What is up, Marvel Squad? And welcome back to our channel. I am talking very quiet. As you read by the title, I am going to be doing the cheating with the door locked prank. If you guys do not know this, we are currently with Connor Bowe and Danny Smith. We are staying at a really nice Airbnb. Let me just show you a little sneak peek of the bathroom really quick. This is our master bathroom. And look at how nice, like even the shower. So yeah, Marvel Squad, this Airbnb is so nice. I'm really excited to be here for the next couple of days. But, as you guys read by the title, we are going to be doing the cheating with the door locked.  And he's going to be like, why is she calling Connor in there? But as you know, I'm pulling a prank on him. So basically what we're going to do is we're going to act like, you know, things are going down in the room.  If you're not already subscribed, make sure you hit that subscribe button and turn your post notifications because we upload three times a week, Wednesdays, Fridays, and Sundays. And you don't want to miss out on any one of our videos. If you guys do not already know this, we are doing an iPhone 11 giveaway for the end of this month. So if you would be entered, all you have to do is make sure you're following both of our Instagram accounts. Make sure you're just showing love in the comments section and through our Instagrams. And make sure you're just constantly showing love in order to be entered. Because even if you don't have an Instagram, as long as you're showing love in the comments sections, that's all that matters. We will see whoever is the most active.  This is our first time meeting him and we're already doing a intense prank like this. So this is about to be a little insane. So let me call Connor in here. Hey Connor, can you come here for a second? Are you ready to open the door? All right, I have to sneak you in on some things, okay? So we're doing a prank, I'm there. So what we're gonna do is we're gonna cheat the door lock. So we have to kind of be loud because our room is at the end of the, if you guys don't know this is Connor Bogey. If you guys don't already know him, he's Connor Bogey. So let's subscribe to his YouTube channel. But yeah, so we're gonna start off. I've seen these before, but I've never done it either. I never either. So we have to figure out some things to kind of make it loud in here. Okay, so we lock the door? Yes, we definitely have to lock the door. What if he just kicks? I'm scared, he's gonna be pissed. So we have to like make noises, shake the bed. Yes. Okay. So this is what we're working with. Do you have a belt? Shoot, I didn't bring one. I didn't bring one. Does he have a belt? I think he has a car. He uses his own belt to break it. Yeah, I know. Yeah, so are you in on this? I'm done. You ready? Yeah. We're gonna get him good. We're gonna get him good. All right, so we're gonna go prank him. I don't know what noises are we gonna do yet because this is our first time meeting. So this is kind of a, this is a little high tech for the first time, but. We'll come up with some good stuff on the spot. Yeah, we will. All right, we just set up the camera in a good spot. He's not gonna be able to even guess what's going on, but all right, how are we still making these noises? Is that, it squeaks? Okay, yeah. How are we gonna get him to hear? Should I like make a lot of noise? Kind of like a yell? Just be like, it's not about Oomar Eye or something like that.  Oh, Brian. Oh my gosh. You guys good? What are you doing today?  Hurry. Okay. Hurry, sis. Hold on, hold on. Pull on, Bill. Bill, just wait one second. Just one second. This bed's just super shaky. Come on, we gotta finish something. I'm almost done. Hold on, put your clothes on. I will break it off. Hey, make the bed real quick. Okay, okay, okay. Hold on. Hold on, Bill. Hey, relax, bro. It's not that deep, it's not that deep. What? Come on. Hold on. Hold on.  Hold on, look at it. See, look, nothing's going on. Nothing's going on. Nothing's going on. Nothing's going on, Bill. Calm down. Where are you going? Why are you on a bed? I was just, girl, what were you doing? You can't be mad. Look, nothing. At what? You just can't be mad, bro. It's just, it's not that deep. You're kidding me.  Bill, stop, back off. What did you do? No, tell me what you did. Nothing happened. Tell me what you did. I'm dead. We were just talking in here.  We were just hearing things. I was not here. Danny! You were not hearing. You literally went and opened the door. Calm down. I'm not going to calm down. Bill, calm down. Bro, what are you doing?  Look at me down the aisle. What did you do? Tell me what you did.  We're done. We're done. I don't know what you did in here. We were just, it's our first time in here, bro.  I was getting to know her a little bit. You were getting to know her a little bit. We're dead. What did you do? Did you not hear them? Did you hear anything? You were knocking the door. You said getting to, you having fun? You might know my girlfriend, bro. This is the first time we meet.  Like it's like nothing? It's not that deep. I told you it's not that deep, bro. No, back up. Come here. Tell me. Back away. It's not a big deal. It was both of our consent. Consent to do what?  It's not just his fault. It's my fault, too. My dude, you guys. It's not my fault. You called him in. I just don't- You don't even know what we did, though, so. I heard knocking in the other room and I was wondering what you guys were doing and then all of a sudden I knew you guys talking about- I mean, I was just- I don't- I heard- It wasn't that deep. I heard that. I heard the bed was moving and hitting the wall. Be careful. Why am I going to be careful? Am I not supposed to be freaking out right now? Like, do you realize what this means? Well, it doesn't matter. We're leaving. Like, we're going home. We just got here, dude. We're left. It's not a big deal. First night, last night. Like, nothing happened. Well, nothing even happened. Explain to me what exactly happened before I literally leave. Then bye. I had your car. I'm going to go. Okay, then leave now. No, you're coming with me. I'm not letting you stay with this. You were accusing us of something that we didn't do. It's because we were in here and- I've heard everything. Yeah, we were making- I was right outside the door. You wouldn't even let me in. We were moving stuff. This dude's out of breath when I walk in the- Why are you laughing? It's not funny. You think it's funny?  Literally. Don't look at me like- Then leave, bye. I'm not leaving you with him. Are you kidding me? No, you're going home too.  Just wait, just wait for Sunday. It's not what we do.  It's not that deep. Stop saying that. That's what he said. That's what I heard when you were in here. You think that's funny? You know, you feel- It's deep, but it wasn't that- Right, we're leaving. Bro, folks, just get off of me, folks.  Bill, it's a prank. Calm down, bro. No, I was out of breath because I was doing this. Yes. That's why I'm out of breath. Yeah, I want to see if it was- No, we were, we were, we were filming it. So, why would I be filming? It's been filming for seven minutes. You can watch it back. We got the proof right here. We do got the proof, Bill. Can you say Team Araya is the best team? Huh? Because you just got- He's going to beat me fast, too, Bill. I was going to- I was going to- My god, Bill. He's going to beat my- She believed it. I was even going to attempt to- Honestly, I didn't- Honestly, I did cry. It's just bad because this bed sound legit. I heard that knocking from inside the kitchen. I didn't think it was going to squeak. It's like memory foam, but it was knocking. Oh, yeah, it's knocking. Most of them must have been doing the freaky in the bed before this. It sounds like a- Somebody broke it in. Someone broke that in. But it wasn't us, yeah. It wasn't us. Even though we both had consent. I meet this kid for the first time. Even though we both- And you're on a slide, I welcome it. We both had consent into making this prank. When you said that, that ticked me off. Oh, I knew. It's coming the wrong way. Like I said, it's not that deep. Yeah, it's not that deep. It's not that deep. It was just a surprise. Do you realize that can mean anything from a wide range of nasty to- It's not that deep. Ooh. Bill, just say, team Araya got you even though you don't want it in minutes. Cheating prank on boyfriend? Cheating with the door locked. The door's locked. I thought you were going to kick it down. I was about to- You know what's epic? I was just telling Connor, this is our first day meeting him and we're already doing an insane prank like that.  Oh yeah. Just watch out. Watch out. This is team Araya's prank. Watch out. I'm gonna, I'm gonna concoct something before we leave. I don't think you're able to do that cause I can't be fooled. Like you- Putting a tarantula on my girlfriend while she sleeps.  Yes, yes. I actually told you- I could tell you this prank. I told you I'd break up with him today. Loki, me, and him. You better not have brought a tarantula. I haven't brought a tarantula.    I literally just met her. First night, literally like, we haven't even been with each other for like six hours. Guys, already breaking the fabulous Connor moment. Thank you so much for- Should we do a house? The first time I meet you. Thank you Connor for being in on that. You had to help team Araya out. I had to. I had to. But I don't know the secret. Stay tuned for the tarantula prank on Connor's channel. Are we doing a house tour? I have to show the house right now. Oh, you can do a level floor. So this is the master bedroom that my girlfriend cheated on me in. The first night happened. The magic happened where, you know. Where we're going to start our family. Tony, this is the best bathroom you've ever seen in your life. It really is. It's simple, but it's crazy. Do a 360 of this. Look at the shower. Connor, come in. Come in. Billy, you want a shower? I'm going to keep it with me for a minute. Bro, I hope for you. Bro, great. Danny, come here. Danny definitely could fit too. Well, we could fit a- I mean, everyone, they're both single. They're both single, so. Hit them up, ladies. Who? What? Connor and Danny? Me and Danny. At Counter Bowl Bay on Tinder. On Tinder? All right, what? Airbnb comes with a hallway. That's not a house. See, yeah, you walk down this hallway. Now we got some masterpieces right here. I don't even know what that is. And then, at Counter Bowl Bay's room? Yeah. All right, so then we come into the main room and the living space. And it's, like, A1. They did a good job designing this. And then we got another cool bathroom in here. I love the lights in here. No walk-in shower. The little bright it is in here. So they couldn't come through with a second walk-in shower, but definitely good for Instagram pictures. We're going to try competing with the Hype House for this bathroom, you know, so. Oh yeah, it's going to be the Hype House bathroom. We're going to finish off the tour with the nice, well-designed kitchen space with marble granite tops. Lights? It's fake. It's fake, but it's okay. It looks good still. Yeah, so that's the Airbnb. It's awesome.  And thank you, Connor, again, for being a part of a lit squad. Dream squad. Dream squad. Yeah. Hype squad. It's been fun. We're still here for another day and a half? Yeah. We're here till Sunday, so we have plenty of time to get something planned. Do some other videos. Have fun. We'll see. Subscribe, make sure to give this video a big thumbs up, and we will see you guys in the next video. But until then. Peace. I'm going to say peace out. We go, peace.


Kiora folks. I'm so thankful to be here, thankful to EHF and Teati Awa and the New Zealand government and the rest of its people for this opportunity. It's really been an incredible time here. So my story begins at the intersection of engineering and entrepreneurship. I grew up making my own games and then also working with my siblings here and everything from our own lemonade stands to I was very terrible at sports but was happy to sell candy bars and so I realized now they kept me on for fundraising but we you know exploring this this domain you know really had a blast but things didn't really get kind of properly weird until I met my co-founder Eric Gradman. We're both engineers robotics computer science electrical we're also trained clowns and how to engineer clowns found each other I'll never really understand but we did in 2008 get really frustrated with what we were working on it was the world financial crisis we were pretty also frustrated the fact that everybody had all this like increasing screen time and they weren't kind of doing a lot of this right hanging out in real life and so we just started collaborating making interactive art we were making games six player things like this that got people you know meeting each other in new places simple arcade cabinets walls of buttons right but a lot of this stuff was enabled by the fact that computer vision and cheap sensors and all of that stuff was getting really awesome and accessible and we could make something in an evening right or an afternoon right that was that stuff was so hard in the in the past we even filled a room with fog and laser beams my favorite thing about this everybody knows what to do in a room of laser beams and and my co-founder actually ended up meeting his wife in this thing so they you know are getting back together in real life it really worked they had their first kid so I still feel like this is our most effective game but eventually this rock band found out about this band of nerds and we helped them make a music video that was a big crazy Rube Goldberg machine that started like this and ended like this and over the course of it these educators started reaching out to us saying hey that was awesome we use it in our science class and the kids that you know watched it were really inspired and they started you know we made our own Rube Goldberg machine and we were kind of scratching our heads like wow we were just like screwing around like this is a teaching tool and it really you know sort of settled in our brains is something important so we launched a proper circus called two-bit circus and in addition to building tons of crazy things virtual reality for the Olympics and even a cloud that rains tequila we finally said you know we've done a hundred events for other people let's do our own event and rather than just a party's for party's sake let's let's let's do something more important and and you know I think we've all seen over the last few days that there's no shortage of hard problems out there right but there's certainly a shortage of creative passionate folks to go out and solve those things and we were really inspired by this one quote from the US Department of Labor 65% of school children be employed in a job that doesn't exist right that doesn't even mean it will exist so we were very shocked by that and the fact that you know this this board millennials photo I think is incredible these they're not even at school right they're at an amusement park and they're bored right and so we were like gosh you know we really there's a high bar here we got to really engage folks so if it's sort of the old world was this one that was really passive for consumers we really liked the idea of something that was much more active much more creator and and even has been talked about today a co-creator so the you know the term stem science technology engineering and math pretty boring usually you think of a guy like this you know and and we were like but wait it's so awesome to be an engineer there's so much great stuff that you can do you know and we started thinking about somebody like this right a nook with practice incredible a creator you know wearable electronics all sorts of crazy stuff so we felt like stem plus art steam was a very exciting a very exciting term we thought of this guy right Leonardo da Vinci right not only an engineer and a scientist but a creator an artist an inventor so we launched a carnival big traveling carnival steam carnival and the idea was one half all of our high-tech entertainment one half a bunch of hands-on projects to inspire kids about science and engineering the message being hey we built this stuff it's not that hard you can too here's how programming electronics fabrication creativity design thinking so all these different hands-on projects as well as remember the old dunk tank where you throw a ball and you fall in water we were like yeah waters okay fire is awesome and so we you know we would immerse you in a fireball you don't die because you're wearing a fire suit that was great this is my favorite photo in our whole cabin my kids not gonna forget that for long and so we did as we launched a big nonprofit I love the the appreciation and reverence for trash in in the Roja Valley we believe the same thing we collect a bunch of clean waste from companies we package it up as art supplies for schools extra bottle caps extra cardboard all sorts of fun like that we provide the physical tools so that then a kid can make their own games right here's a cardboard arcade cabinet their own big crazy ridiculous machines we have a four part program and we came out to New Zealand a few years ago worked with some educators down in Nelson we're very excited to prototype versions of this approach out here collecting trash getting into all the regions and using that as a really inexpensive supplies for schools and I'm done thanks


Live from the Javits Center in New York City, it's theCUBE, covering Inforum 2017. Brought to you by Infor. Welcome back to theCUBE's coverage of Inforum here at the Javits Center in New York City. I'm your host, Rebecca Knight, along with my co-host Dave Vellante and Jim Covellius, who is the lead analyst for Wikibon in AI. So guys, we're wrapping up day one of this conference. What do we think? What did we learn? Jim, we've been here at the desk interviewing people and we've certainly learned a lot from them, but you've been out there talking to people and off the record, I should say. So give us your impressions. I'm going to name names once I may. I want to clarify something this morning. As I said this morning that the implied valuation was like 3.7, 3.8 billion. Charles Phillips indicated to us off camera, actually it was more like 10 and a half. But I still can't make the math work, so I'm working on that. I suspect because I suspect what's happened was that a pre-debt number. Remember they have a lot of debt. I will figure it out, find out, and report back. So I just wanted to clarify that. Run those numbers, okay. I'll call George. Right, but Jim, back to you. What do you think is the biggest impression you have of the day in terms of where Infor is? Yeah, I've had the better part of this day to absorb the Coleman announcement, which of course, AI is one of my core focus areas at Wikibon, and it really seems to me that, well, Infor's direct competitors in the ERP space are all in cloud, SAP is Oracle's Microsoft. They all have AI investments and strategies going forward in their ERP portfolios. So I was going back and doing my own research today just to get my head around where does Coleman put Infor in the race, because it's a very competitive race. I refer to it this morning maybe a little bit extremely as a war of attrition. But what I see, what I think is that Coleman represents a little milestone in the development of the ERP, cloud ERP market, where with SAP Oracle and Microsoft, they're all going deep on AI and ERP, but none of them has the comprehensive framework or strategy to AI enable their suites for human augmentation, natural language processing, conversational UIs, recommenders in line to the whole experience of inventory management and so forth. What Infor has done with Coleman has laid out more than just a framework and a strategy, but they've got a lot of other assets behind the whole AI first strategy that I think will put them in good stead in terms of innovating within their portfolio going forward. One of which is they've got this substantial infusion of capital from Coke Industries of course, and Coke is very much as we've heard today at this show, very much behind where the Infor team under Charles is going with AI enabling everything. But also the burst team is now on board with Infor, the acquisition closed last month. Brad Peters spoke this morning, and of course he spoke yesterday at the analyst pre-brief, and so David and I have had more than 24 hours to absorb what they're saying about where burst fits in. Is burst has AI assets already? That Infor is very much committed to converging the best of what burst has with where Coleman is going throughout their portfolio. What Infor announced this morning is all of that plus the fact that they've already got some Coleman eyes, that's the term I'm using, applications in their current portfolio. So it's not just a future statement of direction, it's all, they've already done significant development productization of Coleman. And they've also announced a commitment Infor within the coming year to bring, to introduce Coleman features throughout each of the industry vertical suite and cloud suites. Like I said, human augmentation plus automation, plus assistance that are chat boxes in line. Infor has a far more ambitious, and I think potentially revolutionary strategy to really make, to take ERP away from the legacy architectures that have all been based on deterministic business rules that a rickety thicket of business rules that need to be maintained. Bringing it closer to the future of cognitive applications where the logic will be in predictive and determine this predictive data driven algorithms that are continually learning, continually adapting, continually optimizing all interactions and transactions. That's the statement of direction. I think that Infor is on the path to making it happen in the next couple of years in a way that will probably force SAP Oracle Microsoft to step up their game and bring their cognitive or AI strategy. So I want to talk some more about the horses in the track, but I want to still understand what it is. So what the competitors are going to say is, oh, it's Alexa, okay? Okay, so it is partly. Yeah, sure, it's very reductive. That's their job to reduce. Yeah, that's right. You lived that world for a while. Actually, that was not your job. So if you don't understand technology, you're just some very smart guy who talks a good talk. Yeah, okay. So, okay, so what we heard yesterday in the analyst meeting, and maybe you found us out today was, it's conversational UX, so it's chat wired into the APIs, and they said that's table stakes. It augments, it automates. An example is early payments versus my cash on hand. Should I take the early payment deal and take the discount or? And so it helps decide those decisions. And which can, if you have a lot of volume, could be complex, and it advises it uncovers insights.  Good question, good question. Whether it's all organically developed so far or whether they sourced it from partners is an open issue that maybe we- Questions for Duncan tomorrow. We'll ask Duncan tomorrow, exactly. Okay, so who are the horses on the track?  Well, here's the thing, you named at least one of those solution providers, IBM, for example, provides obviously a really sophisticated, cognitive AI suite under Watson that is not embedded, however, within an ERP application suite from that. No, it's purpose built for whatever. It's purpose built for standalone deployment into all manner of applications. What Infor is not doing with Coleman and they made that very clear, they're not building a standalone AI platform. Which strategy do you like better? Do I like it? They're both valid strategies. First of all, Infor is very much a SAS vendor going forward and they haven't given any indications of going into pass. I mean, that's why they partnered with Amazon, for example, you know. So it's clear for a SAS vendor like Infor going forward to do what they've done, which is that they're not going to allow their customers apparently to decouple the Coleman infrastructure from everything else that, you know, Infor makes money on. Which for them is the right strategy. Yeah, that's the right strategy for them. I'm not saying it's a bad strategy for anybody who wants to be in Infor's market. So what is an Oracle or an SAP, or for that matter, a work day? Yeah. Do, I mean, ServiceNow made some AI announcements at their knowledge event. So they're spending money on that. I think that was organic IP or, I don't know, maybe they're open source AI components. Yeah, yeah, sure. A, they need to have a cloud data platform that provides the data upon which to build and train the algorithm. Clearly, Infor has casted a slot with AWS, you know, SAP Microsoft, Oracle IBM. They all have their own cloud platform. So GT Nexus plays into that data corpus or? Yeah, because GT Nexus is very much a commerce network, you know, in other words, EDI for this century. That is a continual, free-flowing, ever-replenishing pool of data upon which to build and train and train the algorithm. Okay, but I get you're wrong. You said number one, you need the cloud platform with data. Yeah, you need the conversational UI, the, you know, I'll use a reductive term, chat box. You know, digital assistance. You need that technology. And it, you know, it's very much a technology in the works. It's not like everybody's building chatbots. It doesn't mean that every customer is using them or that they perform well. But chatbots are at the very heart of a new generation of application development, conversational interfaces, which is why Wikibon, why we are doing a study on the art of building and training and tuning chatbots. Because they are so fundamental to the UX of every product category in the cloud. And non-cloud as well. And only getting more so. IOT, right, you know, desktop applications, everything's going with a, moving towards more of a conversational interface. You know, for starters.  You need obviously machine learning and deep learning capabilities, you know, open source. We're looking at a completely open source stack in the middle there for all the data. You know, you need obviously things like TensorFlow for deep learning, which is becoming the standard there. You need things like Spark, you know, for machine learning, streaming analytics and so forth. You need all that plumbing to make it happen. But you need, in terms of an ERP of course, you need business applications. And you need to have a business application stack to infuse with this capability. And there's only a hard core of really dominant vendors in that space that we've named them all. But the precious commodity seems to be data. Yeah. Right? The precious commodity is data both to build the algorithms and to an ongoing basis to train them. See, the thing is training is just as important as building the algorithms, because training makes all the differences in the world between whether a predictive analytics, ML algorithm actually predicts what it's supposed to predict or doesn't. So without continual retraining of the algorithms, they'll lose their ability to do predictions and classifications and pattern recognitions. So, you know, the vendors in the cloud arena who are in a good place are the Googles and the Facebooks and others who generate this data organically as part of their services. Google's got YouTube. And YouTube is a motherload of video and audio and so forth for training all the video analytics, all the speech recognition, everything else that you might want to do. But also very much, you know, when you look at natural language processing, you know, text data, social media data. I mean, everybody is tapping into the social media fire hose to tune all the NLP ongoing. That's very, very important. So the vendor that can assemble a complete solution portfolio that provides all the data. And also very much, this is something people often overlook. Training the data involves increasingly labeling the data and labeling needs a hard core of resources, increasingly crowdsourced to do that training. That's why companies like CrowdFlower and Mighty AI, and of course Amazon with Mechanical Turk are becoming ever more important. They are the go-to solution providers in the cloud for training these algorithms to keep them fit for purpose. All right, Rebecca, what are your thoughts as a sort of newbie to them for and in the forum? I'm a newbie, yes, and well, to be honest, yes, I'm a newbie and I have only an inch wide, an inch deep understanding of the technology. But one thing that has really resonated with me. You think it really well. Well, thank you, I appreciate that, thank you. That I've really taken away from this is the difficulties of implementing this stuff. And this is what you hear time and time again, is that the technology is tough, but it's the change management piece that is what trips up these companies because of personality, personalities who are resistant to it and just the entrenched ways of doing things. It is so hard. Yeah, if you change management, yes, I agree. There's so many moving parts in these stacks. It's incredible. If we just focus on the moving parts that represent the business logic that's driving all of this AI, that's a governance mess in its own way because what you're governing, I mean version controls and so forth, are both traditional business rules that drive all these application code, plus all these predictive algorithms, model governance and so forth and so on. I mean, just making sure that all of that is you're controlling versions of that. You've got stewards who are managing the quality of all that, that it moves in lockstep with each other, so when you change the underlying coding of a chatbot, for example, you're also making sure to continue to refresh and train and verify that the algorithms that were built along with that code are doing their job and so forth. In other words, I'm just giving this sort of this metadata and all that other stuff that needs to be managed in a unified way within what I call a business logic governance framework for cloud, data-driven applications like AI. And in companies that are so big and where people are so disparately located, that is, these are the biggest challenges that companies are facing. Yeah, you're going to get your data scientists in, let's say, China to build the deep learning algorithms and probably to train them. You're probably going to get coders in Poland or in Uruguay or somewhere else to build the code and over time, and there'll be different pockets of development all around the world, collaborating within a unified DevOps environment for data science. Another focus, by the way, DevOps for data science. Over time, these applications, like any application, it'll be year after year after year of change. The people who are building and tuning and tweaking this stuff now probably weren't the people five years ago, as this stuff gets older, who built the original, so you're going to need to manage the end-to-end lifecycle documentation and change control and all that. It's a DevOps challenge ongoing within a broader development initiative to keep this stuff from flying apart from the sheer complexity. So, just, I don't know if, Jim, if you can help me answer this, this might be more of a floor issue, but when we heard from the analysts meeting yesterday, Soma, their chief technical guy, who's been on theCUBE before in New Orleans, very sharp, dude, two things that stood out. Remember that architecture slide they showed? They showed the slide of XI and the architecture. And obviously they're building on AWS Cloud. So, their greatest strengths are where they're, in my view, anyway, the Achilles heel is here. And one is Edge, let's talk about Edge. So Edge to Cloud, very expensive to move data into the cloud. And that's where we heard today that all the analysis is going to be done. We know that. You're really only going to be moving the needles, presumably, into the cloud. The haystack's going to stay at the Edge and the processing's going to be done at the Edge. It's going to be interesting to see how Amazon plays there. We've seen Amazon make some moves to the Edge with Snowball and Greenfield and things like that. But it just seems that analytics are going to happen at the Edge, otherwise it's going to be too expensive. The economic model doesn't favor Edge to Cloud. Sort of one sort of caveat. The second was the complexity of the data pipeline. So we saw a lot of AWS in that slide yesterday. I mean, I wrote down DynamoDB, Kinesis, S3, Redshift. I'm sure there's some EC2. These are all discrete sort of one-trick pony platforms with a proprietary API. And that data pipeline is going to get very, very complex.  Well, yes, but when you talked to Andy Jassy he said, he said, look, we want to have access to primitive access to those APIs because we don't know what the market's going to do. So we have to have control. It was all about control. But that said, it's this burgeoning collection of at least 10 to 15 data services. So the end-to-end, the question I have is, Oracle threw down the Gauntlet in Cloud. They said they'll be able to service any user request in 150 milliseconds. What is the end-to-end performance going to be as that data pipeline gets more robust and more complicated? I don't know the answer to that, but I think it's something to watch. Can you deliver that in under 150 milliseconds? Can Oracle even do that? Who knows? It's- You can if you deliver more of the actual logic, you know, machine learning and code to the edge. I mean, close to the user, close to the point of decision, yes. Keep in mind that the term pipeline is ambiguous here. On one hand, it refers, in many people's minds, to the end-to-end path of a packet, for example, from source to target application. But in the context of development or DevOps, it refers to the end-to-end lifecycle of a given asset, code or machine learning model and so forth. In the context of data science, in the pipeline for data science, much of the training, the term, the whole notion of training and machine learning model, say, for predictive analysis, that doesn't happen in real time, in line to the actual executing application. That happens, you know, it happens, but it doesn't need, it's not in line in the critical path of the performance of the application. Much of that will stay in the cloud because that's massively parallel processing of, you know, of TensorFlow, graphs and so forth. Doesn't need to happen in real time. What needs to happen in real time is that the, well, the algorithms, like TensorFlow that are trained, we push to the edge and they'll execute, you know, in increasingly nanoscopic platforms like your smart phone and like smart sensors embedded in your smart car and so forth. So most of the application logic, probabilistic, you know, machine learning, will execute at the edge. More of the pipeline functions like model building, model training and so forth, data ingestion, data discovery, that will not happen in real time, but it will happen in the cloud. It need not happen in the edge.  And this is the thing, there's going to be a lot more to talk about. We love speaking out here, Rebecca. We apologize for that. We do, we do indeed. It's okay, it's okay. Dave asked, Dave indulges me. He humors me. No, you love it too. Of course, no, I mean, I learn every time I try to describe these things and get smart people like Jim to help unpack it. And we'll do more unpacking tomorrow at day two of Inform 2017, where we will all return. Jim Cobelius, Dave DeValante, I'm Rebecca Knight. We will see you back here tomorrow for day two.


This is Brett, I'm at the 2020 Chicago Auto Show and today I'm checking out just whatever vehicle that I can on the floor, anyone that looks kind of cool and intrigues me a little bit. So today I'm doing a video on this 2020 Hyundai Veloster N. This is a 3-door, has 2-liter turbocharged horse cylinder motor which pumps out 250 horsepower. This color is called Performance Blue. I'm just gonna do a walk around and as soon as everybody gets out of it I'm gonna do some interior photos or videos I should say. Pretty cool looking car. It's gonna come with the Corelli 19 inch painted or Corelli tires, the 19 inch painted polished aluminum rims and these are 235 R19s on here. Four-wheel disc brakes, has a little bit of the carbon fiber trim on the rear wing, the dual rear exhaust has the red accents below and throughout on the brakes, rear bumper. Shoot all of our videos in 1080p, 60 frames per second. So if you have HD capabilities on your computer tablet or smartphone device, turn them on right now because it's like you're right at the Chicago Auto Show with me, even though you're not actually here. Gloss black grille, you get the N logo on there, LED headlamps and running lights and I presume maybe fog lamps, maybe not. This car comes in the N comes in four different colors, chalk white, Performance Blue, racing red and ultra black and this one is the Performance Blue. We'll get inside in just a second here. All right we're gonna check out the interior now. First thing you notice is the color matched Performance Blue seat belts with the Performance Blue stitching on the bucket seat driver seat height adjuster with lumbar, got the Veloster N floor mats, power windows, power locks and power mirrors.  Pretty simple, what you'd expect in a compact car, auto headlamps, steering wheel has the black dash or the black trim on it and you do get the blue stitching on there as well. Blue link up there. And we'll check out the other side, looks like it does have infinity sound system and you get the N kickplate there. Back storage area, pretty decent amount of space back here. I was lucky I have a subwoofer. It's really neat about this car as it is a three-door so to get to the back, this door opens up like so. These seats do fold down. Looks like there's pretty decent seating back here but it is only a two-seater. Pretty good looking car, hope it was a good walk around for everybody. Thanks for checking out the video, hope you liked it. Remember to like, subscribe and share on our channel, in fact in a second you'll see a link to subscribe to our YouTube channel on your left. A link to more 2020 Chicago auto show videos like this one on your right and if you want to check out more, if you want to check us out at our website and a link to that on the bottom. Thanks for checking out the video on this 2020 Hyundai Veloster N in performance blue. Thanks again.