>>Hello, My name is John John Sheikh from Iran Tous. Welcome to our session on new extensions for doctors CLI as we all know, containers air everywhere. Kubernetes is coming on strong and the CNC F cloud landscape slide has become a marvel to behold its complexities about to surpass that of the photo. Letha dies used to fabricate the old intel to 86 and future generations of the diagram will be built out and up into multiple dimensions using extreme ultraviolet lithography. Meanwhile, complexity is exploding and uncertainty about tools, platform details, processes and the economic viability of our companies in changing and challenging times is also increasing. Mirant ous, as you've already heard today, believes that achieving speed is critical and that speed results from balancing choice with simplicity and security. You've heard about Dr Enterprise Container Cloud, a new framework built on kubernetes, the less you deploy compliant, secure by default. Cooper nineties clusters on any infrastructure, providing a seamless self service capable cloud experience to developers. Get clusters fast, Justus, you need them, Update them seamlessly. Scale them is needed all while keeping workloads running smoothly. And you've heard how Dr Enterprise Container Cloud also provides all the day one and Day two and observe ability, tools, the integration AP ICE and Top Down Security, Identity and Secrets management to run operations efficiently. You've also heard about Lens, an open source i D for kubernetes. Aimed at speeding up the most banding, tightest inner loop of kubernetes application development. Lens beautifully meets the needs of a new class of developers who need to deal with multiple kubernetes clusters. Multiple absent project sufficiently developers who find themselves getting bogged down and seal I only coop CTL work flows and context switches into and out of them. But what about Dr Developers? They're working with the same core technologies all the time. They're accessing many of the same amenities, including Docker, engine Enterprise, Docker, Trusted registry and so on. Sure, their outer loop might be different. For example, they might be orchestrating on swarm. Many companies are our future of Swarm session talks about the ongoing appeal of swarm and Miranda's commitment to maintaining and extending the capabilities of swarm Going forward. Dr Enterprise Container Cloud can, of course, deployed doctor enterprise clusters with 100% swarm orchestration on computes just Aziza Leah's. It can provide kubernetes orchestration or mixed swarming kubernetes clusters. The problem for Dr Dev's is that nobody's given them an easy way to use kubernetes without a learning curve and without getting familiar with new tools and work flows, many of which involved buoys and are somewhat tedious for people who live on the command line and like it that way until now. In a few moments you'll meet my colleagues Chris Price and Laura Powell, who enact a little skit to introduce and demonstrate our new extended docker CLI plug in for kubernetes. That plug in offers seamless new functionality, enabling easy context management between the doctor Command Line and Dr Enterprise Clusters deployed by Dr Enterprise Container Cloud. We hope it will help Dev's work faster, help them adapt decay. TSA's they and their organizations manage platform coexistence or transition. Here's Chris and Laura, or, as we like to call them, developer A and B. >>Have you seen the new release of Docker Enterprise Container Cloud? I'm already finding it easier to manage my collection of UCP clusters. >>I'm glad it's helping you. It's great we can manage multiple clusters, but the user interface is a little bit cumbersome. >>Why is that? >>Well, if I want to use docker cli with a cluster, I need to download a client bundle from UCP and use it to create a contact. I like that. I can see what's going on, but it takes a lot of steps. >>Let me guess. Are these the steps? First you have to navigate to the web. You i for docker Enterprise Container Cloud. You need to enter your user name and password. And since the cluster you want to access is part of the demo project, you need to change projects. Then you have to choose a cluster. So you choose the first demo cluster here. Now you need to visit the U C p u I for that cluster. You can use the link in the top right corner of the page. Is that about right? >>Uh yep. >>And this takes you to the UCP you. I log in page now you can enter your user name and password again, but since you've already signed in with key cloak, you can use that instead. So that's good. Finally, you've made it to the landing page. Now you want to download a client bundle what you can do by visiting your user profile, you'll generate a new bundle called Demo and download it. Now that you have the bundle on your local machine, you can import it to create a doctor context. First, let's take a look at the context already on your machine. I can see you have the default context here. Let's import the bundle and call it demo. If we look at our context again, you can see that the demo context has been created. Now you can use the context and you'll be able to interact with your UCP cluster. Let's take a look to see if any stacks are running in the cluster. I can see you have a stack called my stack >>in >>the default name space running on Kubernetes. We can verify that by checking the UCP you I and there it iss my stack in the default name space running on Kubernetes. Let's try removing the stack just so we could be sure we're dealing with the right cluster and it disappears. As you can see. It's easy to use the Docker cli once you've created a context, but it takes quite a bit of effort to create one in the first place. Imagine? >>Yes. Imagine if you had 10 or 20 or 50 clusters toe work with. It's a management nightmare. >>Haven't you heard of the doctor Enterprise Container Cloud cli Plug in? >>No, >>I think you're going to like it. Let me show you how it works. It's already integrated with the docker cli You start off by setting it up with your container cloud Instance, all you need to get started is the base. You are all of your container cloud Instance and your user name and password. I'll set up my clothes right now. I have to enter my user name and password this one time only. And now I'm all set up. >>But what does it actually dio? >>Well, we can list all of our clusters. And as you can see, I've got the cluster demo one in the demo project and the cluster demo to in the Demo project Taking a look at the web. You I These were the same clusters we're seeing there. >>Let me check. Looks good to me. >>Now we can select one of these clusters, but let's take a look at our context before and after so we can understand how the plug in manages a context for us. As you can see, I just have my default contact stored right now, but I can easily get a context for one of our clusters. Let's try demo to the plug in says it's created a context called Container Cloud for me and it's pointing at the demo to cluster. Let's see what our context look like now and there's the container cloud context ready to go. >>That's great. But are you saying once you've run the plug in the doctor, cli just works with that cluster? >>Sure. Let me show you. I've got a doctor stack right here and it deploys WordPress. Well, the play it to kubernetes for you. Head over to the U C P u I for the cluster so you can verify for yourself. Are you ready? >>Yes. >>First I need to make sure I'm using the context >>and >>then I can deploy. And now we just have to wait for the deployment to complete. It's as easy as ever. >>You weren't lying. Can you deploy the same stack to swarm on my other clusters? >>Of course. And that should also show you how easy it is to switch between clusters. First, let's just confirm that our stack has reported as running. I've got a stack called WordPress demo in the default name space running on Kubernetes to deploy to the other cluster. First I need to select it that updates the container cloud context so I don't even need to switch contexts, since I'm already using that one. If I check again for running stacks, you can see that our WordPress stack is gone. Bring up the UCP you I on your other cluster so you can verify the deployment. >>I'm ready. >>I'll start the deployment now. It should be appearing any moment. >>I see the services starting up. That's great. It seems a lot easier than managing context manually. But how do I know which cluster I'm currently using? >>Well, you could just list your clusters like So do you see how this one has an asterisk next to its name? That means it's the currently selected cluster >>I'm sold. Where can I get the plug in? >>Just go to get hub dot com slash miran tous slash container dash cloud dash cli and follow the instructions

>>From the cube studios in Palo Alto in Boston. It's the cube covering the IBM thing brought to you by IBM. Everybody we're back. This is Dave Vellante the cube, and this is our wall-to-wall coverage, IBM's digital thing experienced for 2020. We're really excited to have Jerry Cuomo on. He's the, uh, vice president of blockchain technologies and an IBM fellow and longtime cube alum. Jerry, good to see you again. Thanks for coming on and wish we were face to face, but yeah, this'll do. Good to see you too. Yes, thanks for having me. So we've been talking a lot of and talking to, I've been running a CEO series a, of course, a lot of the interviews around, uh, IBM think are focused on, on COBIT 19. But I wonder if you could start off by just talking a little bit about, you know, blockchain, why blockchain, why now, especially in the context of this pandemic. >>David's, it's as if we've been working out in the gym, but not knowing why we needed to be fixed. And I know now why we need to be fit. You know, blockchain is coming just in time. Mmm. You know, with the trust factor and the preserving privacy factor. Okay. The way we move forward the world is now becoming more digital than ever people working from home. Um, the reliance and online services is, that's critical. our ability to work as a community accompanies companies. The shared data is critical. you know, blockchain brings a magical ingredient and that's the ingredient of trust, you know, in sharing data. Okay. When, if that data and the sources that are providing that data arc okay. From verified and trusted, we're more likely to use that data and you the, any friction that's caused for fear of trepidation that the data is going to be misused. >>Mmm. It goes start to go away. And when that happens, you speed up an exchange and we need speed. Time is of the essence. So blockchain brings a platform for trusted data exchange while preserving privacy. And that provides a foundation. I can do some amazing things in this time of crisis, right? Yeah. And it's, it's not only trust, it's also expediency and you know, cutting out a lot of the red tape. And I want to talk about some of the applications. You're heavily involved in that in the distributed ledger, a project, you know, one of the early leads on that. Um, talk about some of the ways in which you're flying that distributed a ledger. And let's go into some of the examples. So we're, we're really fortunate to be an early adopter blockchain and, and provider of blockchain technology and kind of the fruit of that. >>Um, as I said, it couldn't happen any sooner where we have, Mmm, I would say over a thousand, alright. Users using IBM blockchain, which is powered by the opensource Hyperledger fabric, I'd say over a hundred of those users, um, have reached a level of production networks. you know, it's been great to see some of the proprietors of those networks now repurpose the networks towards hastening the relief of, uh, and one, a couple of examples that stand out, Dave. Mmm. You've seen what's happening to our supply chain. And then I think we got some rebound happening as we speak, but companies all of a sudden woke up one morning and their supply chains were, I'm exhausted. So suppliers, we're out of key goods and the buyers needed very rapidly to expand. They're, the supplier is in their, in their supply chain. there are laws and regulations about what it takes to onboard a new supplier. >>You want to make sure you're not onboarding bad actors. So in IBM for example, we have over 20,000 suppliers to our business and it takes 30 to 40 days who, uh, validate and verify one of those suppliers. We don't have 30 to 45 days, you know, think about you're a healthcare company or a food company. So working with a partner called Jane yard, uh, co-created a network called trust yourself buyer. And we've been able to repurpose, trust your supplier now or companies that are looking, you know, around Kobe 19 to rapidly okay, expand, you know, their, their supply chain. So if you imagine that taking us 45 days or 40 days to onboard a new supplier, okay. Pick, pick a company in our supply chain, Lenovo, that supplier may very well want to go to Lenovo to and provide services to them. Well guess what, it's going to take 40 days, the onboard to Lenovo. >>But if they're part of the trust or supplier network and they've already onboarded to IBM, they're well on their way. You're being visible to all of these other buyers that are part of the IBM network, like Lenovo and many others. And instead of taking 40 days, maybe it only takes five days. All right. So radically, radically, you know, improving the time it takes them. You know, with companies like Ford making ventilators and masks, it will kind of be able to onboard Ford into, you know, health care, uh, companies. But you know, we want to be able to do it with speed. So trust your supplier is a great use of blockchain. Two, expand a buyer and suppliers. Mmm. Exposure. Mmm. And they expand their network to quickly onboard. And you know, with the trust that you get an exchanging data from blockchain with the Mmm provenance, that Hey, this company information was truly vetted by one of the trusted members of the network. >>There's no fee or trepidation that somehow these records were tampered with or, or misused. So that's one example they have of using blockchain. That's a huge, uh, example that you gave because you're right, there are thousands and thousands of companies that are pivoting to making, like you said, ventilators and masks and yeah, they're moving so fast and there's gotta be a trust involved. On the one hand, they're moving fast to try to save their businesses or you know, in the case of Ford, you help save the, the country or the world. On the other hand, you know, there's risks there. So that, that helps. I want to understand me. Pasa basically is, if I understand it, you can privately share, uh, information on folks that are asymptomatic but might be carriers of covert 19. Am I getting that right on? Okay. So me Pasa starts as a project, uh, from a company called has Sarah and their CEO Jonathan Levy. >>And among other things, Jonathan Levy is an amazing, uh, software developer and he's helped us and the community at large, bill, the Hyperledger fabric, uh, blockchain technology, that's part of IBM. Mmm. The power is IBM blockchain. So Jonathan, I have this idea because w what was happening is there were many, many data sources, you know, from the very popular and well known, uh, Johns Hopkins source. And we have information coming from the weather company. There are other governments, um, putting out data. Jonathan had this, this idea of a verified Mmm. Data hub, right? So how do we kind of bring that information together in a hub where a developer can now to get access to not just one feed, but many feeds knowing that both the data is an a normalized format. So that's easy to consume. And like if you're consuming 10 different data sources, you don't have to think about 10 different ways to interact it. >>No kind of normalizing it through a fewer, like maybe one, but also that we really authentically know that this is the world health organization. This is indeed John Hopkins. So we have that trust. So, okay. Yeah. With me, Pasa being I'm a data hub four, uh, information verified information related to the Kronos virus, really laying a foundation now for a new class of applications that can mash up information to create new insights, perhaps applying Mmm. Artificial intelligence machine learning to really look not just at any one of those, uh, data sources, but now look across data sources, um, and start to make some informed decisions. No, I have to say operate with the lights on, uh, and with certainty that the information is correct. So me Pasa is that foundation and we have a call for code happening that IBM is hosting for developers to come out and okay. Bring their best ideas forward and X for exposing me Pasa as a service to the, in this hackathon so that developers can bring some of their best ideas and kind of help those best ideas come alive with me. Me has a resource. >>That's great. So we've got two, we got the supply chain, we just need to share the Pasa. There's the other one then I think we can all relate to is the secure key authentication, >>which I love. >>Uh, maybe you can explain that and talk about the role that blockchain >>we're launching fits, right. So you know, there is people working from home and digital identity verification. It is key. You know, think about it. You're working remotely, you're using tools like zoom. Um, there's a huge spike in calls and online requests from tele-health or government benefits programs. Yeah. So this is all happening. Everything behind the scenes is, yeah. Around that is, is this user who they say they are, is this doctor who they say they are, et cetera. And there are scams and frauds out there. So working with speed, it means working with certainty. and with the verified me networks set out to do a couple of years ago and the beautiful part is, you know, it's ready to go now for this, for this particular usage it's been using. Mmm. Basically think about it as my identity is my identity and I get to lease out information too different institutions to use it for my benefit, not necessarily just for their benefit. >>So it's almost like digital rights management. Like if you put out a digital piece of art or music, you can control the rights. Who gets to use it? What's the terms and conditions, um, on, on your terms? So verified me, um, allows through a mobile app users to invite institutions who represent them, verify them. No. And so I'll allow my department of motor vehicle and my employer, Mmm. Two to verify me, right? Because I want to go back to work sooner. I want to make sure my work environment, um, I'm making this up. I want to make sure my work environment, the people have been tested and vaccinated, but I don't want to necessarily, you know, kind of abuse people's privacy. Right? So I'll opt in, I'll share that information. I'll get my, my doctor and my, uh, department of motor vehicle to say, yes, this is Gary. >>He's from this address. Yes, he has been vaccinated and now I can kind of onboard to services as much quicker whether that service is going through TSA. Do you get on an airplane badging back into my office or you know, signing on to a, you know, telemedicine, a service or government, a benefits program, et cetera. So verify me is using the self, uh, at the station through a mobile application to help speed up the process of knowing that that is truly you and you truly want this service. Uh, and you are also calling the shots as to that. What happens with your information that, you know, it's not spread all over the interweb it's under your control at all time. Right. So I think it's the best of all worlds. The national Institute for standards and technology looked at, verified me. They're like, Oh my gosh, this is like the perfect storm of goodness for identity. >>They actually appointed, yeah, it has a term, it's called triple blind data exchange. It sounds like a magical act. A triple blind data exchange means the requester. Mmm. Doesn't know who the provider is and less know the requester. Um, allows the provider to know, Mmm, the provider doesn't know who the requester requested, doesn't know who the prior provider is that is double-blind. And then the network provider doesn't know either. Right. But somehow across disformed and that's the magic of blockchain. I'm allowing that to happen and with that we can move forward knowing we're sharing information where it matters without the risk of it leaking out to places we don't want to do. So great application of secure key and verified me. Yeah, I love that. Then the whole concept of being able to control your own data. You hear so much today about, you know, testing and in contact tracing using mobile technology to do that. >>But big privacy concerns. I've always felt like, you know, blockchain for so many applications in healthcare or just being able to, as you say, control your own data. I want to better understand the technology behind this. When I think about blockchain, Mmm. I obviously you don't think about it. Cryptography, you've mentioned developers a number of times. There's software engineering. Yeah. Distributed ledger. Um, I mean there's, there's game theory in the, in the, in the cryptocurrency world, we're not talking about that, but there's the confluence of these technologies coming to them. What's the technology underneath these, these applications? Talking about it there, there is an open source, an organization called Hyperledger. It's part of the Linux foundation. They're the gold standard and open source, openly governed, Mmm. Technology you know, early on in 2018 yep. 18, 26. I mean, we got involved, started contributing code and developers. >>Two Hyperledger fabric, which is the industry's first permissioned blockchain technology. Permission meaning members are accountable. So the network versus Bitcoin where members are anonymous and to pass industry Reggie regulations, you can't be anonymous. You have to be accountable. Um, that's not to say that you can't, okay. Work privately, you know, so you're accountable. But transactions in the network, Mmm. Only gets shared with those that have a need, need to know. So that the foundation is Hyperledger fabric. And IBM has a commercial offering called the IBM blockchain platform that embodies that. That kind of is a commercial distribution of Hyperledger fabric plus a set of advanced tools to make it really easy to work with. The open source. All the networks that I talked about are operating their network across the worldwide IBM public cloud. And so cloud technology lays a really big part of blockchain because blockchains are networks. >>Mmm. You know, our technology, IBM blockchain platform runs really well in the IBM wow. But it also allows you to run anywhere, right? Or like to say where it matters most. So you may have companies, I'm running blockchain nodes in the IBM cloud. You may have others running it on their own premises behind their firewall. You might have others running an Amazon and Microsoft Azure. Right. So we use, um, you may have heard of red hat open shift, the container technology so that we can run Mmm. Parts of a blockchain network, I guess they said where they matter most and you get strengthened a blockchain network based on the diversity of the operators. Because if it was all operated by one operator, there would be a chance maybe that there can be some collusion happening. But now if you could run it know across different geographies across the IBM cloud. >>So almost three networks all run on use this technology or run on the IBM cloud. And Dave, one more thing. If you look at these applications, they're just modern application, you know, their mobile front ends, their web portals and all of that kind of, okay. Okay. The blockchain part of these applications, usually it's only 20% of the overall endeavor that companies are going through. The other 80% it's business as usual. I'm building a modern cloud application. So what we're doing in IBM with, but you know, red hat with OpenShift with our cloud packs, which brings various enterprise software across different disciplines, blends and domains like integration, application, data, security. All of those things come together to fill the other 80% the above and beyond blockchain. So these three companies, okay. You know, 99 plus others are building applications as modern cloud applications that leverage this blockchain technology. So you don't have to be a cryptographer or you know, a distributed database expert. It's all, it's all embodied in this code. Mmm. Available on the IBM cloud, 29 cents a CPU hour. It was approximately the price. So it's quite affordable. And you know, that's what we've delivered. >>Well, the thing about that, that last point about the cloud is it law, it allows organizations, enterprises to experiment very cheaply, uh, and so they can get, uh, an MVP out or a proof of concept out very quickly, very cheaply, and then iterate, uh, extremely quickly. That to me is the real benefit, the cloud era and the pricing model. >>I just mentioned, David, as I said it when I started, you know, it's like we were working out in a gym, but we weren't quite sure. We knew why we were, we were so keen on getting fit. And what I see now is this, you know, blossoming of users who are looking at, you know, a new agreement. We thought we understood digital transformation. Mmm. But there's a whole new nice to be digitized right now. You know, we're probably not going to be jumping on planes and trains, uh, working as, as, as more intimately as we were face to face. So the need for new digital applications that link people together. Uh, w we're seeing so many use cases from, um, trade finance to food safety, to proxy voting for stock, know all of these applications that we're kind of moving along at a normal speed. I've been hyper accelerated, uh, because of the crisis we're in. So blockchain no. Couldn't come any sooner. >>Yeah. You know, I want to ask you, as a technologist, uh, you know, I've learned over the years, there's a lot of ways to skin a cat. Um, could you do the types of things that you're talking about without blockchain? Um, I'm, I'm sure there are ways, but, but why is blockchain sort of the right path, >>Dave? Mmm. You can, you can certainly do things with databases. Mmm. But if you want the trust, it's as simple as this. A database traditionally has a single administrator that sets the rules up for when a transaction comes in. Mmm. What it takes to commit that transaction. And if the rules are met, the transactions committed, um, the database administrator has access who commands like delete and update. So at some level you can never be a hundred percent sure that that data was the data that was intended in there. With a blockchain, there's multiple administrators to the ledger. So the ledger is distributed and shared across multiple administrators. When a transaction is submitted, it is first proposed for those administrators, a process of consent happens. And then, and only then when the majority of the group agrees that it's a valid transaction, is it committed? And when it's committed, it's committed in a way that's cryptographically linked two other transactions in the ledger, I'm making it. >>Mmm tamper-proof right. Or very difficult to tamper with. And unlike databases, blockchains are append only so they don't have update and delete. Okay. All right. So if you really want that center of trusted data that is a tested, you know, that has checks and balances across different organizations, um, blockchain is the key to do it, you know? So could you do it in data with a database? Yes. But you have to trust that central organization. And for many applications, that's just fine. All right. But if we want to move quickly, we really want to share systems of record. Mmm. I hear you. Sharing a system of record, you have regulatory obligations, you can say, Oh, sorry, the record was wrong, but it was put in there by, by this other company. Well, they'll say, well, >>okay, >>nice for the other company, but sorry, you're the one in trouble. So with a blockchain, we have to bring assurances that we can't get into that kind of situation, right? So that shared Mmm. Distributed database that is kind of provides this tamper resistant audit log becomes the Colonel cross. And then with the privacy preservation that you get from encryption and privacy techniques, um, like we have like these things, both channels, um, you can transact, um Hm. And be accountable, but also, Mmm. Only share of transactions with those that have a need to know, right? So you get that level of privacy in there. And that combination of trust and privacy is the secret sauce that makes blockchain unique and quite timely for this. So yeah, check it out. I mean, on the IBM cloud, it's effortless. So to get up and running, you know, building a cloud native application with blockchain and you know, if you're used to doing things, um, on other clouds or back at the home base, we have the IBM blockchain software, which you can deploy. Yeah. Open shift anywhere. So we have what you need in a time of need. >>And as a technologist, again, you're being really, I think, honest and careful about the word tamper. You call it tamper resistant. And if I understand it right, that, I mean, obviously you can fish for somebody's credentials. Yeah. That's, you know, that's one thing. But if I understand that, that more than 50% of the peers in the community, it must agree to tamper in order for the system. You tampered with it. And, and that is the beauty of, of blockchain and the brilliance. Okay. >>Okay. Yeah. And, and, and for, um, performance reasons we've created optimizations. Like you can set a consensus policy up because maybe one transaction it's okay just to have a couple people agree and say, Oh, well, you know, out of the a hundred nodes, Mmm. Three agree, it's good enough. Okay. Other, other policies may be more stringent depending on the nature of the data and the transaction, right? So you can tone, you can kind of tune that in based on the class of transaction. And so it's kind of good and that's how we can get performance levels in the, you know, thousand plus. In fact, IBM and RBC, um, recently did, um, a series of performance analysis because RBC said, Hey, can I use this for some of my bank to bank exchanges and we need to support over a thousand transactions per second. They were able, in their use case, there's support over 3000. Transact for a second. Okay. Mmm. You know, that we were very encouraged by that. I'm glad you clarified that because, so essentially you're saying you can risk adjust the policies if you will. >>That's great to know. Mmm. I could go on forever on this topic. Well, we're unfortunately, Jerry, we're well over our time, but I want to thank you for coming back, planning this important topic. Thrilled. IBM has taken a leadership position here, and I think, you know, to your point, this pandemic is just going to, can accelerate a lot of things and blockchain is, but in my view anyway, one of them. Thank you, Dave. Oh, great questions and I really appreciate it. So everyone out there, um, stay safe. Stay healthy. All right. Thank you Jerry, and thank you for watching everybody. This is Dave Volante for the cube. Our coverage of the IBM think digital 2020 event. We'll be right back. Perfect. The short break.

>>LA from Las Vegas. It's the cube covering AWS. Reinvent 20 nineteens brought to you by Amazon web services and they don't care along with its ecosystem partners. >>Hey, welcome back. Everyone is the cubes live covers in Las Vegas for AWS. Reinvent 2019 it's our seventh year covering Amazon reinvent. They've only had the conference for eight years. We've been documenting history. I'm John Farrow, stupid man. Dave Alante, John Walls, Jeff, Rick, they're all on the other step two sets sponsored by Intel. Want to thank their support without their generous support to our mission. We wouldn't be able to bring this great content. Our next guest to talk about the IOT edge jerk DDoSs column. Perfect. Welcome back. VP of IOT. Well the Greek names. Yeah, I'm half Greek, half German so I can expect, okay. Is smart. Good. So Derek, I gotta ask you, so IOT is hot. Explain quickly your role at AWS because you're not an I-Team specifically define your scope. So my scope is owning all or my team's sculpt is owning old software services and tools that deal with non it equipment. >>So when you go to AWS and look for IOT, all the service that you'll find, that's the scope of my teams and this it group which have all the it stuff and just feels like cars, manufacturing sensors, all of the axioms for the NFL, all that good stuff. So women, you're going to see Edelweiss so I go AWS, amazon.com and then you're fine either. means all of our compute, all of our databases, all of our storage and there's also all of our and Melanie and I and then there's an IOT section and there you find all of the goodness that we do for IOT. You know, it's exciting. Stu and I talking about all week here, the whole cloud native, you take the T out of cloud native, it's cloud naive. You've got the general commercial business and public sector barely getting their act together. They're transforming, they're doing it now. >>He's $1 trillion on a vouch. Trillions of dollars of of change coming. Good up business opportunity. But if they're having trouble transforming, you get this whole new world of industrial edge which requires computing cars manufactured. This is a hot area. So a lot of change happening. What is the most important story people should pay attention to in your area that that's notable for this collision of all this transformation? I think maybe the most notable story that we currently have is a corporation that they do with the VW, which is the largest a car manufacturer. And you were just lucky that via their CIO mountain Huffman being part of Verona for good's keynote, our CTO. So if you haven't seen that, just go and review the keynote of Verner and then as the larger part then he was talking about all of that, what he calls industrial 4.0, this digitization fourth revolution. And Martin did an awesome job explaining what are we doing together with them to build their industrial cloud. Yeah. >>Uh, well, one of the things we've been really watching is the, the extent that Amazon services are starting to push out. Uh, I've been super excited, really looking at some of the growth of there. Your team did a bunch of announcements ahead of the show including the one that caught my eye the most was the IOT green grass sport for Lambda and Docker. Maybe start there and walk us through some of the new pieces that in your org. Okay. >>Maybe for us to understand the offer three type of offerings for our customers. One is device software, which might sound strange that a cloud company actually gives you a software that it's not running on the cloud, but then you're talking about IOT. You need software running on your devices in order to be able to be controlled and communicate with the cloud and we have an offering in that area which is called IOT Greenglass, which is a software runtime that you can install on edge devices like gateways for example, and via announced junior additions to our IOT Greenglass. One is Docker supports, which was very important because up till now green were supporting machine learning at the edge and Lambda, which is our service offering, but many companies now more established enterprises said, you know what, I have legacy applications which I can package. Can I deploy them as well? >>Now you can deploy Docker containers, Lambda functions, and a melody edge all with one goal with green glass at the edge. So that was one of the announcements we did for our device >> software. They're, I want to get your thoughts on an area that we're reporting on and doing a lot of investigation, collecting a lot of data, talking to a lot of people and that's around the industrial IOT or IOT, industrial IOT. And one of our big concerns, I want to get your reaction to this and thoughts is security is of paramount importance because it's not just a DDoS attack or some malware which is causing credit card data or these kinds of theft. You could actually take over machines. People could die this and serious issues around the guarantee. This is the number one conversation. What is the state of the art security posture in your area around software and the edge? >>So at AWS, whether it's IOT or any other workloads, we always say if you have two primary zeros, one is security and one is operations. Because if any company puts their faith in us, if we are down, their business is down and if there would be any security issues, of course all the trust would be broken and we do the exact same approach. Now with IOT, so we built our services with security in mind. For example, when you connect to AWS IOT core, every single individual device needs to have certificates to be identified. If you require that you can encrypt your data, it doesn't even lo you to connect to the cloud without encryption. We have software, as I said, at the edge with Amazon free artists and Greengrass where we support all of the hardware TM modules that you have security postures there. If you have secrets managers, they even have an award winning clout. >>If you're like security tool, which is called IOT device management, but at any given point in time audits but the you configured correctly and does something like detection. If something's going wrong, like when you get your credit card and said, Hey, by the way, have you been in this country? Candy making any purchase? If you figure out if something's going wrong with your device >> and you feel good that it's built in from zero, I mean you've got DNS tax going on. What? I mean you feel comfortable that it's, I mean we believe whatever we build, you can never be 100% sure and security is always evolving. But we believe that we are at the forefront of being, you're always the latest and greatest technology at the hands of our customers. >>Jerks. That's really powerful. Cause I saw one of the other announcements was really taking the Alexa voice service integration, but if I understand it rightly, it pulls that core along. So you know part of me was like, it's like okay Alexa enabled everywhere. That's great. I don't need 700 devices in my house that all have that. But the security piece is going to be needed everywhere. So help us tease that out. >>Maybe, maybe don't understand what we did you ask about the other launches. We also launched something called AVS integration for IUT and AVS stands for Alexa voice services. So if you know Alexa, that's our digital assistant that runs for example an equity devices, but if you want to build a device as a third party, which you can directly talk to media, there's microphones and speakers that is called AVS or Alexa built in devices and if you wanted to build one today you needed to put quite some resources onto this device because it needs to understand you. It needs to have a lot of audio processing. That means there's a lot of memory involved and quite some processing. Now I'm using some technical terms. You need something like a cortex, a CPU which makes this device expensive. So the bill of material is quite elevated and we were working with our Alexa team saying is how can we make this really, really affordable? >>If you found a trick where we said let's offload all of this audio processing to the cloud that you an eSense can build very dumb devices. The only thing that these devices don't need to do is have microphones, have our speaker and what we call a week work detection. They need to wake up and you say, Alexa, echo computer, everything else gets streamed to the cloud. Ptosis sits there and comes back so that you can reduce cost for those devices by at least a factor of half. And we had a great customer on stage as well because if you can make so cheap Alexa built in devices, you can put this into a light switch and iDevices now believe it or not, non-sales light switch. Yup. Which you can now directly talk to, reach, talks back and place your music. They're talking about your role. Again, I want to understand that you are not technical side, your development teams. What are you, what do you do on a daily basis? What's your job? So officially I'm a VP of engineering, so I'm a tech guy, so I love the hoodie. By the way. This is tech. That's because I'm on video. Okay. >>It looks great. So I'm an engineer by Heights and at Amazon we don't have a separation between businesses and product management and engineering. They call it a single thread of leaders that we believe the teams have to own it all. So that means my teams on everything from the conception of their services, the development operations that what be called dev ops and also the business behind. So that means all of the services, whether it's free outro, screen grabs at the edge, but it's IOT core device management and defender or our data services like IOT analytics or your talked about industrial site wise, their health or being conceived by my teams. They have all been developed and they are all operated today so that all customers can use that as it make. What should people >>totally does. Thanks for clarifying. That's awesome. Uh, what should people pay attention to? What should we be reporting on in your area? What are some of the key things that people watching this should pay attention to in this, in your IOT area? What are the most important items and products and services that you're doing? I think >>one of the most important things to understand is be talk just before the interview about this, that a lot of the technical hurdles actually solve that because we have the software on devices, we have the connectivity controlled services, and we have all the analytic services to make sense of the data that you can take actions. You don't need to be an expert in machine learning anymore to do machine learning at AWS. You don't have to be an embedded software developer to get connected devices. You don't have to be a data scientist to understand what your data does. The most interesting part though is there is a cultural aspect of this because in the past you had to ideally most likely in your old company join said, Oh, I would like to connect something, so do I have a purchase acquisition? Can I go to my finance team? Does it install this today? You don't need that anymore. With AWS IOT, the same thing that happens with the cloud and it happens with IOT. So understanding that via very powerful tools for engineers in the company that you can build at any given point in time. I think that's maybe the most, >>and I think the it, I think that whole process of the time it takes, they go to the airport on Thanksgiving, go through TSA and knows all that pre ocracy. And then the other thing too is that the other IOT used to be kind of a closed system self, um, form dot devices. Now you've got with Clough, you've got a lot more range and compatibility. Can you talk about that address, address that issue? Because there might be still legacy out there and no problem. It's data's data, but those are the days come in the cloud. But there's now a new shift happening where it's not just, you know, fully monolithic OT devices if it, so the pasta >>monolithic what's called machine to machine, close systems, IOT is the opposite there. It's where you say now all the devices and connections can be done in between the devices and the cloud. So it's system of systems. And in order to make that happen. For example, when you call it the legacy systems, we also announced on Monday and our IOT day additional features for IOT core that you can migrate legacy systems much easier to the cloud without that you need to update your devices. >>Yeah. Dirk, one of the things I find most interesting about your space as you span between the consumer and the enterprise piece, so I remember a few years ago there was like a hackathon on building skills for Alexa and it got lots of people involved. There was a giveaway of lots of the devices there. You know, we used to talk about the consumerization of it. How is what's happening in the tumor world? You know, how is the enterprise going to take care of take that and transform business as we see IOT permeating everywhere. >>So the capabilities that you need, whether you're going in industrial or in consumer or in the medical or pick your favorite other vertical is in essence the same. You need to connect the devices. You need to ensure that they're secure. We talked about security. You need to make sense of the data, whether you do this in the home with your television or your light switch or your robot, or you do the exact same thing with the most sophisticated robot in the industry. It's the same thing. The good thing about us handling all of those sites is that the scale that we gain with literally hundreds of millions of devices now managed by our service in the backend of course means we will handle all of that scale also in the industry and the security and postures and complexity that we need to handle an industrial also benefits computer, so our consumer side, so you benefit from both sides, very cheap and scale on the one industrial benefit. Very complex. How do you solve that consumable benefit, so it's very fruitful synergies if you like, >>Oh, you guys love to solve problems at Amazon that's going to eat those. Yeah. Derek, thank you so much for coming on and sharing the insights and what you're working on and what's important. Congratulations on all your success. Thank you so much. The threaded leader here. Final question for you. Eighth year of reinvent. It gets bigger every year. Louder. Crazier for parties, more business development more. Exactly. I mean just, it's crazy. Yeah. It's just say work hard, play hard. What is your favorite thing going on here? What's the coolest thing that you've seen? >>I think the coolest thing, and it might sound a little cheeky, is, is the excitement from all of our customers and partners coming here every year. >>PR tells you to say, I'm not about fraud. I mean, you're talking about products. I love my products. I'm still so happy about that. I mean, I can talk to a light switch now. Well, you see the comma car and the other quad had the area that we have yet. It's a very different experience that you can do. Don't talk to your lights, which when you get home your wife will think you're going crazy. I love that. Thank you for coming on. Really appreciate it. Thanks for having cube coverage here. All I'm, we're going to wrap up here. Keep coverage with Derek runs all the IOT for with an AWS exciting new area. It's going to change the game on architecture and solutions are being baked out in real time. We're here breaking out the cube in real time. I'm John. Thanks for watching.

>> live from Boston, Massachusetts. It's the Cube covering AWS reinforce 2019. Brought to you by Amazon Web service is and its ecosystem partners. >> Okay, welcome back. It was two cubes. Live coverage in Boston, Massachusetts, for Amazon Web services reinforces A W s, his first inaugural conference around security, cloud security and all the benefits of security vendors of bringing. We're here with a man who runs the marketplace and more. Dave McCann Cube, alumni vice president of migration, marketplace and control surfaces. That's a new tail you were that you have here since the last time we talked. Lots changed. Give us the update. Welcome to the Cube. >> Great to be back, ma'am. Believe it's seven months of every event. >> Feels like this. Seven years. You know, you've got a lot new things happening. >> We do >> explain. You have new responsibility. You got the marketplace, which we talked about a great product solutions. What else do you have? >> So we've obviously been expanding our service portfolio, right? So either us is launching. New service is all the time. We have a set of service is a road in the migration of software. So I run. No, the immigration Service's team and interesting. We were sitting in Boston, and that's actually headquartered 800 yards down the road. So there's a set of surfaces around the tools to help you as a CEO. Move your applications onto the clothes. Marketplace is obviously where we want you to find short where you need to buy. And then once you get into the topic of governance, we had one product called Service Catalog and reinvent. We announced a new product. That was a preview called Control. Yesterday we went to G A full availability off control, Terror and Control term service catalog together are in the government space, but we're calling them control service is because it's around controlling the access off teams to particular resources. So that's control service. >> What people moving into the cloud and give us a sense of the the workload. I know you see everything but any patterns that you can see a >> lot of patterns and merging and migration, and they are very industry specific. But there are some common patterns, so you know we're doing migrations and frozen companies were weighed and professional service is run by. Todd Weatherby is engaged in hundreds of those migrations. But we also have no over 70 partners that we've certified of migration partners. Migration partners are doing three times as many migrations as our old professional service is. Team are doing so in collection. There's a lot going on there, one of the common patterns. First of all, everybody is moved a Web development other websites have done. They're all running on the AWS know what they're doing is they're modernizing new applications. So the building in Europe or bring enough over moving onto containers. So it was a lie that ran on a sever server on. As they move into the clothes, they're gonna reshape the throw away. Some of the court brief the court up into micro service is on. Deploy out, Let's see on E. C s, which is continuing. There's a lot of application organization, and then on the migration side, we're seeing applications clearly were migrating a lost a lot of ASAP. So the big partners like Deloitte and Accenture are doing a C P migrations, and we've done a lot of ASAP migrations. And then there are other business applications are being moved with particular software vendors. You know there's a company here in Boston called Pegasystems. They do a world leading workflow platform. We've worked with Pagan, and we have migrated loss of paga warped floors in dozens of paying customers up on the float. >> You innovated on the marketplace, which is where people buy so they can contract with software. So now you got moving to the cloud, buying on the cloud, consuming the cloud and then governing it and managing that aspect all under one cohesive unit. That's you. Is that good? >> Yeah, it's a good way to think about it. It's a san of engineering teams with Coleman purpose for the customer. So you know, one of the things we do AWS is we innovate a lot, and then we organize the engineering teams around a common customer needs. So we said, above all of the computer stories service is on. We pay attention to the application layer. We described the application, So if you think of a migration service is says, I've actually got a service called Discovery, I crawl over your servers and I find what you have way. Then what we do is we have a tool that says, Are you gonna bring and move the till. So you have to build a business case. We just bought a company in Canada called TSA Logic. They had a Super Two for building a business case that said, what would this absolutely running with either of us. >> So is the need of the business case. What's the courtney that you guys have focused on? What was that? >> So, interestingly, we run more Windows Server and the clothes when Microsoft. So you actually have to business keys here. So many windows servers are running on print. What does it look like when a run on either the U. S. And T s so logic? Really good, too. And we find our customers using it. That says, Here's your own prim Windows server configuration with an app on run the mortal What would it look like when it runs on AWS? >> But why would you just do that with a spreadsheet? What? What is the T s so logic do that you couldn't do especially >> well? First of all, you want to make a simple too Somebody has to go run a spreadsheet. They've turned it into a tool that a business years Ercan used a sales person you could use on. They've built on top of a database. So it's got a rich set of choices. You are richer than you put in. A special with a U IE is intuitive, and you're gonna learn it in 20 minutes. I'm not gonna have you made up >> this date in their best practice things like that that you can draw a library >> of what's going down, and it keeps the data store of all the ones we've done. So we're turning that into two. Were giving Old Toller solution architect. >> Well, you got a good thing going on with the marketplace. Good to see you wrapping around those needs there. I gotta ask for the marketplace. Just give us the latest stats. How many subscriptions air in the marketplace these days? What's the overall number in the marketplace? It's >> pretty exciting. Way decided just at San Francisco to announce that we now have over 1,000,000 active subscriptions in the marketplace, which is a main boggling number on its own 1,000,000 subscriptions. Ice of Scrape. Within those subscriptions, we've got over 240 foes and active accounts, you know, and the audience doors you could be an enterprise with 100 cases and in an enterprise. What we typically see is that there are seven or eight teams that are buying or using software, so we'll have seven or eight accounts that have the right to subscribe. So you could be a one team and you're in another team you're buying B I tools. You're buying security tools. So those accounts on what? We're announcing the show for the first time ever. Its security is we have over 100,000 security subscriptions. That's a while. That's a big number. Some companies only have 100 customers, and the market, please. Our customers are switched on 100,000 security. So >> many product listings is that roughly it's just security security. At 300 >> there's over 100 listings. Thing is a product with a price okay on a vendor could be Let's see Paolo off networks or crowdstrike or trains or semantic or McAfee or a brand new company like Twist located of Israel. These companies might have one offer or 20 offers, so we have over 800 offers from over 300. Vendors were having new vendors every week. >> That's the next question. How many security app developers are eyes? Do you have over 300? 300? Okay. About 100. Anyway, I heard >> this morning from Gartner that they believe that are over 1000 security vendors. So I'm only 30% done. I got a little work >> tonight. How >> do you >> govern all this stuff? I was a customer. Sort of Make sure that they're in compliance. >> Great question. Steven Smith yesterday was talking about governance once she moved things on the clothes. It's very elastic. You could be running it today, not running a tomato, running it in I d running in Sydney. So it's easy to fire up running everywhere. So how did the governance team of a company nor watch running where you know, you get into tagging, everything has to be tagged. Everything has to have a cord attached to it. And then you do want to control who gets to use what I may have bought about a cuter appliance. But I don't know that I gave you rates to use it, right, so we could have border on behalf of the company. But I need to grant you access. So we launched a couple of years ago. Service catalog is our first governance to and yesterday we went into full release over new to call the control tower. >> Right. What you announced way reinvents >> preview. And yesterday we went to Jenny. What control does is it Natural Owes me to set up a set of accounts. So if you think of it, your development team, you've got David Kay and tested and the product ain't your brand new to the company. I'm a little worried. What, you're going to get up. You >> don't want to give him the keys to the kingdom, >> so I'm actually going to grant you access to a set of resources, and then I'm gonna apply some rules, or what we call God reels is your brand. You you haven't read my manual, you're in the company. So I'm gonna put a set of God reels on you to make sure that you follow our guide length >> Just training. And so is pressing the wrong button, that kind of thing. So I gotta ask you I mean, on the buying side consumption. I heard you say in a talk upstairs on Monday. You have a buyer, buyer, lead, engineering teams and cellar Let engineering, which tells me that you got a lot of innovation going on the marketplace. So the results are obviously they mention the listings. But one of the trends that's here security conference and it was proper is ecosystems importance in monetization. So back in the old days, Channel partners were a big part of the old computer industry. You're essentially going direct with service listings, which is great. How does that help the channel? Is there sinking around channel as a buyer opportunity? How do you How does that work with the market? Is what your thinking around the relationship between the scale of a simplicity and efficiency, the marketplace with the relationships the channel partners may have with their customers? And how do you bridge that together? What's the thinking >> you've overstayed? Been around a long time? >> Uh, so you have 90 Sydney? Well, the channels have been modernizes the nineties. You think about a >> long time. It's really interesting when we conceived Market please candidly. Way didn't put the channel in marketplace, and in retrospect, that was a miss. Our customers are big customers or small customers. Trust some of the resellers. Some resellers operates surely on price. Some resellers bring a lot of knowledge, even the biggest of the global 2000 Fortune 100. They have a prepared advisor. Let's take a company record. You often got 700 security engineers that are blue chip companies in America trusts or they buy the software the adoptive recommends. So mark it, please really didn't accommodate for Let's Pick another One in Europe, it would be computer center. So in the last two years we've dedicated the data separate engineering team were actually opened up. A team in a different city on their sole customer is a reseller. And so we launch this thing called Consulting Partner Private offer. And so now you're Palo. Also, for your trained, you can authorize active or serious or s h I to be the re sailor at this corporation, and they can actually negotiate the price, which is what a role resellers do. They negotiate price in terms, so we've actually true reseller >> write software for fulfillment through the marketplace. Four partners which are now customers to you now so that they could wrap service is because that's something we talk to. People in the Channel number one conversation is we love the cloud. But how do I make money and that is Service is right. They all want to wrap Service's around, So okay, you guys are delivering this. Is that my getting that right? You guys are riding a direct link in tow marketplace for partners, and they could wrap service is around there, >> will you? Seeing two things? First of all, yes. We're lowering the resale of to sell the software for absolutely. So you re sailor, you can quote software you build rebuild for you so that I become the billing partner for a serious or a billing partner for active on active can use marketplace to fulfill clothes software for their customers. Dan Burns to see you about pretty happy. You crossed the line into a second scenario, which is condone burns attached. Service is on. Clearly, that's a use case we hear usually would we hear use cases way end up through feeling that a little, little not a use case I have enabled, but we've done >> what you're working on It. We've had what the customer. How does the reseller get into the marketplace? What kind of requirements are there. Is it? Is it different than some of your other partners, or is it sort of a similar framework? >> They have to become an approved resale or so First of all, they have to be in a peon partner. I mean, we work tightly with a p N e p M screens partners for AWS. So Josh Hoffman's team Terry Wise, his team, whole part of team screen. The reseller we would only work with resellers are screened and approved by the PM Wants the AP en approved way have no set up a dedicated program team. They work with a reseller with trained them what's involved. Ultimately, however, the relationship is between Splunk in a tree sailor, a five and a three sailor named after a tree sailor or Paulo trend or Croat straight. So it's up to the I S V to tail us that hey, computer centers my reseller. I don't control that relationship. A fulfillment agent you crow strike to save resellers, and I simply have to meet that work so that I get the end customer happy. >> So your enabler in that instance, that's really no, I'm >> really an engine, even team for everybody engineer for the Iast way, engineer for the buyer. And they have to engineer for the re. So >> you have your hands in a lot of the action because you're in the middle of all this marketplace and you must do a lot of planning. I gotta ask you the question and this comes up. That kind of put on my learning all the Amazon lingo covering reinvent for eight years and covering all the different events. So you gotta raise the bar, which is an internal. You keep innovating. Andy Jassy always sucks about removing the undifferentiated heavy lifting. So what is the undifferentiated heavy lifting that you're working toe automate for your customers? >> Great questions. Right now there's probably three. We'll see what the buyer friction is, and then we'll talk about what the sale of friction is. The buyer frustration that is, undifferentiated. Heavy lifting is the interestingly, it's the team process around choosing software. So a couple of customers were on stage yesterday right on those big institutions talked about security software. But in order for an institution to buy that software, there are five groups involved. Security director is choosing the vendor, but procurement has to be involved. Andre. No procurement. We can't be left out the bit. So yesterday we did. The integration to Cooper is a procurement system. So that friction is by subscribing marketplace tied round. Match it with appeal because the p O is what goes on the ledgers with the company. A purchase order. So that has to be a match in purchase order for the marketplace subscription. And then engineers don't Tidwell engineers to always remember you didn't tag it. Hi, this finance nowhere being spent. So we're doing work on working service catalog to do more tagging. And so the buyer wants good tagging procurement integrated. So we're working on a walk slow between marketplace service catalog for procurement. >> Tiring. So you've kind of eliminated procurement or are eliminating procurement as a potential blocker, they use another. Actually, we won't be >> apart for leading procurement. VPs want their V piece of engineering to be happy. >> This is legal. Next. Actually, Greek question. We actually tackled >> legal. First, we did something called Enterprise Code tracked and our customer advisory board Two years ago, one of our buyers, one of our customers, said we're gonna be 100 vendors to deploy it. We're not doing 100 tracks. We've only got one lawyer, You know, 6000 engineers and one lawyer. Well, lawyers, good cord is quickly. So we've created a standard contract. It take stain to persuade legal cause at risk. So we've got a whole bunch of corporations adopting enterprise contract, and we're up to over 75 companies adopting enterprise contract. But legal is apartment >> so modernizing the procurement, a key goal >> procurement, legal, security, engineering. And then the next one is I t finance. So if you think of our budgets on their course teams on AWS, everything needs to be can become visible in either of US budgets. And everything has become visible in course exporter. So we have to call the rate tags. >> I heard a stat that 6,000,000 After moving to the cloud in the next 6,000,000 3 to 5 years, security as a focus reinforces not a summit. It's branded as a W s reinforce, just like reinvents. Same kind of five year for security. What's your impression of the show so far? No, you've been highly active speaking, doing briefing started a customer's burn, the midnight oil with partners and customers What's that? What's your vibe of the show? What's your takeaway? What's the most important thing happening here? What's your what's your summary? >> So I always think you get the truth in the booth. Cut to the chase. I made a customer last night from a major media company who we all know who's in Los Angeles. His comment was weeks, either. These expectations wasn't she wanted to come because he goes to reinvent. Why am I coming to Boston in June? Because I'm gonna go to reinvent November on this. The rates of security for a major media company last night basically said, I love the love. The subject matter, right? It's so security centric. He actually ended up bringing a bunch of people from his team on, and he loves the topics in the stations. The other thing he loved was everybody. Here is insecurity, reinvent. There's lots of people from what's the functions, But everybody here is a security professional. So that was the director of security for a media company. He was at an event talking to one of the suppliers, the marketplace. I asked this president of a very well known security vendor and I said. So what's your reaction to reinforce? And he said, Frankly, when you guys told me it was coming, we didn't really want the bother. It's the end of the quarter. It's a busy time of year. It's another event, he said. I am sure glad we came on. He was standing talking to these VP of marketing, saying, We want to bring more people, make sure, So he's overjoyed. His His comment was, when I go to Rio event 50,000 people but only 5% of their own security. I can't reinforce everybody's insecurity >> in Houston in 2020. Any inside US tow? Why Houston? I have no clue what I actually think >> is really smart about the Vineyard, and this is what a customer said Last night. I met a customer from Connecticut who isn't a load to travel far. They don't get to go to reinvent in Vegas. I think what we did when we came to Boston way tapped into all the states that could drive. So there are people here who don't get to go to reinvent. I think when we go to Houston, we're going to get a whole bunch of takes its customers. Yeah, you don't get a flight to Vegas. So I think it's really good for the customer that people who don't get budget to travel >> makes sense on dry kind of a geographic beograd. The world >> if we're expanding the customers that can learn. So from an education point of view, we're just increase the audience that we're teaching. Great, >> Dave. Great to have you on. Thanks for the insights and congratulations on the new responsibility as you get more coz and around marketplace been very successful. 1,000,000 subscriptions. That's good stuff again. They were >> you reinvented and >> a couple of months, Seven days? What? We're excited. I love covering the growth of the clouds. Certainly cloud security of his own conference. Dave McCann, Vice president Marketplace Migration and Control Service is controlled cattle up. How they how you how you move contract and governed applications in the future. All gonna be happening online. Cloud Mr. Q coverage from Boston. They just reinforced. We right back with more after this short break

(uplifting music) >> Hi, I'm Peter Burris and welcome again to another CUBE Conversation from our wonderful studios here in Palo Alto, California. Another great topic to talk about, we've got Robert Schmid, who is the Chief IoT Technologist at Deloitte. Welcome to The Cube, Robert. >> Thanks for having me. >> You also have your own video cast, so why don't we get that out of the way. What is it? >> Yeah, every Friday at 9 AM Pacific I do a show called Coffee Chat with Mr. IoT and Miss Connected. I just actually added a co-host, I thought I needed someone to help me. And we talk about IoT. It's on YouTube, you can find it on the channel, and it's really odd for me, that you're going to ask me the questions and I'm going to have to answer. (laughing) So I'm going to try to eat my own, my own advice here and be short. >> Well you know maybe someday you can have one of the Wikibon folks in your podcast, or video cast, we'd love to do that. >> Yeah that'd be great. >> Alright let's start here though. Deloitte's a great name, been around for a long time, associated with customer value in very profound ways, complex applications. That certainly characterizes IoT. What's going on with IoT at Deloitte? >> For us, we started a whole practice around IoT, and I'm leading that practice, but the thing for us was, there were a lot of science experiments going on around IoT, technology based, but we really wanted to bring it to what's the value behind IoT? So we really focused on use cases, and today we see that most focuses are on industrial IoT, though we spend a lot of time around connected products as well. I personally actually today worked on a project in a factory in Chicago, on a shop floor, connecting machines and measuring data and providing value. I work with an airline at an airport, around their travel so really helping guide you throughout the day. Interesting fact, you know we swipe away a lot of notifications without actually doing anything with it but when airline tells you, "Please come in 10 minutes early, the TSA wait time is long." I know you and I got to be there. >> You pay attention. >> Yeah, we got to be there early. We actually react to those notifications so I work on that and I work with high tech companies around their platforms, how to make their platforms better. >> You've raised a lot of really, really important issues but let's start with this notion of use cases >> Sure. >> A factory floor with a lot of PLCs, spitting out information, mediated by individuals or users and the data, where's it end up? That's real different from an airport where a lot of the data's being generated by a human being as they move places or is intended to be consumed by a human being. What kind of common patterns are you seeing in these use cases that brings them all under this notion of IoT? >> I always think of IoT as taking sensor data and making decisions based on those and what's interesting to me is that it creates this real interesting dilemma that we thought we knew what goes on with users, how they work and what they do. We do surveys just to find out what they're saying, the survey's actually probably not what they do but now with sensors we know what they do all the way to machines where we have decades of people having experience about, "This sounds a little odd, the machine doesn't sound right" but then they don't know what to do with it and now we can measure that because really at the end of the day, vibration isn't anything else but sound, right? So for me this is all about, and what's common about this, is that we really take that, we think we know to we actually know because we can now measure with sensors what goes on in that area. >> So it's almost like taking a lot of that time motion analysis, operations research that we used to do periodically, episodically with human beings doing their best to record stuff and bringing a lot of that discipline continuously and in real time so that it can better inform overall decisions, right? >> Yeah, I mean almost near real time, many of these cases and that's a really interesting scenario for me, right? Because now can actually see what happens in the factory when I tune the mix or the blend of my raw materials, what happens to the product that gets made at the end of that. >> As we think about the challenges or the changes that we foresee going on, is there a difference in thinking about humans as users or humans as consumers of a lot of this data and machines? I know there is, but how is this, because kind of the machine side has always been associated with SCADA, OT and the disciplines and approaches for that side seem a little bit different than what's coming out of the mobile world which is still very, very closely associated with how we utilize or how we deploy these systems to inform decisions in either case, is that right? >> I don't really know if we do so much about decisions for machines. I think at the end of the day many of the decisions are still made by humans. I mean I think about this like, we have a heating element running over, at the end of the day it still is a human that goes and sort of like says, "Yeah, let's turn that off." >> But there's still automation that takes place? >> Absolutely there's automation but automation takes place today. >> Sure. >> None of this is particularly new. I mean OT has done automation forever, right? >> Right. >> I think the interesting part is now taking the learning and connecting the different data points together. I talked about the factory floor, I just showed, actually, at the show we created a virtual factory line, life size. You can download it, it's the virtual factory by Deloitte. If I get my phone going I can show you, but it's not. Right here. (laughing) I call it "the internet of rubber ducks". >> "The internet of rubber ducks"? >> The internet of rubber ducks. Yeah, it's kind of cute. You have these little yellow ducks and if you load the app you can see them being made. But it's actually really what goes on at the factory and it really shows how when you change the blend at the beginning of a production line, how it effects at the end of the factory line, the outcome, how much scrap you have. What's the scrap? What's the overall equipment efficiency? OEE and so forth. What happens is now we can connect data from the very beginning of the factory line with he very end of the factory line and then combine that with contextual data such for example as temperature or the vibration on the machine or the current which we haven't done before. This whole time series of data that we now correlate becomes really critical and I don't think that's something we've done really as much before. That has not driven automation in this zone. >> If we think about it, we're talking about sensors which as you said, SCADA's been around for a long time and it tends to automate very, very proximate to where that sensor tower might be but a lot of the information that went into decisions was actually then generated by a person, perhaps a shift supervisor or somebody else or a machine operator said, "I heard a rattle" but there's no time so it's difficult to correlate and now we're talking about up leveling a lot of that information so it becomes part of the natural flow out of the machine but still for human consumption to make decisions? >> Yeah, very much like that. As I said, I talked about the blend of the materials that go in and then now we can correlate that particular part of the sheet. We can look on video and see how it looked and check the quality and then see at the end how many pieces of product did we produce. Actually in that particular case, it's really fascinating, it wasn't so much about reducing cost, it was actually increasing output. For them each line costs about 10 million and with the findings we have and what we're doing with them, we can actually give them the ability not to build another line but actually produce more lines because they can sell more which is a great position to be in. >> Sure, absolutely. >> You actually impact the top line rather than just the bottom line. >> Well productivity fundamentally is a function of what work you can perform for what costs are required to perform that work and if you can improve the effectiveness of something, keep the cost the same but get more work out of it, that's a big, big plus on the bottom line. >> And they have the market to sell it in to, right? >> Absolutely. >> If you just make more and you can't sell it- >> Well there's that, too. >> Yeah, which is really the good thing about that particular example. >> But talk about how, for example, you noted that they can look at a video of how the plastic or the sheets coming off the machine or set of rollers perhaps but how does AI start to be incorporated in to this IoT discussion? And what kind of use cases are you seeing becoming appropriate or more appropriate or made more productive by some of these new technologies we bring, some of the analytics and some of the IoT elements together? >> We find that we do a variety of theories. We go in and we say, "Hm, how about this? How about that?" And then we have our data scientists go and look at models for that and see what goes on and then put machine learning in and then we take those machine learning models and feed it back into, we talked before a little bit about this, but age processing is really something where we now process some of those models on the edge. The algorithm development and all the analysis we send that to the Cloud, we do number crunching there and we really take advantage of the unlimited capacity. >> A lot of the training happens up at the Cloud? >> A lot of the training happens in the Cloud and then whatever models come down, we load those on the edge and we actually do make decisions right there on the edge or we give the operator the choices to make the decisions right there on the edge. >> Training up in the Cloud but the inferencing actually is proximate to the actual action so there's locality for the action based on what's in the model and there's a lot of training that can happen, quite frankly, where you don't have to underwrite the cost of the infrastructure to do it? >> Exactly. >> That suggests that there is going to be a fair amount of change in the industry over the next few years in this notion of moving from OT to IT or SCADA to IoT. This is not just a set of technology issues, there's some fundamental other questions that are going to be important. A lot of people just kind of assume, "Oh, well throw a bunch of general purpose stuff at these IoT related things and it's going to be the IT industry all over again." Or is really the expertise associated with the use case going to be more important? How is that use case going to be ultimately realized? Is it going to be a bunch of piece parts or is it going to be more of a holistic approach to really understanding the nature of the solution and making sure that the outcome is the first and focal point? >> I'm going to come back to your question in a second. I just always, I have to smile because, so I have a Masters in petroleum engineering. So when I studied, I built really fancy models, like differential models, indicial models and you know, I simulated fracturing and- >> Process control's built with that stuff. >> I lived a good part of my life in OT and then after I came out of university I really moved more and more into IT so I've spent most of my career in information technology, including being a CIO. I always thought that the most fancy math we'd ever do is percentage calculations and that was pretty fancy. (laughs) Now, I find myself in this awesome place where I can bring together some of that OT, some of that real deep data science work that I did early on in my life, now with some of the process and system implementation expertise and practice that have come out of IT. They really come together, I don't think one takes over the other. I think there's real sort of meeting each other and going like, "Wow, okay. I guess we really got to work together." So that's really fun. About your question around what solutions do we see today? I see a lot of very vertical, very one use case oriented solutions, that go all the way from the sensor to edge to Cloud to, hopefully, integration to the back office systems because without that you can't really take good action. But they're very narrow and so, like in the good old Cloud days when Cloud became really big, there were really good point solutions and the good Cloud providers sold to the business user right there and then and ran around IT. And I see the same in IoT happening right now. You get a very good solution for temperature control on a truck, for example, right? Which is a very narrow solution but the moment you want to start doing something with your warehouse where you have other sensors and you need a horizontal platform, those vertical solutions fall short. That's what I think is sort of like the interesting dilemma right now. You have these vertical pillars and you have the horizontal platforms that the big providers have and so it'll be interesting to see when we're going to see some consolidation in this space when some of the vertical solutions are going to get bought out by the horizontals to provide better use cases. It's a little bit like the ERPs who did every industry and then eventually they realized, "We need industry focused solutions." We'll see the same in the IT space. >> The IT industry has always supposed that we can transfer knowledge we gain in one domain into other customers, into other use cases. It almost sounds like what you're saying is we're going to have that vertical organization of expertise, which is absolutely essential to solve that complex, core business problem. High risk, high value, high uncertainty, often bespoke, never done before but over time we will see a degree of experience sharing and diffusion so that over time we might see better, more applicable platforms that are capable of providing that foundation for a broader set of use cases but that' going to be a natural process of accretion. Is that how you kind of see it? >> Yeah, I mean we're all going to need streaming capabilities. We're all going to need capabilities for machine learning, for cognitive, for video analytics. We'll all need that but I think it'll be specific to the individual use case in a sense of, I'll give you an example, I just had a data scientist show me how he started looking at 20 year old scientific research on gear boxes. What frequencies happen in gear boxes, specifically to certain scenarios. That's not replicable from a gearbox to a pump, you know? >> Right. >> You have different, so there is specific things and yes it might be the same gearbox in one factory that produces, I don't know, rubber ducks to another factory who makes metal sheets but it's still gearbox specific, right? I think this is the specificity we're going to see around models, around learning and around sensors to a certain extent. >> Excellent, Robert Schmid, Chief IoT Technologist at Deloitte, thanks very much for being on theCUBE. >> Thanks for having me, Peter. It was a pleasure, thank you. (uplifting music)

(dramatic music) >> Narrator: Live from Washington D.C., it's cube conversations with John Furrier. >> Hello everyone, welcome to this special cube conversation, I'm John Furrier, the host of The Cube, co-founder of SiliconANGLE media Inc. We are here in the Washington D.C. Beltway area. We're actually at Amazon web services' public sector headquarters in Arlington, Virginia. My next guest is John Wood, he's the CEO and chairman of the board at Telos, a big provider of some of the big contracts, certainly with Amazon CIA, among others, welcome. >> Thank you very much. >> Thanks for joining me. >> I'm glad to be here. >> So, you guys have been pretty instrumental and we were talking to Teresa Carlson earlier, with an exclusive interview with her, and we talked about the shot heard around the Cloud. That was the CIA, Amazon win, four years ago. >> Yes. >> Kind of infiltrated the government area. It's almost a gestation period and now you got DOD action, a ton of other opportunities, but it really is an architectural mindset changeover from the old way. >> Yes You're involved in this, with Telos. What's your take, how are you guys involved, what's going on? >> Yeah, so it was groundbreaking, when the CIA made the determination that they were going to move to the Cloud, for sure. It kind of made everybody stand up and take notice, if the most security conscience organization in the world was considering it, why aren't I? And here we are, four years later, so where is the CIA now? Well now, the CIA is able to provision a server in a couple minutes, whereas the past, it used to take them almost a year. Now, with the use of automation tools like we have with Telos and the Xacta suite, the CIA is able to get their authority to operate in less than a week, when it used to take 18 months. So, I basically think what's happening is, the Cloud is providing an access point to IT modernization and the agency is showing that there is a blueprint that the rest of the government can also follow if they want to. >> One of the things we're involved in a lot of Blockchain covers, as well as kind of kicking the tires on Blockchain. You're in the middle of a Cloud gain with identity. Identity is the secret to having good scalable systems, because when you have good identity, good things happen. In Blockchain, some people say a theory about those. In IT, it's what identity you're going to use. How does the authority to operate challenge, you mentioned, become so important, because you're talking about massive amounts of time, I mean time savings. >> Wood: Yeah, so-- >> Just tease out the nuances of why it's so important to have that identity solution. >> So, in the past, there was no common language within which our cyber security professionals could engage with each other. Now, with the signing of the President's executive order on cyber security, the White House really is mandating the adoption if the NIST framework. What's relevant there is that on the one hand it provides you with a common language, but on the other hand, it's 11 hundred controls. So, as a result, automation is going to be key, to making sure that people can work with each other and making sure that, actually, the adoption actually takes off. >> They're safe, they know the trusted party. Is trust a big part of this and how does that--? >> I think what's happening, because the intelligence community has been working so closely together, and when I say the intelligence community, it's not just the traditional CIA, NSA, NRO, et cetera, it's also the military component of the intelligence community. So, you've got almost 38 assessors that are assessing C2S and SC2S. You know, the secret, if you will, Cloud, and the top secret Cloud, and those assessors all have been working in the same community under this framework and I think that has given them the confidence that the data is protected and as a result, they're heading much closer to reciprocity than ever before. >> There's been observations certainly on the Cube, we've said this many times with the past few years in tracking IT over the years, IT transformation, digital transformation, whatever you want to call it, buzz word. The reality is you had some progressives that would move faster and kick the tires, certainly financial services, in some areas you see that. Really, no problem. Then you had the folks who have just been consolidated down, didn't have a lot of budget and were lagging, waiting to adopt. Now there's no excuses, with cyber security, top of mind, with hacking, malware, ransomware, cyber warfare from nation states, sponsored states, an open source it's out of control. >> It is. >> So the security equations is forcing IT to move. The action has to be taken. What are you guys seeing in this area, because this is a big story and it's really putting a fire under everyone to move. >> And it's long over due. I co-wrote and article with our chief security officer in 2011, talking about why the Cloud was the way to go for federal, state, local, and education customers and at the end of the day, I think what's happening from a top cover perspective, the legislative community understands that. Obviously the Executive branch understands that, and now with editions like C2S the rest of the environment, the rest of the government can see what's possible. So, I believe the leadership within the government is ready for this change. They're seeing the benefit as it relates to C2S and SC2S and ultimately, the key is, the guys who run the contracts themselves, you got to make sure that those guys want that, to embrace that change too. >> Furrier: Yeah, so you have the-- >> And right now, 80, if you look across the government, 80% IT span is going back into maintenance. If you look at all my commercial customers, it's somewhere between 20 and 25%. What does that mean? It basically means that the government has a lot of legacy systems, which means that there's a lot of threats, and, which means there's a real cyber security problem. I believe fundamentally that by moving work loads to the Cloud, you'll be eliminating a lot of those cyber security problems. >> Yeah, it just means security is going to be the driver. The other thing I wanted to bring up, especially here in D.C., in public sector, is transparency. Now everyone can see everything. We're in a data-driven world, you can't hide either. The light is on, it's right there on the table. No more hiding. How has transparency been impacted in the procurement process, in the sales motions, the overall engagements with gov and public sector customers? >> I think, truth be told, there have been a lot of ideas that were sort of short-term and not really thoughtful, but the good news, as I said, is that the policy makers are really thinking and considering, trying to figure out how to make changes. Take for example, LPTA, low price technically acceptable. When I went to the congress and talked to both the House and the Senate side, and talked about how if I have one customer whose gotten hacked and the other customer has the same hack, but one happens to be a government customer and one's a commercial customer, the resources that we have are really trained, highly skilled, highly sought-after resources. Well, my commercial customers are willing to pay three to four hundred percent more than my government customers are. So when you have scarce resources, where are you going to apply them? You're going to apply them where the people are who are going to pay you. So my point to the Congress was simply to say, hey man, you get what you pay for. So ultimately, the good news is that, both on the House and the Senate side, that they elimanted LTPA, as it relates to cyber security, goods and services. So I believe, again, that there's a lot of, not just transparency happening, but there's a lot of people realizing that there are things that we can do. Procurement is kind of the last frontier for me. I have seen recently, I saw one of our government customers, where we were subcontracted, they went with something called an OTA, which stands for an other transaction agreement. Big problem in the government these days is everybody protest everything and there's really no downside to the protesting. With an OTA it's not protest-able. So I am seeing our government customers beginning to think about other means of actually doing things like procurement, and so that you can actually acquire. >> Are they going to have instant replay? (laughter) It sounds like the NFL, that call's not reversible. I mean, this is kind of, we're getting into all these rules and regulations where you've got protest, it seems that policy injection is not healthy at some level, because that point about what cost more on the commercial side, because of demand there, they understand the consequences and resource availability. To the government you just eliminated a policy that wasn't really helping. >> Right. >> So policy is a real consideration in here. >> I think so. Again though, it's a different environment than it was five or six years ago and I do think that there are some real positive things that are happening. I agree with you that there's a ground-swell of support behind the Cloud and certainly, players like us see the benefit associated with that shared security model. >> One of the things we've been observing and tracking on Sillaconangle and the Cube is this notion of public-private collaberation. Sharing data is a huge deal. Certainly, in Cyber people realize that data is valuable. Certainly, at Scale, you see patterns you might not see, customers on workloads, here and there, need to be identified. You're not sharing the data you don't know. So data sharing is a big deal, but also, collaborations between the private and public sector. Can you comment on what's going on there, because we're seeing some movement where, you're seeing some security agencies saying, "We'll share some stuff." >> Yeah. >> Furrier: You share some stuff with me, so you're seeing a little bit of the community developing heavily around data-sharing, what's you're take on that? >> So, I think we have a ways to go to make it work right, because if it was working right, you wouldn't see the very published, publicized hacks that have gone on. One of the things that the Congress can do is to provide incentives for the private sector to share more information, more quickly. When the Yahoo hacks occurred, it wasn't discovered until two or three years later. As a result, like I said, there's really no incentive and there's a perceived amount of liability. One of the things I'm asking some of our Congress people to consider is if you do share information, maybe, there's a limitation on liability and that provides, if you will, a mechanism and that provides an incentive for the private organization to work with the public organizations. >> So not to bury it, like Yahoo tried to bury that thing. >> Exactly. There's no sense in burying it. There should be no reason to bury. >> Okay, take a minute to talk about Telos, what you guys are doing, the chief executive. What's going on with the company, talk about the successes, where you guys are winning, your challenges and opportunities. >> Sure, we're in the business of, we do cyber security, we do identity and we do secure mobility. In the area of cyber security, I'm very proud about the fact that we're the database of record for intelligence community, many department of defense agencies use us, homeland security, a whole, department of safe-- There's a whole bunch of organizations that tend to work with us. I think that the issue for me has always been around investing in things that make our customers more efficient. So whether it's cyber security, it's one thing to provide the authority to operate, but I like to provide that authority to operate on a continuous basis. When we talk about identity, it's one thing to say that I am who I say I am, but it's another thing to let you know that I'm actually somebody that's trust worthy. So, we have a special relationship with the FBI that allows us to do real-time data look-ups on their people. We're the integrator of record for the common access card, the military ID card, we have been for a long time. From that, we built a business relationship with the TSA and now we have about 70 airports around America that use our service to do identity as a service for all their employees. >> Can you get me to cut the line at Pre? (laughter) >> You know, if you want to cut the line at TSA pre-- >> Quality of service opportunity and people will pay more for that. >> Absolutely. And plus, I think TSA pre-check wants to have a lot more people in that ecosystem too. No different than when the Easy Pass came into play years and years ago. I remember just zooming through the Easy Pass and wondering why people would want to stand in line, why would you, right? And then if you think about it, we're also involved with secure mobility, so we have a capability called Telos ghost that allows you to basically hide on a network. You're familiar with the notion of signal hopping? In World War two that's how we avoided detection by the enemy, so this is what we invented here with something around IP hopping. So as a result of that, whether you're a server-facing thing or a client-facing thing or a mobile device, you can't be seen on the network and if you can't be seen on the network, you can't be hacked. >> Well, that's awesome stuff. Your relationship with Amazon Web Services, talk about that, some of the things you're involved in. >> Yeah. >> The deals, the momentum. What's the relationship look like between you guys? >> So we have an enormous relationship with Amazon, most important part that we have, it started with the agency and I was in a meeting with Teresa Carlson, one of the senior people in the agency, and we wondered whether or not we could do for, we Telos, can do for the Cloud that which we've been doing for the enterprise for the better part of 15 to 17 years now, which is basically providing that authority to operate in an automated way. So we invested together and we were able to prove that we could absolutely do that. Now, what we're doing is we're basically copying and pasting that model to our customers across the government. >> And you guys put a stake in the ground, 2011. You were early. I mean 2008 was the beginning of the DevOps movement, you were in the heart of it in 2011. >> Wood: Yep. What's the biggest thing you've learned or observed or experienced over those years, since 2011? >> The biggest thing? >> Or just the most important. >> Wood: That is an enormous question. >> It could be the most important, the most relevant, most surprising-- >> Well the most important thing was I got married in 2012. (laughter) I have a four year old and two year old and a 14-year old, those are the most important. >> Was it really you who got married, was it your identity? >> Wood: It was really me and it was my identity. I will say, I think that the government is embracing efficiency. The government is embracing change. I think it started around 2014 or 15, and now it's really moving out. I think there's a lot of top cover, both from a policy side and an executive side and I'm seeing a lot of leadership from within the government itself of people who want to make the change happen. >> And there's also the competitive fairness question we're hearing, just here in town, yesterday, rumblings of one-source Cloud, multi-Cloud. Amazon is technically a one-source Cloud, but they've got an ecosystem. Should they have multi-Cloud in their requirements? All these things almost feel like that protest model is going on, like there's a little fud going everywhere from the other vendors. Do we expect to see more of that in your mind or less of it? (laughter) >> I think at the end of the day-- >> The chips are taken off the table. >> The people who don't want change are the ones, who are, if you will, very invested in the legacy. If those people are paid, time, material or cost blessed, they're not paid to be efficient. So there's going to be push back. On the other hand we've seen by the gigantic growth of the adoption of the Cloud and by the Cloud infrastructure and the Cloud ecosystem itself, there are enomorous opportunities for organizations out there. So I think people should embrace the change, I really do. I think, fundamentally, it's going to be a really big positive to this industry and into this region. >> I always say to Dave Vellante and my co-hosts, it's like no brainer, you look at the main frame, that was the generation when I was growing in the industry. I was the young gun, like main frame co-ball, who the hell wants that? Mini computer, eh, I want the client server. It's pretty obvious when you're in it. So I got to ask you with that in mind, Cloud is pretty obvious. Folks will understand DevOps and automation and those efficiences. You mentioned authority to operate as an example. Some of these numbers are pretty significant. So let's go down the problems that are important, what are the consequences, how do you quantify it, right? So the problem that people are trying to solve is how do I get resources, computing, software, whatever. Pretty important, because now you've got security, you've got all kinds of stuff. What are some of the consequences and you mentioned some benchmarks that you've quantified. You mentioned provisioning a server in a year. Is that really true? >> Wood: That's true! >> So give me some data on some of consequences, kind of the old way and new way. >> Well the old way if you're using the traditional procurement, it's like I said, one of the big issues is whether it was the culture or it's procurement roles or just the process to get an approval, it would take a year to get a server provisioned. Now, it's literally, you push a button and one to two minutes later you have a server, a new server. So you get ultimate scale, you get ultimate throughput, you pay as you go, you pay what you use. What's not to like? So that's all good. From the standpoint of security, because it's the NIST framework we can automate about 90% of that. That's 11 hundred controls, right? So we automate about 90% of those 11 hundred controls. Now, you get a whole bunch of auto inheritance, a whole bunch of things that can be automated are, and as a result, when NIST goes from one version of NIST to another version, all that happens automatically, and more importantly, as a cyber security professional, and I've only been at it since 1994. (laughter) I've been in it for relatively a long time as a CEO. As a cyber security professional, what I see is, as long as I can show a continuous monitoring of your current status, that's very relevant to the operational security professional. That's really good. So for us, we know that our customers are going to be a combination of Cloud, hybrid, and on-prem. These large organizations are going to take years and years and years to move to the Cloud, but they got to start, because now is the time. >> So automation and having that nice stack where it automatically updates and auto-provisioning, auto scaling, but the operational provisioning piece is really where the rubber meets the road, right? Is that what you're getting at? >> Well it's that. It's also you're consolidating your data centers. You don't need lots of them anymore. You can just focus on one, that's another big area. Another big area is, you can lift and shift your legacy IT infrastructure into the Cloud and then put the big investment into the new application as it's siting in there in the Cloud. >> Awesome, John, thanks for joining us here in the cube conversation. Here at Amazon Web Services Headquarters, breaking down the trends in GovCloud public sector as Cloud computing really levels the playing field, opens up new doors, new solutions, faster time to operate, in vi of other things, here in Washington, D.C., in Arlington, Virginia, I'm John Furrier. Thanks for watching. (dramatic music)

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

prepare for the extraction point we've been briefed on all the important stories and events in the world of emerging information now it's time to extract the data and turn it into action live from the silicon angle studios in the heart of Silicon Valley this is extraction point with John furrier okay we're live back in the palo alto studios i'm john furrier for the extraction point we extract the signal from the noise and my special guest today i'm excited to have here is Paul Martino who is the founder of aggregate knowledge and also storied entrepreneur in Silicon Valley who now lives in Philly with his family comes out here Paul is known for among other things being a great entrepreneur tech geek loves tech loves to build build startups started one of the first social networks with Mark Pincus called tribe started his own company funded by Kleiner Perkins with his partner Chris law called aggregate knowledge which is booming and doing great and now more famous for being the first round investor in zynga company that is exploding with revenue as Kleiner Perkins said is the of all their portfolio comes in the history more than Google's made more money faster than anybody Paul Martino welcome to the extraction point great to see you John as always awesome to see you first I got to start with your now I forgot to mention that you're actually running a venture firm so in addition to being famous with Zynga you're running bullpen capital so first give the folks out there an update and first confirm or deny you were in the first round of Zynga or not yes the the first round of Zynga there were several institutional investors and several individual investors Morocco me Reid Hoffman were individual investors Avalon Union Square accelerator ventures and foundry where the institutional investors in that first round Peter was Peter Thiel yeah Peter was also an individual investor in the first round so that's officially the first round investors of Zynga we have clarified that and that is now hot on the books but now you're you've been successfully founded aggregate knowledge you know have a CEO running that what's the update with aggregate knowledge yeah so great guy runs that company as a guy you need to meet and have on this show Dave jakubowski aggregate knowledge really went in a direction where all of the focus was on providing data and analytics to the major ad agencies and John John Nelson who started organic one of the first agencies is now the CEO of Omnicom digital joined the board and I said look we got to get a guy who's an ad heavy in here and jakubowski was previously the GM of microsoft adcenter and had a senior position at specific media and we brought him in and he's just been kickin butt our greek knowledge has really really made a significant significant contribution in the area of data and analytics for these major agencies and he was very able to bring in a crew of people know exactly how to run that business so you're a big fan of big data then mm-hmm oh yeah we just had a big special yesterday on Big Data mentioned about it so that's cool we're going to get into a lobbyist I was just kind of get the small talk out of the way here your current role is the founder of bullpen capital right so bullpen to me I'm a baseball not I love baseball bullpen means you go the bullpen for relief right yep thank God close the game out hopefully or mid-innings relief so tell us about what bullpen is it's a special fund as I know from reading talk to you to target an expansion of this new seed and explosive new funding environment Bryce plain force right I'll tell you how we got the name at the end too so here's what happened I've been investing with a lot of the so-called super angels and that's kind of a misnomer because they really are actually in some cases actual small venture firms to I've been investing with a lot of them since they got off the ground Josh Kopelman from first round is one of the first investors in aggregate knowledge mike maples was an early advisor to the company I've known Jeff claw be a who run soft tech since he was at Reuters and with the late 90s and so I've worked with these guys done a lot of investing and we were me and my buddies Duncan Davidson rich Melman were sitting around over summer of 09 doing a little bit data analysis right another big data assignment we realized that as more and more these seed funds got created they were creating an inventory of companies that weren't quite ready to go to the traditional venture guy but we're also difficult to bridge from just the seed guys because the see guys at that time didn't have really big funds so wait a minute you've got some really good companies here is to clarify the for the folks out there seed funds don't traditionally have follow-on big funds like a VC firm right that's what you're referring to yeah they tend not to have as bigger reserve so if a big fun writes you a five-million-dollar check and you stub your toe you can probably get some more money to get through the hardships but a lot of the the new super angel funds or smaller funds and you get a five hundred thousand dollar check and if you need another five hundred thousand dollars it can frequently be very difficult because they make so many investments with smaller reserves yeah and so you've got dave McClure clavey a maples first round capital true ventures made the first round truevision more traditional VC then say dave McClure and mike maples and claw VA they're out doing some really good work out their funding really good company spending a lot of time I know I've seen them working their butt off yeah they need some air support right they need some cover the little bullpen is that that's you come in and say hey for your stars they're going to rise up yep and so that's exactly right so what happens is here's what the analysis we did turned out of their portfolio thirty percent of their portfolios in aggregate quickly are really exciting companies you know and they quickly go up to a venture auction and the guys and sandhill rotor excited about it about twenty percent of their deals you know that they don't like too much it's kind of just floating there yeah that you know the entrepreneur wasn't a fit that team didn't execute that left fifty percent of their deals in the middle which they kind of were too early to tell as Mike maple sometimes says they were in an extended learning and discovery phase they hadn't quite figured out what their models yeah and this de pivoting stuff's going on right now the Marcus changes turbulence so these guys are right and so you look you look at some examples and you go well wait a minute for every zynga that goes up into the right immediately go look at the stories of chegg and modcloth and etsy and quite frankly the in-between round on twitter and for everyone Zynga that you find that just hits it out of the park the right way there were four to five companies that went through that hard intermediate round that it was difficult in the environment where you have only a potentially thinly capitalized seed fund in front of you go get through that difficult point I said guys you need a bull pen and way we came up with the name is I'm involved in a deal with Chad Durbin who used to pitch for the Phillies and now as a relief pitcher for the cleveland indians and he was in our office and we were talking about this idea and Chad said yeah it's kind of like you're building a bullpen for the seed guys I'm like that's exactly right that's the name we got to go with and so fortunately I was involved in in this company called showcase you which is actually cool cited suppose for recruiting for college scholarships for a collegiate athletes right you're a high school student you throw 80 miles an hour left hand it and you're in 10th grade how do you figure out where the right scholarships are so Durbin and some of the Phillies where the original investors in this company called showcase you it's actually a cool company as the combine work out online basically fries for the high school kids and because the high school kids sometimes are in tough geographies to get to you're in you're in a small rural area in Nebraska how do they find out that you're the guy who can throw 89 miles an hour great so I mean this VC market so basically you're referring to with bullpen right now is an innie and you've been in our sprayer so you live through classic you know classic financing your last company financed by kleiner perkins and a tribe i forget who financed tribe yet Mayfield was the lead investor may feel again another traditional VC firm all tier 1 VCS although may feel people are you now is slipped a little bit that's some of their key partners who have slipped away but they've all moved on what you're really referring to is there's a new dynamic of entrepreneurship going on now we're now there are some break outcomes that just need a little bit more time to mature in the old model they just be kind of closed down the VC guy would be on the Bora has just a pain in the ass and you know really not growing and do another round it's they get kind of lazy in a way if they got 10 10 boards are on so with the super angels and the fact that does take a lot of cash to start a company you've got more deals getting done so the the Y Combinator the Dave McClure's and chef claw va's in the mike maples and sometimes SiliconANGLE labs which we're doing here is telling you about right we're funding companies the more [ __ ] is funded a better will you come in as you keep them alive longer just wreck the pivot possibly that's right and so what happens is right now the venture industry is being disrupted the same way the venture industry has funded companies that have rupted other industries they are being disrupted in the exact same way and the disruption happened from below as always happens it started in seed stage now in order for the disruption to go all the way through there need to be companies that come after seed stage investors that have the same philosophy and mentality pro entrepreneur easy terms operating people who get their hands dirty to get deals done you need that in the B stage and in the sea stage and here's what our prediction is John our prediction is a few years from now there'll be a company that comes after bullpen that does series c and series d financing or mezzanine financing but the same philosophy is bullpen and then DST s at the end of that chain and you can imagine building companies that go all the way to liquidity that you got money from maples first bullpen second this unnamed company third and you went quasi-public with DST and you've bypassed the entire venture scheme entirely and the entire institutional public markets complete liquidity wealth creation companies creating jobs I mean this is new paradigm I mean this isn't amazing I mean this is a potentially amazing point in the history of us finance the idea that you could go two billion dollar outcomes by passing not only the public markets on the back side but the traditional venture ecosystem on the front side I mean that is a disruption if ever there was one amen I mean hi and with you a hundred percent the other some people who will argue regulation is if market forces first of all I'm a big believer in market forces so I think what you're doing is clearly identifying an opportunity that dynamics are all lying lining up entrepreneurs are validating it and so but the questions are regulations I mean first of all I'm anti-regulation but as you start to get to that liquidity and some are arguing I even wrote a blog post about saying hey you know basically Facebook's public merry go buddy what do you say to those guys this is the change in the history of this financial asustor we want the government regulating this yeah so my co-founder of both i started bullpen with two really good guys Duncan Davison who was the founder covad was advantage point for years asking them to buy government regulation would go bad i mean what happened then because of the I lack warsi like Wars but only that the some extent covet doesn't exist unless the telco 1994 happens through in some ways a creation of the government to good point it's social right but but think about it the arbitrariness of government as opposed to a well-thought-out centralized plan so anyway so Duncan sometimes uses that phrase you know he talks a lot about the way in which the government you know that the worst thing you can ever hear is I'm with the government I'm here to help right i mean that's about the way it goes but his point around the the the new quasi public markets is money we'll find a way yeah and when sarbanes-oxley happens and it's tough to go public and you're a CEO like Pincus who's running one of the great all-time companies in Silicon Valley at Zynga he says you know going public is not an entrance is not an exit it's an entrance that's that's this quote what why would I why do I need that headache I mean I was just talking with Charles beeler who sold for the hell dorado he sold to compel in one of his investments to dell for over a billion dollars and and 3 para nother firm he wasn't on that one that was sold to HP during storage wars he's talking about the lawsuits literally this shakedown of immediately filed lawsuits you know you could have got more money so this is this public markets brutal no doubt no doubt i think what you're doing is a revolution I'm all excited about this new environment again anything with his liquidity wealth creation with the engine of innovation can be powered that's fantastic look back the startups okay get back to where you're playing yeah the history of Silicon Valley was built on the notion of value add some have said over the past 10 years venture capital has not been truly value add and some were arguing value subtract and then just money so what you're talking about here is getting in and helping me stay alive what's the value added side of the equation mean I know that a lot of these folks like like like ourselves here it's looking angle McClure Xavier and maples and true ventures they roll their sleeves up first round capital right before we can only provide so much it kind of expands right you guys are filling in the capital market side right how are you guys helping out on the value add because a lot of those companies may be the next Twitter right you've got a bridge to finance that's right allow them to do the pivot or get the creative energy to grow and they hit that market if they hit that hit it going vertical you got it kind of sometimes nurture it you guys have a strategy for that talk about the so let me let me give you my perspective on that so I think 10 years ago when you're starting a company the name of the venture firm was more important than potentially the partner on your board ten years later the name of the firm matters much less and it's the name of the partner and it's the operating experience that that partner partner brought to bear and you go talk to the 24 year old entrepreneur verse the 34 year old entrepreneur the 24 entrepreneur 24 year old entrepreneur wants a guy like you or a guy like me on his board he wants have been there done that started a company was a CEO exited it got fired hired people fired other people scar tissue scars knowledge experience exactly and if a good friend of mine who's in the traditional business I'll leave his name out of it he sometimes says the following phrase the era of the gentleman VC is over and what he means by the era of the gentleman VC is over is you know if your background is you were a junior associate who came in with a finance degree in an MBA and it never started a company you're not going to get picked by the entrepreneur anymore in 10 years from now almost everyone in the business is going to have a resume that looks more like a Cristal Paul Martino a mark pincus that you name all the people who we've started our companies with if there's a lot more hochberg with track record certainly with with the kind of big companies in the valley just in our generation yet started with netscape google paypal right now i want to see facebook is and then now's inga either the ecosystem is just entered intertwined I mean for every failure that spawns more success right so that's right that's a Silicon Valley way yeah well a tribe was tribe was a perfect example of a successful failure tribe was not a successful outcome but it was in many ways a very successful way to actually pioneer what became social networking you know investments got made into Facebook as a result of that Zynga in aggregate knowledge were both the outcrops of what was learned to some extent the original business case of Zynga was remarkably simple there is a ton of time being spent on social networks and after you get done finding your buddies and looking at photos what do you do and Pincus is original vision to some extent was let's have games to play and that insight doesn't happen that way unless you don't do tribe and go into the trenches and get the scars on your back and your in your your second venture of our adventure right at the tribe was aggregate knowledge was similar concept people are connected I mean you got to be excited though I mean you know you were involved in tribes very early on all the stuff that you dealt with activity streams newsfeed connections the social science you know the one that one of the nicest pieces of validation of this recently was over in q4 of 2010 seven of the patents that me Chris law Elliot low and Brian Waller wrote got issued now they're all owned by Cisco Cisco bought tribe in the end they bought the assets in the and the patent filings but there are patent filings that go back to 2002 on the corner stones and hallmarks of what social networking really is that we wrote back then that have now issued order granted or sitting in the cisco portfolio and well that's kind of like a consolation prize and that there wasn't a big outcome for tribe it is very validating to see that those original claims on really cutting-edge stuff have been had been issued and I'm excited about that you should be proud i'm proud to know your great guy you have great integrity you're going to do well as a venture capitalist i think you people will trust you and you're fair and there's two types of people in this world people who help people people who screw people so you know you really on one side of the other you're you're not in between you're truly on the on the good side I really enjoy you know having chatting with you but let's talk about entrepreneurship from that perspective about patents you know I'm try was an outcome that we all can relate to the peplum with Facebook of what Zuckerberg and and those guys are doing over there that's entrepreneurship so talk to the entrepreneurs out there yeah hey you know what you do some good work it all comes back to you talk about the the Karma of entrepreneurship a failure is not a bad thing it's kind of a punch line these days I'll failures are stepping stone to the next thing but talk about your experience and lets you and i talk about how to deal with faith for those first-time entrepreneurs out there in their 20s what just give them a sense of how to approach their venture and if it fails or succeeds what advice would you give them yeah well like winning and losing is important part of the game I mean certain companies are going to be successful in certain ones art and if you go and start ten unsuccessful companies maybe this isn't exactly the business for you but that said how you the game is important as well and if you're a high integrity guy who gets good investors and you make quality decisions and let's say the market wasn't a fit you're going to get the money the second time because people said you know I work with that guy that guy really did a good job you know they never got it quite right but this is a guy learn the right lessons so when I'm coaching a first-time CEO and i'm the CEO coach of a couple guys now you know i'm looking for someone who's sitting there going hey i not only want to do this to win and be successful but i want to learn i I want to do this better than no one no one walks in and says I learn from my failure I hope I'm successful I mean you let it go and say hey I'm gonna be successful I want to win failure is not an option but failure happens right i mean you know it's bad breaks that mean but but here is the key less I tell this to all of the entrepreneurs I work with you will not be successful if you're making mistakes that were made by those before you if you make novel mistakes you're in good company right and so only ever make a novel mistake I made a good example this is one claw and I started Chris law and I started aggregate knowledge aggregate knowledge was the original business model was around recommendations and there were dead bodies in front of us there was net perceptions there was fire fly and she was in the office this morning with Yazdi one of the founders of [ __ ] cast with it man yeah so predictive analytics residi what did we do we went out and we I flew out and met John riedle University of Minnesota who was the founder of net perceptions I dug up yes d i got these guys on my advisory board and while aggregate knowledge was not successful in the recommendation business and pivoted into the data management thing we made novel mistakes we did not repeat the mistakes of met perceptions and firefly and so i think that's an important important lesson to an entrepreneur if you're going into an area that has dead bodies in front of you you better research them you better know who they are you better know what happened and you better make sure that if you screw it up you at least screw it up in a way which none of us could have predicted yeah that's the only way you're going to get a hall pass on that well let's talk about talk about some of the hot Renisha of activity saw so you're in that sector where you're feeding the seed the super angels in the first rounds early stage guys and it's a good fit what about some of the philosophies on like the firms out there there's of this to this two philosophies I just taught us to an entrepreneur here you met on the way out a street speaker text and there at seven you know under a million dollars in financing hmm series a yeah and then you got in the news yesterday color 41 million dollars building to win magnin flipboard a hundred million dollars i got this is these guys that we know i mean there are yep our generation and a little bit around the same time and certainly they have pedigree so remember the old days the arms race mentality right when the sector at all costs right that's kind of what's going on here i mean some of the command that kind of money there's actually an auction going on what do you make of that I mean bubble is an arms race so so rich Melman inside a bullpen de tu fascinating analysis he looked at the full portfolio of 28 took about 20 of the best super angels by the way the super angles are all different some are micro vc summer buying options etc so so first off super angel is a weird word but it's everybody from Union Square and foundry on one side first round and flooding but any take the top 20 or so of these guys and look at their portfolios what's amazing about their portfolios is the unlike 10 and 20 years ago in prior tech bubbles there are not 20 companies doing the same thing when you categorize them yeah ten percent are in ad tech ten percent our direct-to-consumer consider but like forty percent are one-offs that is this is I think one of the first times in the history of venture that forty percent of the deal flow is a one-off unique business idea that there aren't 30 guys going to do and I think that the importance of that to what happens in this next stage of the tech boom we don't know what that means yet because back in the day well we need to just we're venture firm we need to disk drive company okay so your venture firm you've got your disk drive companies and I'll 20 venture friend knows if drive out and created the herd mentality everyone talks about with venture yep mean I was an opponent on a talk on here in the cube and I don't think I actually put in a blog post but I called the era of entrepreneurship like with open sores and low cost of entry with cloud computing and now mobility the manure of innovation where you know in the manure that's being out in the mark place mushrooms are growing out of it right and these you don't know what's going to be all look the same in a way so how do you tell the good ones from the bad ones so it's hard right so you have a lot of one you have a lot more activity hence angel list hence the super in rice so so the economics and the deal flow are all there the question is how do you get them from being just a one-off looked good on paper flame out the reality yeah well look in my opinion seed stage investing is about investing in people and I think when big firms trying to seed stage investing there's an impedance mismatch a lot of times because they want more evidence they want to know did the market work to the management then this is this is an early stage venture and am I going to want to go in a foxhole with this person and in many ways the good super angels are instinctive investors who are betting on people that they want to be in the foxhole with and yeah did they do it before do they know how to hire people is the market reasonably interesting but guess what they're probably gonna pivot three times so wait a minute at the end of the day you got to invest in people later stage venture is not you can look at discounted cash flows you can look at mezzanine financing you can do traditional measures but if you're going to invest in two people who have a prototype and need five hundred thousand dollars you're investing in people at that point what do you think about the OC angel is I'm a big fan of and recently was added thanks to maybe out there but even though i'm not i don't really co-invest with anyone else other than myself maybe you guys would bullpen but but if that's a phenomenon you don't have angel list which is opening up doors for deal flow companies are getting funded navales getting yeah a ton of activity nivea doing great job with venture hacks i get y combinator which I called the community college of startups they bring in like they open the door and I mean that an actually good way don't mean that negatively I mean they're giving access to entrepreneurs that never had access to the market right and now you have Paul Graham kind of giving the halo effect or thrown the holy water on certain stars and they get magically funded but yesterday at an event and they're they're packed right I've heard from VC saying I'm not invited because I didn't wasn't part of the original investment class so it seems that Y comma day is getting full yeah so do you see that you agree is there will be an over lo y combinator you know kind of like I've TED Conference has you know Ted they'll be you know y combinator Boston little franchises will be like barcamp for sure I mean look and look at techstars they franchise they'd I was over there with Dave Tisch in New York there's TechStars New York after those TechStars older in techstars seattle there is no doubt in my mind that right now there is an over investment in the seed stage meaning that there is a little bit of a seed bubble going on that's not necessarily bad though because in terms of raw dollars there's not a bubble yet Rory who's over at rafi it smells like a bubble it looks like a bubble but when you look at the mechanic when you look at the actual total dollars it's not a bubble rory who has a hinge recent Horowitz been said that that it's a boom not a bubble yeah so don't be confused it looks like bubbles and booms kind of look together the same right I actually I'm not quite sure I had the exact data right but here's the quick summary if you take a look at venture capital investment as a percent of GDP historically it's been something like point one percent of GDP in the bubble back in 99 it went to one percent something like it went 10x higher right now we're still at point one percent but since it's very much centered around the seed stage investing you see this frothiness in the sea but until that number goes from point 1 percent of GDP back up to one percent there's no real bubble because the tonnage of money hasn't come in yet and so so it's starting but this is what a tech boom feels like the early stages are excitement and lots of ideas and lots of flowers blooming and then the big money comes in because John I'll bet you're your brother and your sister and your mom haven't invested in a tech startup back in 99 video there's no public market that supports seven in a way that's a good and bad star basement yeah there's no fraud going on and most of the companies that are out there whether their lifestyle business or seed or bullpen funded are actually generating income the entrepreneur he has any earlier Mike was saying that he could a business deal so people are kind of like saw the old bubble and said shoot I don't want to do that again I gotta have at least revenue right and so companies didn't seem to start out with cash so you know that because you invested it but you know Pincus was getting some cash flow in the door from day one that's right that company was company was profitable the first day it started basically so talk about you know so I'm with Paul Martino by the way with bullpen capital entrepreneur wrote the patents on social networking which he sold the cisco when they sold the company now with bullpen capital huge dynamic you're a company out there this is exactly the positive dynamic you want to see because mainly you know dave mcclure jeff clavier mike maples have been kind of getting their butts handed to them in the press about super angels not having the juice to kind of go anywhere and it's been kind of a negative press there so you know this is the kind of void that's been filled by you guys to show the market that look at this there's a road map here so even though that the McClure's and clubs don't have big funds that there's a path to follow on financing so that the vc's can't shut them down and i've heard some pc say that so a lot of traditional venture guys would like to say that you know this little disruption we nipped it in the butt and it stopped after the seed stage but that's not the history of disruptions the history of disruptions are they start from the bottom then they get ecosystem support and then they grow and they disrupt the incumbents and I think we're halfway there so so the Angel gate thing that Arrington reported on was interesting because you know essentially what happened there it was a lot of him fighting Ron Conway I was not happy you can't be happy about competition I mean this is competition that increases prices right so you know in the short term prices have been inflated on valuations true or false that's true but but but I think I think the whole way angel gate was reported was absurd the most Pro entrepreneurial venture people perhaps in the history of the business are the guys who were supposedly at those tables I mean mike maples Jeff claw VA josh cop and Ron Conway fired his guy that was there I I understand suppose again suppose a key are right these are the most Pro entrepreneurial venture guys in the history of the business so I think that turned into something that it never was yeah well I mean that's the thing you know good for content producers who want page views I got to create some drama and you know as you know SiliconANGLE doesn't have any banner ads on our site quick plug for us we are motivated by content not page views so thanks for coming in today no but seriously I mean there's a there's a black cloud over the super angels has been since Angel gate I've heard privately from VCS that super angels it's been kind of a scuttlebutt they're misaligned just rumors I completely overblown and you know their business model threatens the incumbents and you know someone needed someone needed a piece of fodder to start a you know start a techcrunch discussion right there's no doubt that the market is need in need of a new ecosystem for the early stage because individual angels traditionally were wealthy individuals but now you have people with more experience like yourselves and entrepreneurs from google and facebook etc coming out and doing some things okay so next topic more on a personal kind of professional note k last final question is I know you got to run appreciate your time you're a technologist a lot of folks don't know that you're hardcore computer science guy and our model southern angles computer science meet social science right in your wheelhouse so with that just kind of final parting question what gets you excited technically right now I mean I'll see you have roots in both comps I and social Iran Zynga's early investor roster you got a bullpen capital you're looking at a lot of deals outside of that you as a computer scientist geek mm-hmm what gets you jazz what do you see in the horizon that's not yet on the mega trend roster that kind of you can't put your finger on it truly we might really get a good feeling well so I think you'll be disappointed with this answer because I think it's now cross the chasm to start being one of those mega trends it's called consumerization of enterprise and that's now the buzz word for it but what is it really mean and why do I think it's for real look you've got cool self-service applications for everything you can go do home banking by logging into a portal you can go to an ATM you can go do these things but you know go bring a new laptop into your big stodgy fortune 500 company and you know it's like getting a rectal exam right you know we got to install this we got to give you this private key yet that's TSA it writes like going through TSA exact idea that IT inside of big fortune 500 companies is going to stop being this gatekeeper to new technology I think look how long do you think it'll be until pick your favorite fortune 500 company the IT people know how to deal with the ipad 2 but how many people bought an ipad 2 into the off already everyone and so this to me is going to be the big next deck the next decade are going to be self service offerings for the enterprise getting around a very frustrating gatekeepers inside of you know the IT department etc and that's going to lead to an awesome boom of everything from security to auditing to compliance etc that's the convergence question Paul Martino my friend entrepreneur great guy venture capitals now on the good side helping the seed Super Angel micro VCS great to have you consumerization of IT that hits the cloud mobile social it's everything so that I was buzzword compliant on that great job great to have you know you're busy got to have you in again thanks so much for time that's a wrap thank you very much great thank you John