more than just data scientists access to the low-level mechanics of machine learning, of deeper enforcement learning. Um, everything we've been working on really they've been working on for four years prior to the acquisition was, uh, building out that tool chain. And from my side of the world it was where did we figure out where to point that? Where do we, where are we seeing the strongest traction and adoption for the tools? Early days, uh, from a go to market perspective. And so while they worked on the technology, we really found a pocket of interest, uh, in these real world, often industrial systems. Uh, and so inside Bonzai that's a lot of the work we were doing was taking that platform to market. Um, as part of Bonzai. And then, you know, of course post acquisition, we're doing a lot of the same >>thanks so accessible AI. I love the concept, but what does it really mean? So this is so that someone could be a subject matter expert in an industrial company and be able to still program. Can you explain a little bit, give us an example of, of what bonsai was? >>Yeah, sure. And I mean there's a lot of low level mechanics and machine learning, the algorithms, the toolkits, et cetera that are, that are difficult for just anyone to pick up and start programming. And so the idea here is how can you write an obstruction layer above that? And in this case, it takes a foamer for programming language that allows a developer or subject matter expert to break down the concepts of the problem they're trying to solve in, in, in business terms, right? And so if you think about a wind turbine or a drill or um, a baggage optimization system, it's not the data scientists that intimately understands the behaviors of that system and how it works. It's the subject matter expert that can practically stand next to it and understand or hear that it's starting to fail. Or they know the, the way to turn the knobs most optimally to figure out how to program that system. Now if you just took a of data and threw it at infrastructure, eventually it would figure it out the patterns and how to optimize that thing. But you have a subject matter expert inside the four walls of your organization that readily knows how to solve it like that. And so why not empower them with a, a programming language, really a mechanism to outline the core concepts that you want the AI to learn because they've spent their entire career, uh, trying to figure them out. All right, >>so yeah, Dave, yesterday, Satya Nadella talked a bit about the autonomous systems and if I got it right, he said, we're allowing those engineers to really build systems, become the teachers for what's going on there. So help help frame this a little bit as to where this fits into kind of the broader AI discussion that Microsoft's having with companies today. >>Yeah, I think there's, there's a obviously a massive AI portfolio at, at Microsoft and there's lots of different applications and systems and use cases that are fit for more and more intelligence in the form of AI and machine learning. What we've seen is that an opportunity in the real world and the physical domain that requires a different set of tools and techniques than maybe in the logical, you know, our data centric domains. And oftentimes in the press you see a lot of emphasis on supervised and unsupervised learning and very data centric use cases for the logical world, right? For, for databases or CRM systems or things like that. We believe there's this massive opportunity in the physical world. And when you get into the physical world and these vast practically infinite state spaces, you need different sets of tools and from a machine learning perspective, different sets of techniques. And so I think Microsoft looks at the entire portfolio and says, you need the right tool for the job. Um, as opposed to hammer nailing everything. And that's really the autonomous systems piece is really our effort in real world systems. So >>David, you know, when I'm listening to what you're saying there reminds me of some of the discussions we've been having the last five years or so about the industrial internet. A lot of the OT systems here, which really outside the domain of traditional it or though some of the same challenges that your your team's facing. >>Absolutely. So OT, it's interesting you bring that up. Um, oftentimes the teams that have time inside an organization to pick their head up from their day job to look at new emerging technologies aren't in operations. They're not in the business because they're running the business. And so you have to be able to bridge the gap between central technology, central and innovation teams and those that are actually running the business. And I view OT as kind of the, the kind of mortar between those two bricks oftentimes as the one that has to accept this technology and figure out how to deploy it. And that's just not technically that it works, but also kind of commercially and from a safety risk, trust perspective. So OT really has a, a big role in this. And understanding, not that it just solves the problem technically, but it actually can be deployed, um, in ways that fit within corporate security requirements, data privacy requirements, trust, et cetera. Um, it's not, you know, there's a, there's a, there's a lot of gaps to be bridged there. So I saw this, this, this, like autonomous systems have been projected to grow to more than 800 million in operation by 25. Right? >>That's a big number. So what are you doing within Microsoft to do prepare for that? >>Yeah, so I think I view autonomous systems. It's not a product, it's an endpoint, right? This is like 2000 when VMware came out and said, listen, you're on the journey to the virtual data center. Right? And their customers were in physical data centers trying to go virtual. The journey towards autonomous systems is kind of that we're on that same path. And really it's about providing customers the tools to, but I them along that journey from where they are today to kind of full autonomy, full autonomous systems. And it's a, it's a, it's a maturity, right? You start out, you know, just managing that system, you're maintaining it, then you're, maybe you're, you're optimizing it and you're, then you're controlling it a little bit better, but there's always a human in the loop and then you're at full autonomy. And I think along that path there's lots of different pieces or tools and technologies that we can bring to bear to help them on that journey. Um, technically, commercially. And then also from a safety and trust perspective. And so a lot of the work we're trying to do is build out that tool chain and, and we think Bonzai is a core piece of that actually at the, at the center of what we're trying to do. >>So how, how when you're talking about the human and the aluminum, I'm, I'm imagining a subject matter expert who is working in concert with you developing whatever, whatever tool it is that is going to automate something that they are the subject matter expert, as you said, can fix it like this. Calibrate the buttons and know when a system's about to fail. So how, how trusting are they in terms of, Oh, so this is no longer something I'm going to be doing here. How, how, how do you work with them and, and helping them understand? No, really you can trust this. >>I think it's really about, um, augmenting and scaling the work of the, of the experts and, and oftentimes in every customer engagement we have the subject matter experts are excited because they're literally caudifying their expertise and then figuring out how to scale it. Right? Those experts are frustrated because they are the subject matter expert by definition. They're the problem solver for that problem for everybody in the organization. And so the ability for them to take that expertise in scale, it means more time for them to do what they really want to do, which probably isn't solving problems tactically for everyone. That's not at the expertise level. They are at the executive level. It's about scaling that quality of work so that your expert, you know, your best expert for tuning this turbine can then be scaled across the organization and you're reducing, you know, training costs and other things because you can scale that expertise more effectively. >>Yeah. So Dave, what are some of the big challenges that customers are having? Is it the availability of the expertise and hiring the right people? Uh, you know, we, we've looked at, uh, you know, the, the big data wave, uh, you know, half of those deployments failed for, you know, so many different reasons there. You know, why, why, why, why will this be different? >>Yeah, I mean it's certainly not without challenges. I mean, I think the, one of the things where we run into, you know, data readiness, like I naively thought because we use simulations, we got, we got over the cold start problem that, you know, we don't have data, we'll just use a simulation instead, I think to get around the idea that simulations, there's this idea of a simulation, which is where we train our environment in. And I can kind of go into that in detail, but that's very different than a machine learning ready simulation and having a simulation that runs. It can be parallelized, it can run on Azure that works fast enough to train. These are all impediments to just getting to train these models before you even get to the actual model working in the real world. And so I think the pipeline for training these models is as intense in some cases as you know, data centric training environments. >>Once you get that model trained, it's been about deployment and you have a whole different set of challenges and that's where OT comes into play is starting to figure out, okay, how do we operationalize this model? Is a human in the loop? Is there a a mechanism to to stop the AI and defer to the human right. And we see a maturity model there as well where customers are starting with decision support, which means you know, the AI is not controlling the end system. It is making a recommendation and then a business analyst would then implement that in real time. But walking through what those procedures look like is something that most customers haven't done yet until they're like right at that last step ready to deploy to saying, wait, who's going to watch this? What, what is our safety procedure for deploying a drill, an autonomous drill? It usually doesn't exist in an organization today. >>Yeah, it sounds, it's a little bit different as to, as opposed to, you know, just your regular it operations and you kind of say, here's the five step model. Oh wait, I've always done this. You're, you're attacking some new challenges here. So are they a little bit more likely to move a little bit further and let the autonomy take over? Is that the case? >>Um, I mean, I think so, and it's, it's certainly lines of business, right? This is not, it is there to kind of manage the transition as needed and kind of watch over for security and privacy concerns. Um, I don't, I don't see the hesitation around the autonomous nature of it from the business users. It's, it's people around the periphery, whether that's security or compliance or safety that is most concerned about that. And organizations I think are still trying to get all of those people in the same room and develop policies around that. And oftentimes for better or worse, we're the, we're the forcing function to get them all in the same room and say, okay, what is this going to look like? But, but I, I see the businesses as really driving for the smarter and smarter and increasingly autonomous systems and excited about those pieces because the, the efficiencies to be gained from, from that are so significant. >>And a lot of these use cases I want to ask you about innovation. So this is, you are part of Bonzai and now you are part of Microsoft, which as big tech companies go is, is a rather mature company. We've had some guests on this week who've said that Microsoft actually feels like a lot like a startup. Yeah. I'm interested to hear the, the approach to innovation, the mindset that your new colleagues have and how you are keeping that, that more startup agile approach and that inclination in this big company. Yeah. So I can certainly speak to our experience with Bonzai. It's been pretty neat. I think as having been acquired a few different times by different companies, the way that Microsoft has landed this technology has actually been quite interesting. And we sit within a team within Microsoft research called business AI and business AI's entire charter is to incubate either required or organically developed technologies to the point that they're ready to graduate and scale across the organization. >>Up until that point in time, they're trying to figure out, you know, almost product market fit, but inside a larger organization, leveraging the tools that you know at their disposal that is the broader Microsoft, whether that's the field of the marketing engine or things like that. And then you seeing bonsai be able to take advantage of things like that. The keynote was Satya and, uh, you know, our access and collaboration with the Microsoft field, but we're still in that incubation mode trying to figure out exactly how the technology goes to market. Um, let be continuing to build out and mature the technology and figure out the right home for it. Um, the right partner for it. If it's a business unit or you know, whatever that may be. Um, and I think in that scenario, we're, we're a bit standalone in that regard while we figure this process out. >>So it's, it's, I think oftentimes you see innovation gets stymied when you, you, you force a premature integration of technologies like this and you almost kind of determine their destiny before even knowing really where they're trying to go. And just letting us breathe a little bit for a pointing for, for a period of time, I think allows a better outcome than if you tried to guess ahead of time. Cause at this early stage, you don't know the answer, right? You're still trying to figure out what is the ideal application, what is the ideal target audience? What is the ideal, um, port part of the portfolio where they should sit? Right? Those, those aren't, I think, guessing those up front, even a year ago when the acquisition closed would have been impossible. So that kind of, I don't know that gestation period is, is I think a key, uh, Dave, take us inside some of the conversations you're having at the show. >>Uh, key takeaways you want people to have of, of your group. Uh, out of Microsoft ignite. >> Yes. Right. I think a lot of the conversations are, you know, this, this big vision that is autonomous systems and that really is an end point. And what you really have to do is distill down, you know, where to get started. And that's not the glamorous kind of use cases are the ones that you see in the press or drones. Um, there are autonomous vehicles, right? It's, you know, things that likely fly or we saw on the Jetsons. But the reality is that like where customers are seeing the strongest business opportunity is, is drills, it's turbines, it's air conditioners, it's a extrusion process for some food that you've probably consumed right while you've been here at the conference. Um, that's, and so really kind of, I think dialing customers into surface level use cases that are a fit for deep reinforcement learning is refreshing because a lot of people come at it saying, well, I don't have an autonomous vehicle and I don't have a drone, so I must not be for you. >>And that couldn't be further from the truth. All you need is a control system. Right? If you have any sort of system run by a PID controller or model predictive control, you can optimize that system further with deeper enforcement learning and bonds as a mechanism for making that significant more accessible to your teams. So I think bringing it way back to like, Hey, I saw this big vision on stage, where do I start? It's just really been a bit of a, you know, a search inside their organization for the types of applications that are good fits >> AI. It's not just for the Jetsons anymore. That's right. Great. I'll take it. Dave Cahill. A pleasure having you on. Thank you so much. Yeah, thank you both. It's good to be back. I'm Rebecca Knight for Stu Miniman. Stay tuned for more of the cubes live coverage.

>> from Palo Alto, California It's the Cube covering the em where women transforming technology twenty nineteen Brought to You by V. M. >> Where >> Hi, Lisa Martin on the ground of'Em were in Palo Alto, California, at the fourth Annual Women Transforming Technology Event, or W T. Squared one of my absolute favorite events to cover. And I'm pleased to welcome from one of the sponsors, Jennifer Cohen, the vice president of operations at Toyota Research Institute. Welcome to the Cube. >> Thank you, is that I'm really excited to be here to >> This is such a great event. It's It's morning time. You and I both have a lot of energy coming from even before you walk into the keynote here. Collaboration. The positive spirit, the energy, all of these women talking about and menas well past experiences. It's you walk in, and the energy of Deputy squared is palpable. This is your fourth year. So you being here now at all four >> have, and that's why I keep coming back because the energy here is so good because every year I walk away with tips I can use at work and in my personal life, championing diversity >> and diversity inclusion one of the tracks here, as well as trucks like helping emerging leadership the younger generation, which is key because the attrition rates in technology are so, so high. Tell me a little bit about Tech Toyota Research Institute, Terra What you guys doing? And what made it important for tea Right to sponsor W T Square this year. So Toyota Research >> Institute is a subsidiary of China. We're working on a really exciting things like autonomous driving robotics to help elders, agent place and material sciences. So it's really exciting next level stuff. And it's thrilling to kind of coming to work every day on things that we've been hearing about in the world. And now they're real world things, not just the Jetsons, you know? Yes. >> And so you were here as I mentioned the last three years. But last year, uh, when you were here, you were saying a minute ago. You leave this event every year with really useful kind of we'LL put it into tech terms act personal insights, absolutely clueless about your conversations at Tier I that where they said yes, this is an important event for us to >> sponsor, absolutely so that when I When I came back last year, I had brought a couple of folks from T. Ry to attend the event because I've been attending since the beginning. And as I said, every year I find something that I can bring back to the teams, if not multiple things. Andi weaken our chief diversity officer, Our senior chief of staff is also our diversity inclusion Head. She was very passionate about also supportive event. We're involved with Grace Hopper. We have a women's employee resource group. We're really putting our efforts our time here. They were glad to sponsor. And what was so exciting to walk into that room full of energy today and to see t rise logo up there? It was amazing. >> And I'm sure that for that you mentioned that there's about twelve of your your folks that are here that probably feel it's great that you're not just it's not just a logo. Now, this isn't just branding. This is actual. We're here, You're here. It's a focused, concerted effort. That tiara has an in fact when you join Tiara on the last couple of years, one of the things that inspired you was there's a Chena female leadership here, which is not >> common. No, it's definitely not definite, not common in my career. So one of the reasons I started at here I was because of my manager. Who's her name is Kelly K. She's our EVP and CFO, and she's an amazing leader and so on having the opportunity to go to another company. I wanted to go to one that makes a difference. Like tea, right? Look working to improve the quality of human life. And I wanted to work for somebody that I really respect. It could learn from on. It's been pretty rare in my career tohave women, female leaders to report to. So it's been amazing. And that, I think shows in the role that I have the role, that our chief of staff has Kelly's role and the fact that we're here today. It all flows through. >> So talking. Let's talk about more about flow as VP of operations tell me, like, for example, last year's W T squared what were some of the learnings that you brought back and used in your team, whether it's your management style or even hiring the next generation, >> so a few things that I've learned and not all of them are from last year. I'LL be honest. I'm not. All of them are ones I've just up like at you write. But some of them are things about management. Patty Vargas was here a couple years ago, talking about winds and challenges and really highlighting wins and every team meeting that something that it took back. And it well, it's not necessarily diversity. It's been transformational for me as a leader and really helpful to my team's. Then something. Other things I learned were about on, especially in a few years ago, about saying tohr, I'm not accepting any candidates until you have a diverse candidate pool. That's made a really big difference. And it's hard to say it's hard to stick with because it is hard to find women in technology. However, sticking with that has really helped in my career, hiring folks to have a more diverse team, >> so sticking with it, you've been in a technology for a long time. Tell me a little bit about your career path where you stem from the time you were a kid knowing I love computer science, or was it more zigzag ee >> Ah, little's exactly I was actually history, major say, But I always love technology. Back when we had trs eighties, I love technology. And so I actually started doing that to put myself through school, and I loved it so much. It's what I've stopped what's happened in technology for twenty five years, starting as health desk and systems administrator and moving my way up in my career over time, and every so often they still let me touch something technology and a firewall or some of my best. I keep a little bit of that skill set, but it is quarter who I am, and it's quarter Why I made it. Twenty five years sets >> a milestone. Congratulations, by >> the way, twenty five years in any industry that techno technology industry. I was reading some reports the other day upwards of forty five percent contrition, which is higher than any other industry. What have been some of the secrets to your Obviously I'm imagining persistence, but twenty five years is a long time to stick with anything, but you clearly have a passion for this, but I'm sure it hasn't been easy. Give us a little bit of an understanding and maybe some of those more challenging times you encountered. And how did you just kind of with that internal rules also know I'm I like technology. This is what I wanted. >> So, you know, it's always tough being the only woman in a room that's happened the bulk of my career, although thankfully, not a tear I but it has happened across and actually was the only woman at one company, and I thought it was gonna be a great opportunity. And I love the technology that we were doing. And I was excited Teo to infrastructure in operations and support it. And it was really a bad experience. And it wasn't imagine purposeful, but it was not great. And I was there a very short period time when I realized it wasn't gonna work and I had to take a real hard look. Don't want to keep doing this for a living. I do. I don't want to give up technology. So the right thing was to give up that company, right? And the right thing was t make sure that I stayed and what I loved, but not in the wrong spot. So I think being stubborn and persistent. Not being willing to give up the stuff that I love because the environment wasn't right was a huge part of why I have made it this far. And my daughter is a computer science major, and so I really want for her not to have to go through those things apart. The reason I come here today, what I'm excited about W T two is I want to make sure she has a far easier time of it than I had growing up. >> So was your daughter always >> an interested Or did she? Is she kind of following in Mom's footsteps? She >> wasn't the beginning. Actually, she don't want anything to do with it. And my mom's a c P A. And I don't want to do anything to find >> a way. >> So maybe a cool and her uncle, but never the parent, >> exactly. But as she took coding classes, she actually did Girls who code the seven week immersion camp she found like me that she loves it. So I think she'd like to not compare it to Mom. She doesn't want to hear Mom wars, but she absolutely has that same passion. She she loves to code and see the output and see the changes it can make in her life and potentially others. >> So she'd underground. Currently she is. You should give you anything back on the diversity in her. Yes, is she >> does. And I wish I could give you something inspiring. But unfortunately, she it's for four girls to forty guys. >> Okay, so maybe she has that. Maybe it's a DNA thing where she has that some people might say Stubbornness bad. However, I think you're a great example of how that can be, you know, sort of flipped that coin and look at it is persistence. What keeps her saying, I don't care that I'm for forty? >> I'm not sure. I think e think it's similarly the same thing that it's she's passing around and also she's had everybody's in lovely to her. She's had no mistreatment, so she's definitely loving it, but does notice that she's one of, you know, four out of forty. So but would you >> would you advise? And I, I know not like to say the next generation like your daughter's generation, but it's It's the generation of US women who are in technology now with the attrition rates. If they're in a situation, how would you advise him to recognize the experience that you shared with us? That this is situational? This is an industry wide. I'm not going to make a generalization. What would your advice be to them in terms of making that decision to not not leave? >> So I would say, actually, a mentor of mine told me when I was years ago at a company says, Do you like the work or do you do not like the work? Do you like the people do not like the people. If you don't like the people, you need to go somewhere else. But if you like the war, if you don't like the work here in the wrong industry and I like the work and I always have So I would say if you'd like the work, find the right opportunity and see what change you, Khun, doing the company that you're at. If you're at a company and things aren't right, have you to talk to a man in your manager HR there's ways tto see if you could fix it and if you can't, it's okay. Go somewhere else and do what you love. >> I love that it is. Okay, So one of the things that I'd loved digging on as well as you had gone to Terry's a HR and said, I'm not going to be looking at any candidates until you actually did >> a previous companies. But that is my stance since then, >> you know, >> it's without a diverse school, >> okay? And so what is diverse mean to you? What do you say to them? I know you can find us. >> Yes, Well, I diverse. I don't I don't want to dictate it. I just don't wanna have to, you know, the team's all be the same person. I think Joy is talking up the keynote right now about how important it is that we be careful of bias and that we look at those things and that we are having the people who build the technology be well rounded because this technology that's built here in the Valley goes all over the world has to serve everyone, not just the folks who build it. So I think it's having that same mindset going into it, goingto hiring >> one of and that's so important. And there's also debated. Is it a pipeline problem? I just read Emily changed Look proto Pia and where she kind of documents where that pipeline problem was created? Yes, many, many, many decades ago. And a lot of people would say it's a pipeline problem. But the majorities, the underrepresented, which isn't just women and people of absolutely well who say it's not a piper and problem this. And even if we look at a I, there's so many exciting possibilities. All the autonomous vehicle weren't that tear eyes doing, for example, that will impact everybody and jurors facial recognition? You know, there's probably people in the baby boomer, a generation that have iPhones with facial recognition. But the things that joy wish areas about the bias Easter thes malls being trained on, really, it gives me goose bumps. Didn't mind blowing more. People need to understand. We need better data and more diverse data, not just that to train the models to recognize more agree, but there needs to be lots of different, uh, data sets. So this inclusiveness and I think of diversity, inclusion. One of the things that I thought of when Joy was talking about inclusivity is its inclusivity of different data sets and different technologies, so that ultimately going forward, we can start reducing these biases and this technology that is all for good. >> And I think one of things that we've done is, you know, for our company, we actually had on all hands doing unconscious bias training like we are absolutely committed to making sure that we're thinking about those things on the idea if it's pipeline or if it's or or if it's not, I think it's a combination because the fact is, my daughter is in a class with four girls in forty men, and that's not necessarily, you know, there's no judgment there, but that's the reality. So there's pipeline. But I also think we can demand is hiring managers to have a diverse pool come to us? University isn't just I speak to women because that's what you know. That's my story. But there's not. There's, You know, we had those other kinds of diversity inclusion, you know, we have our G d l G B T. Q plus energy starts a lot of letters to get out at once. We have our women than allies. Yogi Employee resource Scripts were supporting that. It's here, I But I think, you know, we see people out there in the world all trying toe push forward on this. I think if we come out of these conferences and take those actions, that's how overtime it's going to get better. So that's my personal thought. >> I love that last question. What are you looking forward to? Taking away from Debbie U T squared for inclusive innovators as the >> well being of a company doing innovation? I'm really curious to see what's presented today, and I know that we've heard studies that talk about women, run companies and with women on board that profitability and innovation go up. So I think that the more inclusive we are, the better. All of our technology that comes out of the Valley is going to be so I'm looking forward to the whatever thought leadership is here today. That's different from each year that there's something different here that I learned it's not the same thing was Pipelines four years ago, right? Like the last year. It was a lot about women's leadership, so I'm really excited to see what comes out today. >> Well, Jennifer, I thank you so much for sharing some of your time on the kid with me today. And I think a lot of people are going to be able to learn a lot from us. Well, we appreciate your time. Thank you. My pleasure. Lisa Martin on the ground with the Cube. Thanks. For what?

>> From Boston, Massachusetts it's theCUBE covering LiveWorx 18. Brought to you by PTC. >> Welcome back to Boston, everybody. You're watching theCUBE, the leader in live tech coverage. I'm Dave Vellante with Stu Miniman. This is day one of LiveWorx, theCUBE's special coverage of the PTC-sponsored conference. Karen Leavitt is here. She's the CMO of Locus Robotics. Karen, welcome to theCUBE. Thanks so much for coming on. >> Thanks for having me, Dave. >> So, tell us about Locus Robotics. Most people in our audience might not be familiar with it. >> No, I think when most people think about robots they either think about things that clean their house or something from The Jetsons. But these days robots are really anything that can autonomously fulfill the needs of the job requirements. In our case we build robots that work in e-commerce warehouses. Anybody whose ever purchased anything online realizes that the magic is you click the button, you pay for it, and then two days later it magically shows up at your door. >> How'd that happen? >> Yeah, exactly. Well the magic is the raw materials of most of those goods that you get at your door is human labor. They're actually people who walk through warehouses 10, 15 miles a day pushing glorified shopping carts picking items off of shelves in order to put into a box to ship to you. Well e-commerce has been growing like wildfire and we have actually a labor shortage in this country and so what we need to do is we need to figure out ways of automating the warehouse so that the merchandise can get picked efficiently, cost-effectively, and get it out to the consumers. So let's summarize. The big drivers in the business, the demand drivers you're talking about e-commerce spurring the need for automation, lack of labor, >> Yep. >> or a lot of sort of low level tasks that can be replaced by machines. Is that right? Maybe talk about that a little bit. >> Well, you know there are a few different tasks involved. Humans are still best at the ones requiring manual dexterity. Actually physically picking an item off of a shelf or out of a bin and putting it into a tote, humans are still the best at that. But robots are really the best at transporting merchandise through the warehouse and they not only transport the merchandise, they also transport the instructions. So instead of a worker having to snake her way for 15 miles a day through the warehouse, the worker simply needs a robot at a pick location and a robot displays the information that the worker needs. It shows her a photograph of the item to be picked, tells her the bin location to take it from, then she just does the pick, drops it in, and the robot moves on but it means she can be twice or three times as effective. So we built our robots that are collaborative with humans that make the humans work much better. We created sort of an external bionics, if you will. You know this is like the six million dollar warehouse worker but working with a robot friend. >> And the other trend is there is a labor shortage you're saying >> Absolutely. >> Maybe double click on that a little bit. >> We hear all the time about the labor gap in this country. First of all, we have virtually full employment in this country. When we hear about the labor gap when people say there are six million workers who are unemployed and six million jobs that need to be filled but there's a mismatch. I think a lot of people think well maybe what we need to do is we need to retrain these workers to fill the jobs that need filling and our perspective is maybe we should be doing is training the jobs not to require any training on the part of the humans. So by putting the robots in the warehouse, the robot knows what action needs to be taken, knows what task needs to be performed, and has a really bright, friendly screen to be able to inform the worker. So the entire training is you say to the worker, "Hey whenever you see a robot with it's light turned green, approach the robot and do whatever it tells you to do." And that's the whole training and since we're operating in an industry, e-commerce has a lot of spikes. The last three months of the year, last two months of the year really make up probably 40 percent of all the volume in a typical e-commerce warehouse and so the warehouse operators will often hire temporary workers and those are people that may not be trained on the full layout of the warehouse. But now, with the use of the robots, a warehouse operator can bring in even temporary labor and the training can be minimized to within say 15 minutes so that the robot is really creating an environment where the humans work better and they're more engaged. The work is less cumbersome. They're not having to push or pull a heavy cart and we can use the screen on the robot to really create some gamification to keep that worker engaged and make their job more fun. >> Karen, could you give us a little insight into your business. >> Sure. >> The clients that you sell to, who do you sell to? Are there challenges about them? Do you help them with the kind of the training of the robots aren't going to come put you out of work? >> Absolutely. Sure. The generic form of who our customers are; are warehouse operators, these are large retailers that are operating warehouses from which they do direct shipment to consumers or what are called 3PLs, third-party logistics providers. I'll give you an example. One of customers is DHL. DHL runs thousands of warehouses in which they house other people's products and they store and they ship those good in response to e-commerce orders that come in online. As I said, there are seasonal peaks and valleys to this and everybody is looking to hire workers the same seasonal peaks. So what our robots do is, and actually we've got one back here. Got a robot spinning around. They don't usually spin for a living but they're usually moving through the warehouse. They're effectively self-driving cars that carry things and they have a tablet interface with large text to be able to describe to the workers what's going on. So there's a curious passerby looking at the robot. But the robots integrate with the software that's managing the warehouse. They take the orders as they come in and then the robot will drive through the warehouse to the locations where the merchandise is located and then ask a worker to please grab one of those items, or more than one, and put it into the robot's bin and then the robot will take it to where it needs to go. To packing, it may need to go a gift wrap station or may need to be express shipped. But a company like DHL has found that by using humans alone they get one level of productivity out of their workers and it's typically measured in units picked per hour and with the robots they're getting two and a half to three times that level of productivity. So the worker is still doing the real core part of the task that only the human can do, for now, which is picking the item but the robot's doing everything else: carrying instructions, transporting the merchandise, prioritizing the tasks and actually even directing the workers' motions. >> So for decades in the technology world the innovation of the industry marched to the cadence of Moore's Law. >> Yes. Absolutely >> Metcalf's Law obviously came in so you had these sort of laws that are very predictable Like the sunrise, they come up and you can draw a straight line on a log-log scale. What's the innovation source in this world? >> And there is a Moore's Law and it's being written today for these autonomous vehicles. For the self-driving robots. What we're seeing is the hard tech has really been developed for the automotive industry by and large and we're drafting off of that. So a company our size in this nascent industry of warehouse robotics the price of a robot would be out of reach were it not for the fact that there's been critical mass established in the automotive industry. So they key hard tech items are laser in the form of LIDAR and laser cameras and tactile sensors. So all the things that you would imagine you would equip your self-driving passenger car with the robot gets equipped with and of course this hard tech is advancing along the lines of Moore's Law where it's doubling in functionality or capacity every period and it's dropping in cost. So it gives us the opportunity of either holding the cost steady while continuing to improve the functionality of the robot or holding the functionality steady while dropping the cost and because we offer our robots as a service you're effectively hiring a robot as a worker. There's no capital investment. You don't go and buy this as if you were buying a fork truck, for example. You go and you hire a robot and you pay a monthly salary to the robot and so you're instantly seeing a drop in your operating expense because you've got robots that are able to do the work of a couple of humans and you're paying them a little bit less so that you're able to really make your operating expense that much more predictable and lowering it. >> Presumably with less complaints. >> We were really kind of nervous, honestly, a year and half ago that the workers would revolt but the workers are telling us that they're really enjoying it and so it's also allowing the warehouse operators to retain their workers better because they happier workers. >> So software's the lynchpin as well. You're taking advantage . . . >> It's all software >> . . . of the advances in hard tech and then you've got non-recurring engineering costs and then you've got software-like economics driving the business. >> Yeah, apart from the hard tech robots are probably 90 percent software. They look adorable and they're running around and they have a physical manifestation which is obvious to everyone but it really is all about the software. It's about enabling the robots to move fluidly in an environment that's often very congested and gets more congested at times that are most critical because as the volume rises you need to have more workers, more robots, there's higher volume of product moving through and the robots have to be able to move fluidly through very narrow spaces between people. It's very easy to get a robot to totally avoid a human but in this case because the robots are collaborating with the humans we need them to kind of nuzzle up to you kind of like a service animal and work with you in very close quarters and that's all software. >> So, we've got this demo that I've been mesmerized by, your robot behind us. Do you think that the trend toward robotics will reverse the trend towards offshore manufacturing? Maybe bring some of that back or not? >> So our robots are not manufacturing robots however we sort of play in the same world. We see different robots. One of the interesting characteristics of this display that we're looking at here is you'll notice those robots are hidden behind glass. They're expressly being cordoned off so that they're not working with the humans who are deriving the benefits. [Dave] Much different from what you guys are doing. >> So yeah, ours is really designed to work as closely as you would with a human collaborator, perhaps maybe even more closely. (coughing) Excuse me. What I think we are seeing is we've seen automation again, particularly in large scale manufacturing like the automotive industry, for years as that automation has replaced the need for workers. And of course we now see the automotive industry, even offshore manufacturers, offshore headquartered manufacturers are doing their manufacturing here in the United States and I think because they're able to use this automation to get a more consistent cost of production worldwide. I'm not an expert on the automotive industry but I would imagine that the automation allows them to more consistently predict their costs. >> How large is this market? Can you give us a sense of that? You're probably asked that all the time. You're probably asking yourselves that all the time but when you think about the TAM, it feels like it's enormous. >> Well it's global. Everybody shops. Everybody around the world has e-commerce. It depends on how we slice it, of course. If I just looked at the e-commerce industry today, it's at roughly close to two trillion dollars worldwide in e-commerce top line revenue growing to probably three and a half trillion in just the next four or five years. It's growing about 10 percent a year here in the U.S.. Everybody is seeing the stores disappearing from their malls because people are choosing to buy things online rather than walk into a store to do shopping and we're seeing that trend continue at about ten percent growth every year. So that's growing and then at the same time we can look at it and say in terms of how many robots you need, it's a function of how many humans are required to keep up the pace and the robots sort of represent a fraction of that. The robots are designed to hold payload of varying sizes. Our robots are perfectly-sized for things like consumer packaged goods, clothing, medical devices, things like that. They're not designed to carry large building supplies, for example, but we're seeing robots with different form factors all over. But we're looking at probably a multi-billion dollar market over just the next few years. >> Obviously product costs in e-commerce is huge but the labor costs are quite substantial. >> Labor is the single biggest variable cost. >> So you're attacking a portion of that variable cost. >> Absolutely. >> Be interesting to see as this plays out, sports analogy if I may, what inning are we in here? >> I think we just had the coin-flip. >> Okay, so the national anthem just played. >> Actually, we're a little bit further than that because we have established that they serve their purpose well. We already have 10 customers, four of whom are going on record and saying I'm doubling and tripling my productivity and we expect to see this expanded dramatically over the course of just the next year. I'm seeing this grow at a very rapid pace very similar to the early days of the PC. >> Okay. >> So we've had kickoff, I would say, and the ball is moving downfield. >> And how large are you guys? Headcount-wise. >> Our company is about 70 employees. >> Seven-zero? >> Seven-zero. >> And you're VC-funded presumably? >> We're VC-funded. We're angel-funded. Fairly recently, we took on an additional 25 million dollars about six months ago. >> So that was your A round? >> That was our A round. >> Excellent. Who was in? Who was it? Do you remember offhand or could you tell us? >> Actually, most of the round was taken by a combination of around-the-table folks and a company . . . . . .great I'm going to forget my . . . >> Local VC or East Cost, West Coast? >> No, no. West Coast VC. >> But you guys are a Boston-based company. Just north of Boston, right? >> Yes, just north of Boston. >> Well I guess you're now bi-coastal. Kind of like theCUBE. >> Well, we're international. Our largest customers are DHL, which is obviously a German-domiciled company, and GEODIS, which is a French-domiciled company. So we will be moving offshore very quickly. >> Excellent. Well, Karen, thanks so much for coming to theCUBE. >> Thank you for having me Really interesting story and thanks for bringing your robot here behind us. Really appreciate it. >> Thank you. >> Alright, you're welcome. Keep right there. Stu and I will be back with our next guest right after this short break. You're watching theCUBE live from LiveWorx in Boston. We'll be right back. (electronic music)

>> Voiceover: Live, from Orlando Florida, it's theCUBE, covering ServiceNow Knowledge17, brought to you by ServiceNow. >> Welcome back, this is Day 3 of ServiceNow Knowledge17, and this is theCUBE, the leader in live tech coverage, where we go out to the events and we extract the signal from the noise. My name is Dave Vellante, and my co-host this week has been Jeff Frick. Not only this week, Jeff, but for the last five years, we've been doing ServiceNow Knowledge events, really getting a sense as to what this company is all about, the evolution of the company, the transformation from really early days of IT, help desk, service management, to now just permeating throughout the enterprise. One of the key things, Jeff, that is notable, and that we saw a couple years ago, I think it was three years ago, when they had the first CreatorCon. In fact, actually, in 2013, I think you did a little sidebar, you went out-- >> It was the Hackathon, we went with Allan Leinwand and checked in on the Hackathon. >> The point I want to make is that we work with these events, we come to these events. We see a lot of large company events, And whether it's Oracle or IBM or HPE, even, in the past. Even EMC with its code initative, they are drooling over developers. They can't get enough developer action, and it's like ServiceNow builds this platform, they create, they open it up with this low-code development kit, essentially, throw their glove in the field, and everybody comes to the game. >> Right, right. >> It's just amazing, and so today, Day 3, is about CreatorCon, and it was hosted by Pat Casey, who's the senior vice president of DevOps, and really the closest, I think, to the Fred Luddy DNA. I mean that's really Pat, you know, Fred Luddy's the founder of the company and sort of the icon of ServiceNow, not here, you know? We're entering a new era and it's really underscored culturally by CreatorCon and Pat Casey. You were in there today. What'd you think? >> Was it Fred termed the citizen developer? I can't remember, I'll have to go back and check the tape, because he definitely talked about low code, and I think he may have been the one that said citizen developer. And it's funny, even with CJ Desai, right, when he was thinking about coming over, what was the first thing he did? He downloaded the app, and wanted to create a little app. So everybody here is a developer, and I think, just looking back at some of the interviews yesterday, Donna from Cox Automotive, she built a prototype app. It was her, one business analyst, and an intern to start a whole new perspective, so I think, you know, they're really trying to make everybody a developer. It's a different way to think, and not just the business analyst, then you have to pass it off to development, but using, again, a simple workflow tool, it's still a workflow tool, to let everybody automate processes. And we were just in the CreatorCon. The other piece that really strikes me, and it strikes me every time I look at my phone now, you know, my phone knows I follow the Warriors, and so it just automatically gives me an update. So it's kind of this soft, a push of AI and machine learning into your day-to-day activity without this heavy overlay. And that's really how they do it effectively, and then that's kind of the basis of what they're doing here with integrating the machine learning into the applications to collect the data, build the models, try to take some of the mundane, mind-numbing work off of your plate and get people doing it, real decisions based on the machine giving you better data. >> It's an incredible dynamic to me, Jeff, because it's not like this company has a blank sheet of paper and says, "Okay, let's go after developers." They have this impassioned community of people, and they just keep rolling out new function, and then of course, ServiceNow has some really killer developers, internally, and so they make those people available to inspire and educate other developers, and so, as they say, this platform just permeates throughout the organization. I mean, it's really hard to do platforms. We've seen it so many times, you know, companies saying, "Okay, we're developing a platform," and the platform gets a little traction and it gets bought out, but this company, ServiceNow, really has a foothold here. So 4,500 people at CreatorCon this year, it's up from 2,000 last year, so another example of just super meteoric growth. Pat Casey, I loved, he put up the, you know, he showed a mainframe. It actually looked like a VAX to me, but anyway he put up a mainframe, and then he showed the H-P-U-X, what did he call it, HPUX? And, oh yeah we thought that was better, and then client server, it kind of worked for a while, and then he put up "August of 1995," and of course I was immediately saying, that's Gabe Ryden. >> Right, right. >> And then he showed the NetScape logo, and that really changed the development paradigm. >> Just as a way to, you know, and I'm sure none of us thought of it, it was just kind of web bulletin boards with pictures now, when you saw NetScape back in the day, but really as an application delivery vehicle, when you think of what browsers have become, it's pretty fascinating. I had a friend who was working on Chrome, and they described it as kind of an OS in a browser, and I'm like, who would want an OS in a browser? Well, now we're basically here. It's like the old Sun Ray machine, right? Anytime you log onto your browser, you're basically into everything in your world. Whether it's your phone, your tablet, my computer, your desktop computer. It's pretty fascinating. The other thing that Pat talked about was, you know, these things that we grew up with kind of in our imagination. He talked about flying cars, and then he adjusted it to maybe electronic cars, this vision, and now, you know, electronic cars are here, and Tesla's the highest-selling luxury nameplate out there. But in my old world it was flat TVs. The Jetsons had flat TVs. The concept of a flat TV was completely bizarre, and I remember seeing the first one in Chicago, at the Consumer Electronics show. It was like nine inches, you had to have secret passes to get back to see it, but now look what happened. I can't help but think of a Mar's Law, Dave, and he's Gartner's Trough of Disillusionment. I like a Mar's Law better, which is we overestimate the impact in the short term, but way underestimate the impact in the long term. Look at flat screens now, compared to, well, it didn't even exist now. And that's going to happen in AI, it's going to happen in machine learning, and in a very short period of time, especially with the advances in compute-store, networking, cloud, speed of networks, IOT, it's going to be a phenomenal amount of horsepower driving your interaction with all these various objects. >> Look at even the dot-com, you know, how overhyped that was, when really it was underhyped. >> Jeff: Right, in the long term. >> So, the other thing I loved, we've been talking about data for quite some time, and every time we came to a Knowledge show, we'd say, is there a big data angle here? Eh, well kind of, and it's really now coming into focus what the machine learning and AI and big data angle is, and Pat threw up a really nice infographic. He went back to 1969, he gave some interesting stats that I wasn't aware of. I knew the 2k, the moon landing was done on a computer with 2k of memory, that I knew. What I did not know is that it had two programs: one for docking and one for landing, and there wasn't enough memory on the computer to have both programs, so they had to reprogram the computer after the dock. >> Not even reload, right? They couldn't just put the USB stick into it. >> They had the code, which is kind of cool. So that was 2k, he had an intern download the 1982 census, and it was 182 megabytes. And then the human genome project was 53 gigabytes, which he's right, it wouldn't have fit on your previous iPhone, but it will fit on this one. And then, I didn't know this stat, the spell-checker in all of our phones and the red lines and so forth, the back end of that, that's sitting in the cloud, is four terabytes. So you're seeing this explosion of data. These are just some simple examples. So this company, again, it's not just starting from scratch saying, here's some kind of machine learning tool, apply it. What they're doing is saying, we're going to build this into the platform, take the existing corpus of data that you have, now what is that corpus of data? It's a bunch of incidents, it's a bunch of categories and people and it's going to autocategorize, for example, all these incidents, on an existing corpus of data. That's not how most people are using machine learning today. What many people are talking about is a use case of real time continuous applications and doing machine learning in real time to try to affect an outcome, which means try to get you to buy something, or try to detect fraud, or whatever it is. Some healthcare outcome, even. Although you'd think healthcare could be some more post process, but essentially that's what ServiceNow is doing. They're using a post-process methodology on top of this corpus of data to add instant value that lives inside of the platform. It's very compelling, simple, and practical in my view. >> And that's the part I love the best, Dave, is simple and practical and delivers immediate results. Allen Leinwand, who we'll have on later and we've had on a number of times, made a mention that the other thing that's very different is now the apps are listening in real time, and they're adjusting what they're doing and rejiggering their algorithm based on stuff that's happening in real time. So it's a different way to think about applications. And just a couple of things I wanted to touch on from yesterday, with some of the guests we had, a great reason we love the show is the number of customers we get is so high. And I was just struck by Donna Woodruff from Cox Automotive, how much she understood innately that it's a platform. Yes, she bought some applications, but she really understood the platform component and was able to drive from it. And the other one I just wanted to touch on was Eresh from Vitas Healthcare, and the impact of mobile. All I could think about when he was talking about was delivery service. Where's my truck, I had my fridge fixed the other day, where's the guys he close called me, and then to apply that to something as powerful as the work they're doing around hospice and to enable that nurse to get to one more stop per day. Wow, what an impact, just by getting on mobile. And the funny part, he said, is some of their older nurses, when they saw the mobile device, said, "I'm done, I'm not doing it anymore. I'd rather schlep around 25 pages of case information and then go back and forth to the hub in between every stop." So again it's this combination of all this power, all this coming to bear along the three horses of compute that are now delivering phenomenal transformation to people that are willing to think of things in a slightly different lens. >> Yeah, and when you look at the problems that ServiceNow is solving, they are in the boring but important category. And that's why I think that this company for a long time sort of flew under the radar, and is still misunderstood. I mean, even CJ, who's basically in charge of all the products, when he was first approached by ServiceNow, he's like "Meh, I don't really know." And then he dug into it and said, "Wow." So a lot of people don't understand it. I talked to a lot of people in the software business, software sales, people that just don't understand the power of what this company does, and I would make a prediction, is that like Salesforce before it, and we've been talking about this for years, how these guys are on a collision course, and they'll say "No, no, no" but very clearly, the power of the platform that Salesforce has, for example, and ServiceNow is replicating, in some way is much much different. Because Salesforce has a lot of bulldogs, sorry, we love it, we use it, but my point is, my prediction is that over time this company is going to become a very well-known company because of the impacts that it's having on the business. It's going from boring but important to, you know, fundamental transformation of organizations. And I tell you, CRM, I even put it up there with ERP. I think that what ServiceNow is doing is as big as the ERP trend, potentially bigger when you put in all the IOT stuff and the machine learning capabilities and the like with what is a relatively modern platform. >> Well, we're in an attention game, right? On the consumer side it's about attention. The thing that people have the least amount of anymore is time, so how do you get their attention? Do they spend their time on Facebook, Instagram, Snapchat, watching TV, looking at YouTube videos? Watch your kids. How do they spend those hours of their day? On the work side, what screen are you interacting with in your day? Are you in Salesforce all day? Are you in email all day? Are you in Salesforce all day? Are you in Marketo all day? That's where the competition is going to come. And there's only going to be two or three primary applications in which you engage and get work done, and they're making a hard play to say, "We are the application that we want basically in your face, that you're using to get stuff done all day long." >> One of the things, too, I wonder, you always wonder, is think about blind spots to a company like this. They're on this amazing ascendancy. What could come in and disrupt ServiceNow? And you think about the millenials, there's no question that ServiceNow is on to the new way to work. I call it the new way to work, I don't think they use that term. And the millenials are going to come in, and they don't want to use email. They're going to be much more open to adopting a platform. Now, is that platform going to be something like ServiceNow or is it going to be too boring but important? Are they going to do something more like Facebook? My feeling is this is enterprise, and as we talked about yesterday, is it possible that enterprise could actually begin adopting a lot of these consumer-like interfaces and user experiences and leapfrog in some regards because of the use of AI and the enterprise nature and the security capabilities that a company like this can bring? I don't know, maybe that's a stretch, but the gap between consumer and enterprise has to close. It is closing, and I think it will continue to close. >> I think it's the automation piece, to automate themselves out of their customer base. As more and more things are automated, there's going to be less and less and less people looking at the screen to do fewer tasks in terms of just an in. Blind spots always come where you're not looking, that's what's going to hit them, but certainly as more and more of this mundane stuff can be automated, if they can actually execute their vision so these autocategorization and autorouting and things are getting solved before they get to a customer service agent, happen, then their C-base licenses, but that's why they're trying to find other places to go. Facilities management, HR management, integration on the human connection across multiple applications, and to even these other systems, like we've heard about on the HR side, etc. So, I think that's, as the nature of work changes, what will people be doing with their work, or are they just going to be getting assigned tasks to go execute what the machines can't do? It's going to be interesting to watch it evolve. >> Well, and then coming back to the top of this segment, the developers, and that's really where the innovation occurs. The developer ecosystem here continues to grow. The importance of developers is very well understood. We've seen it previously with companies like Microsoft. We see all the big enterprise companies trying to appeal to the developer community. Certainly Amazon, Google, having great, very strong developer ecosystems, Apple as well, Facebook, and so forth. Enterprise guys continue to struggle, frankly, in that regard, and IBM's done a good job with Bluemix, but it's been a real heavy lift for IBM, HP. We've talked to, from Kadifa to all their software execs, and they just never were able to figure it out. Oracle kind of lost its developer edge, despite the fact that it owns Java now, and it's trying to get that back, whereas, as they say, ServiceNow just says, "Hey, let's have a game," and they throw their glove in the field and boom, everybody shows up. >> Think of the focus of a SaaS software company, or even like an Amazon, AWS, right? Everyone here in the company is working on platforms and derivative products from that platform. They don't have this hardware group, that hardware group, this software group, that software group. It's a single application at the end of the day. Salesforce is a single application at the end of the day, work day, single application at the end of the day. AWS, infrastructure for customers at the end of the day. So I think that gives them a huge advantage in terms of focus, everybody going in the same direction, and ability to execute. >> Everybody talks about platform as a service, and it's really, a lot of people say that whole market's collapsing. It's IaaS+, think Amazon, and it's SaaS-, think Salesforce and ServiceNow. All right, we've got to wrap. Keep it right there, buddy. We'll be back with our next guest at theCUBE, we're live, Day 3 from Knowledge17. We're right back. (upbeat music)

>> Narrator: Live from Austin, Texas it's theCUBE covering South by Southwest 2017 brought to you by Intel. Now here's John Furrier. Welcome back everyone, we are here live inside theCUBE SiliconANGLE Media's flagship program. We go out to events, and extract the signal from the noise, I'm John Furrier, we're here in the Intel AI Lounge for South by Southwest special, three days of coverage, interviews all day, some interviews tomorrow and some super demos and panels with Intel's top AI staff and thought leaders and experts and management. My next guest is Suresh Acharya with JDA Software, I've got it right? Welcome to theCUBE. >> Thank you. >> We were chatting before we were coming on about the IOT in your world, but you had made a comment about you were walking around the convention center-- >> Suresh: Yeah. >> What's it like outside? What's the scene look like out there? >> Well, I mean first of all, it's really fun to be here for South to Southwest, of course, and just walking from the convention center here, there are a lot of places, but you guys have something going on here, long lines, it's just a very, you know, a ... There's a huge buzz if you will. Very exciting. >> People are partying here, they got free beer, free booze-- >> Suresh: It's great! >> If you're watching and you're here at South pie, you definitely want to be at the Intel AI Lounge, one it's cooler, all the cool kids are here-- >> Suresh: That's right. Talking AI which is onstage, it's an AI VR show. You've seen a lot of virtual reality, you've seen a lot of AI. >> Suresh: Uh, huh. >> This speaks to a new interface, a new interface from a virtual augmented reality, but also AI from a data centric world-- >> Suresh: Of course. Yes. >> Your thoughts, cuz this is what you're involved in. >> Sure, let me tell you a little bit more about what I do, just to set the context. JDA we work in the supply chain and those are manufacturing plants into transportation into warehouse into stores. Things that are-- >> Known businesses, known processes. >> Known exactly. But what is now changing dramatically is the fact that a lot of this is being digitized. And not only is data being generated, the smarts, that's where the AI comes in has really helped or will continue to help improve efficiencies. So in your question around what the role of hollow ends or whatever the VR capabilities could be and where the smarts come in, if you will, is what we're trying to do is how do these technologies, how do you use them in the store, how do you use them in the warehouse, so that dynamically you can use the smarts for better efficiency. So that's where the machine learning as well as the VR technology comes together. >> So Suresh talk about the dynamics between data science and math and software, because what's happening is it's a real intersection now of confluence of maths, math and science, data, that's really available, and software. >> Suresh: Yeah. >> This is the power trend. This is the big tailwinds to the marketplace. >> Sure, so I'm a data scientist by training, you know I've always done algorithmic work and I've always worked in an industry where my mathematical models make it into the software. It's just music to my ears that a lot of this is now really, really becoming very, very important. Data science is just a word, there's two pieces. There's a data piece. There's a science piece. We all get trained in school on the science, and what we're finding early on was that data sometimes simply wasn't there. >> John: Yeah. >> But now, there's a lot more data, there's a lot more clean data and you can do a lot more with it. So it's a great time to be in AI, machine learning, and just the broader space of the data side. >> Well databases are changing, you're making more unstructured data available-- >> Suresh: Yes. >> Addressable, okay let's get back to your example of manufacturing in supply chain because I was going to say, boring, but it's never boring, it's business. >> Suresh: Yeah. >> We have a world we live in, an analog world, but you mentioned digitizing. This is not trivial. So I want you to take me through in your opinion and working in the labs of JDA Software, what are the key things for digitizing businesses, because you've got to bolt on senors, you got to have actuators, you got to have all kinds of new potentially hardware-- >> Suresh: Yeah. >> You need more processors. But now you got to connect it to the network, that's the Internet of Things. How hard is it to digitize a business? >> Sure, so it is hard and so this is more of a journey than something that's going to happen over night. Let me walk you through a couple of use cases both upstream to the end, and then the other way around, just so that you see the value and how complex, but yet how much value one can add. As you know, there are production plants all over the world, so it's quite possible then that there's a vessel that's carrying your product from China to Long Beach, California. A lot of times currently there's no visibility around when that ship will ever make it to Long Beach. But with sensors, with real-time tracking of all these vessels, we're now able to say that rather than it arriving in Long Beach on the 22nd because of weather reasons, it's now going to arrive on the 25th instead. And how that then drives the downstream supply chain around when should the product make it to the distribution center, when will it make it to the store, and oh, by the way, I might need to make alternate plans now because I don't have the luxury to wait for the three day delay that I am incurring, what are my alternate sources. So that's upstream down to the store. We don't really see it when we go buy something at the store, the fact that this has had such a long journey upstream, is typically shielded from us. >> So it's a ripple effect. >> Ripple effect. >> So the old days was, hey where's my product? Oh, it's on a boat from China, so you didn't know where it's coming from and the guild expression-- >> Suresh: Exactly. >> Maybe it was China or not. >> Suresh: Right. >> But the point was that you had a delay in impact, a disruption-- >> That's right. >> Here you can say, okay contingency policy, software, trigger, hey it's here, get some supply from somewhere else, it could be produce or other goods. >> Suresh: Exactly. >> Am I getting it right? >> You're absolutely right. So that's the kind of upstream down to the consumer, but how about the consumer or the store upstream, right, so sometimes what happens is folks go to the store and then they start to get on social media to say these are awesome products, everyone's got to buy em, these things start to sell off the shelf, if you will, very, very rapidly. And now can you start to detect that social sentiment trend to start to realign your supply chain so that you avoid out of stock. Alternatively, you could have the rewards-- >> Or you could game it like they're doing now. Create scarcity, then make the retail market move. >> There's that as well. >> Supreme is doing it. My kids are buying these things, Supreme, these jackets and backpacks. >> Correct. You can gamify as well. On the other hand, what you can also do is what if you introduce a new product, which you're now finding out is not selling as well as you thought it would. You're not going to continue to push inventory there, you're going to be smart about where you now send those and potentially also manage the manufacturing upstream. >> So it's the classic effect of efficiency opportunities are every. >> Suresh: Exactly. >> Talk how about Intel, what do you think Intel's doing right? Because if you think about about what's powering all this, it's the chips. >> Suresh: Yep. >> It's not just the processor and the PC, it's software end-to-end solutions. >> Suresh: Yeah. >> I was just covering Mobile World Congress two weeks ago, and 5G is bringing potentially a gigabit, I mean not that you need a sensor on a boat or a machine to use a gigabit-- >> Suresh: Sure. >> But still it does create more bandwidth-- >> Suresh: Yeah. >> Cuz you got to connect to the network. (laughs) >> Suresh: Sure. Exactly. (laughs) >> Your data's got to go somewhere. >> So one of the pieces of work that we're doing with Intel is really at the store level to have sensors detect where an object is. You'd be surprised. People sometimes, not sometimes a lot of times what happens is retailers will say that they're out of stock, when it's still in the store, it's just that they don't know where it is. >> John: Yeah. >> To now have sensors to precisely detect whether it's in the back office, whether it's in a fitting room, whether it's somewhere else and really track that inventory real-time to then provide the visibility around inventory is huge. This is the holy grail. You and I may not realize it, but this is the holy grail for a lot of retailers. Because they simply do not know where their inventory is and the work that we're doing around sensors, you know connecting the devices and of course adding the smarts with AI, that's the value. >> I love to hear the word holy grail, great stuff. I want to ask you a question on a personal note. >> Suresh: Yeah. >> Someone who's in labs and you've been in the industry of data science with a math background in retail, in supply chain, you kind of see the big picture. What are the coolest things out there right now, for the folks watching, whether it's a young kid or someone in college or an executive or a developer. Can you highlight some things of the coolest things that people should pay attention to, and what is cool that people aren't paying attention to. >> Yeah, well I think I'm going to be biased when I say just the space of machine learning is actually exploding, but it is. So that's my own heritage as well. To me it's just fascinating to see how things that were very rudimentary have now really caught on. So the area of AI and machine learning has endless potential in my mind. Around a lot of the devices then that actually generate the data that then feeds into it, that space is exploding as well. One of the pieces of work-- >> John: You mean IoT data? >> IoT data. I'd like to give you a specific example of things that are now possible. We are doing research in the space of cognitive robotics. These are not robots that will help automate things or make things faster, these are robots in the stores that will actually interact with you, so they will actually talk to you. You can go up it and say, "Hey, I'm trying to find "these shoes and I can't find them." What it's going to tell you is it's going to bring that immense power of AI to tell you where the products are, it could be in that store and it's going to have someone go fetch it for you, or it's going to tell you, oh it's in another store five miles down the road, would you rather go there to pick it up or it can say I can have it be mailed to your house. So that's in terms of the cognitive robot understanding your emotions that you're angry trying to find something or you're a happy customer and being able to respond that way, but it's also continuously collecting data about you. That it's a male of a certain age group coming into the store at this time, coming out of aisle number 19 looking for this kind of product. This is all pieces of info ... So our goal is even when you're 10 feet away from the robot, it's going to know what questions you're going to ask. >> So robotics is really hot right now, >> Suresh: Right. >> Because this is the interactivity potential, not just a static machine. >> Suresh: Correct. This is more ... >> It's the whole experience. >> We had Dr. Naveen, on earlier, Rao, he said it's like the Jetsons, go clean my room, I mean we're getting there. >> Suresh: We are getting there. >> Almost there. >> We're almost getting there and so ... So the notion that users will use software in a two-dimensional screen manner that we're doing now, that's already changing. So to your point earlier on VR being submersing yourself into your supply chain, which we never have done-- >> John: Yeah. >> Is really where this is going. >> John: Got it. >> So-- >> Suresh, so final question, shoot the arrow forward five years, what does our future look like, what's going to change, what's it going to look like? >> Well, there's a lot of buzz around the autonomous self driving car. In my world it's really the autonomous self-learning supply chain. Think about it, it's going to detect things, it's going to know things, it's going to predict things so much better and also be able to prescribe things dynamically. There's a lot of inefficiencies built into the supply chain that will gradually over time get better and better. So a lot of folks that could be scary, just like driverless car to a lot of folks is scary, but if you really grasp the value of it, where we're going is tremendous in terms of operational efficiencies, in terms of smart, just making our everyday lives so much better. >> Alright Suresh Acharya inside theCUBE, we're here in the Intel AI Lounge, I'm John Furrier with SiliconANGLE Media. We're breaking it down here at South by Southwest where all the buzz is happening virtual reality, artificial intelligence, machine learning is the hottest reality trend right now. Software developers are booming, it's Suresh great, it's the holy grail! This is theCUBE here at the Intel AI Lounge. Back with more coverage after this short break. (upbeat music)

(bright music) >> Narrator: Live from Austin, Texas. It's theCUBE, covering South by Southwest 2017. Brought to you by Intel. Now here's John Furrier. >> We're here live in South by Southwest Austin, Texas. Silicon Angle, theCUBE, our broadcast, we go out and extract the signal from noise. I'm John Furrier, I'm here with Naveene Rao, the vice president general manager of the artificial intelligence solutions group at Intel. Welcome to theCUBE. >> Thank you, yeah. >> So we're here, big crowd here at Intel, Intel AI lounge. Okay, so that's your wheelhouse. You're the general manager of AI solutions. >> Naveene: That's right. >> What is AI? (laughs) I mean-- >> AI has been redefined through time a few times. Today AI means generally applied machine learning. Basically ways to find useful structure in data to do something with. It's a tool, really, more than anything else. >> So obviously AI is a mental model, people can understand kind of what's going on with software. Machine learning and IoT gets kind of in the industry, it's a hot area, but this really is points to a future world where you're seeing software tackling new problems at scale. So cloud computing, what you guys are doing with the chips and software has now created a scale dynamic. Similar to Moore's, but Moore's Law is done for devices. You're starting to see software impact society. So what are some of those game changing impacts that you see and that you're looking at at Intel? >> There are many different thought labors that many of us will characterize as drudgery. For instance, if I'm an insurance company, and I want to assess the risk of 10 million pages of text, I can't do that very easily. I have to have a team of analysts run through, write summaries. These are the kind of problems we can start to attack. So the way I always look at it is what a bulldozer was to physical labor, AI is to data. To thought labor, we can really get through much more of it and use more data to make our decisions better. >> So what are the big game changing things that are going on that people can relate to? Obviously, autonomous vehicles is one that we can all look at and say, "Wow, that's mind blowing." Smart cities is one that you say, "Oh my god, I'm a resident of a community. "Do they have to re-change the roads? "Who writes the software, is there a budget for that?" Smart home, you see Alexa with Amazon, you see Google with their home product. Voice bots, voice interfaces. So the user interface is certainly changing. How is that impacting some of the things that you guys are working on? >> Well, to the user interface changing, I think that has an entire dynamic on how people use tools. Easier something is, the more people use, the more pervasive it becomes, and we start discovering these emergent dynamics. Like an iPod, for instance. Storing music in a digital form, small devices around before the iPod. But when it made it easy to use, that sort of gave rise to the smartphone. So I think we're going to start seeing some really interesting dynamics like that. >> One of the things that I liked about this past week in San Francisco, Google had their big event, their cloud event, and they talked a lot about, and by the way, Intel was on stage with the new Xeon processor, up to 72 cores, amazing compute capabilities, but cloud computing does bring that scale together. But you start thinking about data science has moved into using data, and now you have a tsunami of data, whether it's taking an analog view of the world and having now multiple datasets available. If you can connect the dots, okay, a lot of data, now you have a lot of data plus a lot of datasets, and you have almost unlimited compute capability. That starts to draw in some of the picture a little bit. >> It does, but actually there's one thing missing from what you just described, is that our ability to scale data storage and data collection has outpaced our ability to compute on it. Computing on it typically is some sort of quadratic function, something faster than when your growth on amount of data. And our compute has really not caught up with that, and a lot of that has been more about focus. Computers were really built to automate streams of tasks, and this sort of idea of going highly parallel and distributed, it's something somewhat new. It's been around a lot in academic circles, but the real use case to drive it home and build technologies around it is relatively new. And so we're right now in the midst of transforming computer architecture, and it's something that becomes a data inference machine, not just a way to automate compute tasks, but to actually do data inference and find useful inferences in data. >> And so machine learning is the hottest trend right now that kind of powers AI, but also there's some talk in the leader circles around learning machines. Data learning from engaged data, or however you want to call it, also brings out another question. How do you see that evolving, because do we need to have algorithms to police the algorithms? Who teaches the algorithms? So you bring in this human aspect of it. So how does the machine become a learning machine? Who teaches the machine, is it... (laughs) I mean, it's crazy. >> Let me answer that a little bit with a question. Do you have kids? >> Yes, four. >> Does anyone police you on raising your kids? >> (laughs) Kind of, a little bit, but not much. They complain a lot. >> I would argue that it's not so dissimilar. As a parent, your job is to expose them to the right kind of biases or not biased data as much as possible, like experiences, they're exactly that. I think this idea of shepherding data is extremely important. And we've seen it in solutions that Google has brought out. There are these little unexpected biases, and a lot of those come from just what we have in the data. And AI is no different than a regular intelligence in that way, it's presented with certain data, it learns from that data and its biases are formed that way. There's nothing inherent about the algorithm itself that causes that bias other than the data. >> So you're saying to me that exposing more data is actually probably a good thing? >> It is. Exposing different kinds of data, diverse data. To give you an example from the biological world, children who have never seen people of different races tend to be more, it's something new and unique and they'll tease it out. It's like, oh, that's something different. Whereas children who are raised with people of many diverse face types or whatever are perfectly okay seeing new diverse face types. So it's the same kind of thing in AI, right? It's going to hone in on the trends that are coming, and things that are outliers, we're going to call as such. So having good, balanced datasets, the way we collect that data, the way we sift through it and actually present it to an AI is extremely important. >> So one of the most exciting things that I like, obviously autonomous vehicles, I geek out on because, not that I'm a car head, gear head or car buff, but it just, you look at what it encapsulates technically. 5G overlay, essentially sensors all over the car, you have software powering it, you now have augmented reality, mixed reality coming into it, and you have an interface to consumers and their real world in a car. Some say it's a moving data center, some say it's also a human interface to the world, as they move around in transportation. So it kind of brings out the AI question, and I want to ask you specifically. Intel talks about this a lot in their super demos. What actually is Intel doing with the compute and what are you guys doing to make that accelerate faster and create a good safe environment? Is it just more chips, is it software? Can you explain, take a minute to explain what Intel's doing specifically? >> Intel is uniquely positioned in this space, 'cause it's a great example of a full end to end problem. We have in-car compute, we have software, we have interfaces, we have actuators. That's maybe not Intel's suite. Then we have connectivity, and then we have cloud. Intel is every one of those things, and so we're extremely well positioned to drive this field forward. Now you ask what are we doing in terms of hardware and software, yes, it's all of it. This is a big focus area for Intel now. We see autonomous vehicles as being one of the major ways that people interact with the world, like locality between cars and interaction through social networks and these kinds of things. This is a big focus area, we are working on the in-car compute actively, we're going to lead that, 5G is a huge focus for Intel, as you might've seen in other, Mobile World Congress, other places. And then the data center. And so we own the data center today, and we're going to continue to do that with new technologies and actually enable these solutions, not just from a pure hardware primitives perspective, but from the software-hardware interaction in full stack. >> So for those people who think of Intel as a chip company, obviously you guys abstract away complexities and put it into silicon, I obviously get that. Google Next this week, one thing I was really impressed by was the TensorFlow machine learning algorithms in open source, you guys are optimizing the Xeon processor to offload, not offload, but kind of take on... Is this kind of the paradigm that Intel looks at, that you guys will optimize the highest performance in the chip where possible, and then to let the software be more functional? Is that a guiding principle, is that a one off? >> I would say that Intel is not just a chip company. We make chips, but we're a platform solutions company. So we sell primitives to various levels, and so, in certain cases, yes, we do optimize for software that's out there because that drives adoption of our solutions, of course. But in new areas, like the car for instance, we are driving the whole stack, it's not just the chip, it's the entire package end to end. And so with TensorFlow, definitely. Google is a very strong partner of ours, and we continue to team up on activities like that. >> We are talking with Naveene Rao, vice president general manager Intel's AI solutions. Breaking it down for us. This end to end thing is really interesting to me. So I want to get just double click on that a little bit. It requires a community to do that, right? So it's not just Intel, right? Intel's always had a great rising tide floats all boats kind of concept over the life of the company, but now, more than ever, it's an API world, you see integration points between companies. This becomes an interesting part. Can you talk up to that point about how you guys are enabling partners to work with, and if people want to work with Intel, how do they work, from a developer to whoever? How do you guys view this community aspect? I mean, sure you'd agree with that, right? >> Yeah, absolutely. Working with Intel can take on many different forms. We're very active in the open source community. The Intel Nervana AI solutions are completely open source. We're very happy to enable people in the open source, help them develop their solutions on our hardware, but also, the open source is there to form that community and actually give us feedback on what to build. The next piece is kind of one quick down, if you're actually trying to build an end to end solution, like you're saying, you got a camera. We're not building cameras. But these interfaces are pretty well defined. Generally what we'll do is, we like to select some partners that we think are high value add. And we work with them very closely, and we build stuff that our customers can rely on. Intel stands for quality. We're not going to put Intel branding on something, unless it sort of conforms to some really high standard. And so that's I think a big power here. It doesn't mean we're not going to enable the people that aren't our channel partners or whatever, they're going to have to be enabled through a more of a standard set of interfaces, software or hardware. >> Naveene, I'll ask you, in the final couple minutes we have left, to kind of zoom out and look at the coolness of the industry right now. So you're exposed, your background, we got your PhD, and then you topic wise now heading up the AI solutions. You probably see a lot of stuff. Go down the what's cool to you scene, share with the audience some of the cool things that you can point to that we should pay attention to or even things that are cool that we should be aware that we might not be aware of. What are some of the coolest things that are out there that you could share? >> To share new things, we'll get to that in a second. Things I think are one of my favorites, AlphaGo, I know this is like, maybe it's hackneyed. But as an engineering student in CS in the mid-90s, studying artificial intelligence back then or what we called artificial intelligence, Go was just off the table. That was less than 20 years ago. In that time, it looked like such an insurmountable problem, the brain is doing something so special that we're just not going to figure it out in my lifetime, to actually doing it is incredible. So to me, that represents a lot. So that's a big one. Interesting things that you may not be aware of are other use cases of AI, like we see it in farming. This is something we take for granted. We go to the grocery store, we pick up our food and we're happy, but the reality is, that's a whole economy in and of itself, and scaling it as our population scales is an extremely difficult thing to do. And we're actually interacting with companies that are doing this at multiple levels. One is at the farming level itself, automating things, using AI to determine the state of different props and actually taking action in the field automatically. That's huge, this is back-breaking work. Humans don't necessarily-- >> And it's important too, because people are worried about the farming industry in general. >> Absolutely. And what I love about that use case of like applying AI to farming techniques is that, by doing that, we actually get more consistency and you get better yields. And you're doing it without any additional chemicals, no genetic engineering, nothing like that, you're just applying the same principles we know better. And so I think that's where we see a lot of wonderful things happening. It's a solved problem, but just not at scale. How do I scale this problem up? I can't do that in many instances, like I talked about with the legal documents and trying to come up with a summary. You just can't scale it today. But with these techniques, we can. And so that's what I think is extremely exciting, any interaction there, where we start to see scale-- >> And new stuff, and new stuff? >> New stuff. Well, some of it I can't necessarily talk about. In the robot space, there's a lot happening there. I'm seeing a lot in the startup world right now. We have a convergence of the mechanical part of it becoming cheaper and easier to build with 3D printing, the Maker revolution, all these kind of things happening, which our CEO is really big on. So that, combined with these techniques becoming mature, is going to come up with some really cool stuff. We're going to start seeing The Jetsons kind of thing. It's kind of neat to think about, really. I don't want to clean my room, hey robot, go clean my room. >> John: I'd love that. >> I'd love that too. Make me dinner, maybe like a gourmet dinner, that'd be really awesome. So we're actually getting to a point where there's a line of sight. We're not there yet, I can see it in the next 10 years. >> So the fog is lifting. All right, final question, just more of a personal note. Obviously, you have a neuroscience background, you mentioned that Go is cool. But the humanization factor's coming in. And we mentioned ethics, came up, we don't have time to talk about the ethics role, but as societal changes are happening, with these new impacts of technologies, there's real impact. Whether it's solving diseases and farming, or finding missing children, there's some serious stuff that's really being done. But the human aspects of converging with algorithms and software and scale. Your thoughts on that, how do you see that and how would you, a lot of people are trying to really put this in a framework to try to advance more either sociology thinking, how do I bring sociology into computer science in a way that's relevant. What are some of your thought here? Can you share any color commentary? >> I think it's a very difficult thing to comment on, especially because there are these emergent dynamics. But I think what we'll see is, just as like social network have interfered in some ways and actually helped our interaction with each other, we're going to start seeing that more and more. We can have AIs that are filtering interactions for us. A positive of that is that we can actually understand more about what's going on around in our world, and we're more tightly interconnected. You can sort of think of it as a higher bandwidth communication between all of us. When we're in hunter-gatherer societies, we can only talk to so many people in a day. Now we can actually do more, and so we can gather more information. Bad things are maybe that things become more impersonal, or people have to start doing weird things to stand out in other people's view. There's all these weird interactions-- >> It's kind of like Twitter. (laughs) >> A little bit like Twitter. You can say ridiculous things sometimes to get noticed. We're going to continue to see that, we're already starting to see that at this point. And so I think that's really where the social dynamic happened. It's just how it impacts our day to day communication. >> Talk to Naveene Rao, great conversation here inside the Intel AI lounge. These are the kind of conversations that are going to be on more and more kitchen tables across the world, I'm John Furrier with theCUBE. Be right back with more after this short break. >> Thanks, John. (bright music)

>> live from the Congress Centre in London, England. It's the queue at M i t. And the digital economy The second machine age Brought to you by headlines sponsor M i t. >> I already We're back Dave along with Student of American Nelson and Macca Fear are back here after the day Each of them gave a detailed presentation today related to the book Gentlemen, welcome back to to see you >> Good to see you again I want to start with you >> on a question. That last question That and he got from a woman when you're >> starting with him on a question that was asked of him Yes. And you'LL see why when you find something you like. You dodged the question by the way. Fair for record Hanging out with you guys makes us smarter. Thank you. Hear it? So the question was >> around education She expressed real concern, particularly around education for younger people. I guess by the time they get to secondary education it's too late. You talked about in the book about the three r's we need to read. Obviously we need to write Teo be able to do arithmetic in our head. Sure. What's your take on that on that question. You >> know those basics, our table stakes. I mean, you have to be able to do that kind of stuff. But the real payoff comes from creativity doing something really new and original. The good news is that most people love being creative and original. You look at a kid playing, you know, whether it there two or three years old, that's all that you put some blocks in front of them. They start building, creating things, and our school system is, Andy was saying in his his talkers, questions was, is that many of the schools are almost explicitly designed to tamp that down to get people to conform, get them to all be consistent. Which is exactly what Henry Ford needed for his factories, you know, to work on the assembly line. But now that machines could do that repetitive, consistent kind of work, it's time to let creativity flourish again. And that's when you got to do on top of those basic skills. >> So I have one, and it's pretty clear that that that are Kramer education model. It's really hard for some kids to accept. They just want they want to run around. They want to go express themselves. They wantto poke a world. That's not what that grid full of desks is designed to do. >> We call that a d d. Now I follow. Yeah, I have one >> Montessori kid out of my foot. Really? He's by far the most creative most ano didactic. You're a Montessori Travel Marie, not the story. Have it right? Is that >> Look, I'm not educational research. I am Amon a story kid. I think she got it right. And she was able to demonstrate that she could take kids out of the slums of Bologna who were, at the time considered mentally defective. There's this notion that the reason the poor are poor because they were they were just mentally insufficient. And she could show their learning and their progress. So I completely agree with Eric. We need all of our students need to be able to Teo, accomplish the basics, to read, to write, to do basic math. What Montessori taught me is you can get there via this completely kind of hippie freeform route. And I'm really happy for that education talk. Talk about you and your students. >> Your brainstorm on things that people could do with computers. Can't. >> Yeah, a lot of money >> this and exercise that you do pretty regularly. What's that? How is >> that evolved? A little >> something. We do it more systematically, I almost always doing in at talking over where With Forum. It's a kind of dinner conversation out we can't get away from. So we're hearing a lot. And you know, there's a recurring patterns that emerged, and you heard some of them today around interpersonal skills around creativity. Still, coordination is still physical coordination. What some of these have in common is that their skills that we've evolved over literally, you know, hundreds of thousands or millions of years. And there are billions of neurons devoted to some of these skills. Coordination, vision, interpersonal skills and other skills like arithmetic is something that's really very recent, and we don't have a lot of neurons devoted to that. So it's not surprising the machines can pick up those more recent skills more than the Maurin eight ones. Now overtime, will machines be able to do more of those other skills? I suspect they probably will exactly how long it will take. That's the question for neuroscientists. The AI researchers >> made me make that country think about not just diagnosing a patient but getting them to comply with the treatment regimen. Take your medicine. Eat better. Stop smoking. We know the compliance rates for terrible for demonstrably good ideas. How do we improve them? Is in a technology solution a little bit. Is it an interpersonal solution? Absolutely. I think we need deeply empathetic, deeply capable people to help each other become healthier, become better people. Right Program might come from an algorithm, but that algorithm on the computer that spits it out is going to be lousy at getting most people to comply. Way need human beings for that. So when >> we talking technology space, we've been evangelizing that people need to get rid of what we call the undifferentiated having lifting. And I wonder if there's an opportunity in our personal life, you think about how much time we spend Well, you know, what are we doing for dinner when we're running the kids around? You know, how do I get dressed in the different things that have here their studies sometimes like waste so much brain power, trying to get rid of these things and there's opportunities. Welcome, Jetsons. Actually, no, they >> didn't have these problems that can help us with some of that. I think people should actually help us with over of it. You know, I actually I have a personal trainer and he's one of the last people that I would ever have exclude from my life because he's the guy who could actually help me lead a healthier life. And I play so much value on that. >> I like your metaphor of this is undifferentiated stuff, that really it's not the stuff that makes you great. It's just stuff you have to do. And I remember having a conversation with folks that s AP, and they said, you know, sure would like to brag about this, but we take away a lot of stuff that isn't what differentiates companies in the back office stuff. Getting your basic bookkeeping, accounting, supply chain stuff done and it's interesting. I think we could use the same thing for for personal lives. Let's get rid of that sort of underbrush of necessity stuff so we can focus on the things that are uniquely good at >> alright so way have to run out when I need garbage bags with toilet paper. Honestly, a drone should show up and drop that on my friends. >> So I wonder when I look at the self driving car that you've talked about, will we reach a point that not only do we trust computers in the car, it's cars to drive herself? But we've reached a point where we're just got nothing. Trust humans anymore because self driving cars there just so much safer and better than what we've got is that coming >> in the next twenty years? I personally think so, and the first time is deeply weird and unsettling. I think both of us were a little bit terrified the first time we drove in the Google Autonomous Car and the Google or driving it hit the button and took his hands off the controls. That was a weird moment. I liken it to when I was learning to scuba dive. Very first breath you take underwater is deeply unsettling because you're not supposed to be doing this. After a few breaths, it becomes background. >> But you know, I was I was driving to the airport to come here, and I look in the lanes left to me. There's a woman, you know, texting, and I'd be much you're terrifying if she wasn't driving. If the computer is doing because then we could be more, that's the right way to think about it. I think the time will come and it may not be that far away. We're the norm's shift exactly the other way around and be considered risky to have a human at the wheel and the safety. That thing that the insurance company will want is to have a machine there. You know, I think this is a temporary phase with Newt technology. We become frightened of them. When microwave ovens first came out, they were weird and wonderful. Not most of us think of them is really kind of boring and routine. Same thing is gonna happen with self driving to accidents. Well, that's the story is, that is, But none of them were. Of course, according to the story >> driving, what's clear is that they're safer than the human driver. As of today, they are only going to get safer. We're not evolving that quick, >> but you got the question. Is that self driving, car driven story? Dr. We laughed because we're live in Boston. But your answer was, Will drive started driving, driving, >> you know, eventually, you know, I think it's fair to say that there's a big difference. You know, the first nineteen, ninety five, ninety nine percent of driving is something that's a lot easier. That last one percent or one hundredth of one percent becomes much, much harder. And right now we've had There's a card just last week that drove across the United States, but there were half a dozen times when he had to have a human interviews and particularly unusual situations. And I think because of our norms and expectations, that won't be enough for a self driving car to be safer than humans will need it to be te next paper or something like maybe >> like the just example may be the ultimate combination is a combination of human and self driving car, >> Maybe situation after situation. I think that's going to be the case and I'LL go back to medical diagnosis. I would at least for the short to medium term, I would like to have a pair of human eyes over the treatment plan that the that being completely digital diagnostician spits out. Maybe over time it will be clear that there are no flaws in that. We could go totally digital, but we can combine the two. >> I think in most cases what anything is right, what you brought up. But you know the case of self driving cars in particular, and other situations where humans have to take over for a machine that's failing for someway like aircraft. When the autopilot is doing things right, it turns out that that transition could be very, very rocky and expecting a human to be on call to be able to quickly grasp what's going on in the middle of a crisis of a freak out that's not reasonable isn't necessarily the best time to be swishing over. So there's a there's a fuel. Human factors issued their of how you design it, not just to the human could take over, but you could make a kind of a seamless transition. And that's not easy. >> Okay, so maybe self driving cars, that doesn't happen. But back to the medical example. Maybe Watson will replace Dr Welby, but have not Dr Oz >> interaction or any nurse or somebody who actually gets me to comply again. But also, I do think that Dr Watson can and should take over for people in the developing world who only have access instead of First World medical care. They've got a smartphone. OK, we're going to be able to deliver absolute top shelf world class medical diagnostics to those people fairly quickly. Of course, we should >> do that and then combine it with a coach who gets people to take the prescription when they're supposed to do it, change their eating habits or communities or whatever else you hear your peers are all losing weight. >> Why aren't you? >> I wantto askyou something coming on. Time here has been gracious with your time and your talk. We're very out spoken about. A couple of things I would summarize. It is you lot must Bill Gates and Stephen Hawking. You're paranoid tens. There's no privacy in the Internet, so get over. >> I didn't say there's no privacy. I know working. I think it's important to be clear on this. I think privacy is really important. I do think it's right that we have, and we should have. What I don't want to do is have a bureaucrat defined my privacy rights for me and start telling >> companies what they can and can't do is a result. What >> I'd much prefer instead is to say, Look, if there are things that we know >> Cos we're doing that we do not approve >> of let's deal with that situation as opposed to trying to put the guard rails in place and fence off the different kinds of innovative, strict growth, right? >> I mean, there's two kinds of mistakes you could make. One is, you can let companies do things and you should have regulated them. The other is. You could regulate them preemptively when you really should have let them do things and both kinds of errors or possible. Our sense of looking at what's happening in Jinan is that we've thrived where we allow more permission, listen innovation. We allowed companies to do things and then go back and fix things rather than when we try and locked down the past in the existing processes, so are leaning. In most cases, not every case is to be a little more free, a little more open recognized that there will be mistakes. It's not gonna be that we're perfectly guaranteed is that there is a risk when you walk across the street but go back and fix things at that point rather than preemptively define exactly how things are gonna play. Let >> me give you an example. If Google were to say to me, Hey, Andy, unless you pay us x dollars per month, we're gonna show the world your last fifty Google searches. I would completely pay for that kind of blackmail, right? Certain your search history is incredibly personal reveals a lot about you. Google is not going to do that. It would just it would crater their own business. So trying to trying to fence that kind of stuff often advance makes a lot of sense to me. Then then then relying on this. This sounds a little bit weird, but a combination of for profit companies and people with three choice that that's a really good guarantor of our freedoms and our rights. So you >> guys have a pretty good thing going. It doesn't look like strangle each other anytime soon. But >> how do you How do you decide who >> does one treat by how you operate with reading the book? It's like, Okay, like I think that was Andy because he's talking about Erica. I think that was Erica's. He's talking, >> but I couldn't tell you. I think it's hard for you to reverse engineer because it gets so co mingled over time. And, you know, I gave the example the end of the talk about humans and machines working together synergistically. I think the same thing is true with Indian me out. You may disagree, but I find that we are smarter when we work together so much smarter. Then when we work individually, we go and bring some things on the blackboard. And I had these aha moments that I don't think I would've had just sitting by myself and do I should be that ah ha moment to Andy. To me, it's actually to this Borg of us working together >> and fundamentally, these air bumper sticker things to say. If after working with someone, you become convinced that they respect you and that you could trust them and like Erik says that you're better off together, that you would be individually, it's a complete no brainer to >> keep doing the work together. Well, we're really humbled to be here. You guys are great contact. Everything is free and available. We really believe in that sort of economics. And so thank you very much for having us here. >> Well, it's just a real pleasure. >> All right, Right there, buddy. We'LL be back to wrap up right after this is Q relied from London. My tea.