 So, you are CUBE alumni from Silicon Valley. It's theCUBE, covering Google Cloud Next 17. Hey, welcome back everyone. We're live here in the Palo Alto Studio for theCUBE, our new 4,500 square foot studio, just moved into a month and a half ago. I'm John Furrier here, breaking down two days of live coverage in studio of Google Next 2017. We have reporters and analysts in San Francisco on the ground. Getting all the details, we had some call-ins. We're also going to call in the end of the day to find out what the reaction is to the news, the keynotes and all the great stuff on day one, certainly day two tomorrow, here in the studio as well as in San Francisco. My next guest is Tyler Bell, good friend, industry guru, IOT expert. They've been doing a lot of work with IOT, but also has a big data background. He's been on theCUBE before. Tyler, great to see you and thanks for coming in today. Thanks, great to be here. So, data has been in your wheelhouse for a long time, you're a product guy and the cloud is the future hope that's happening big time. Data at the edge with IOT is certainly part of this network transformation trend and certainly now machine learning and AI is the now the big buzzword AI, kind of a mental model. Machine learning, using the data. You've been at the front end of this for years with data at Factual and Mapbox, your other companies you work for. Now you have data sets. So before it was like a ton of data and now it's data sets and then you got the IOT edge, a car, smart city, a device. What's your take on the data intersecting with the cloud? What are the key paradigms that are colliding together? Yeah, there's, I mean the reason IOT is so hot right now is really because it's connecting a number of things that are also hot. So together you get this sort of conflagration of fires, technology fires. So one side you've got massive data sets, just huge data sets about people, places and things that allow systems to learn. So on the other end you've got basically large scale computation, which isn't only just available but it's actually accessible and it's affordable. And then on the other end you've got massive data collection mechanisms. So this is anything from the mobile phone that you'll hold in your pocket to a LiDAR laser based sensor on a car. So this combination of massive sort of hardware derived data collection mechanisms combined with a place to process it on the cloud, do so affordably in addition to all the data means that you get this wonderful combination of the advent of AI and machine learning and basically the development of smart systems. And that's really what everybody's excited about. It's kind of intoxicating if you think about it. I mean from a computer science standpoint this is the nirvana we've been thinking about for generations with the compute now available we have. It's just kind of coming together. What are the key things that are emerging in your mind? Because you've been doing a lot of this big data stuff. When I say big I mean like large amounts, large scale data but as it comes in, you know, as they say the world's the future's here but it's evenly distributed. You can also say that same argument for data. Data is everywhere but it's not evenly distributed. So what is some of the key things that you see happening that are important for people to understand with data in terms of using it, applying it, commercializing it, leveraging it. Yeah, it's what you see or what you have seen previously is the idea of data in many people's minds has been a database or it's been sort of a CSV file of rows and columns and it's been the sort of fixed entity. And what you're seeing now is that and that's sort of known as structured data. And what you're seeing now is the advent of data analytics that allow people to understand and analyze loose collections of data and begin to sort of categorize and classify content in ways that have people haven't been able to do so previously. And so whereas you used to have just a database of sort of all the places on the globe or a whole bunch of people, right now you can have information about say the images that the camera sensors on your car sees and because the systems have been trained about how to identify objects or street signs or certain behaviors and actions, it means that your systems are getting smarter. And so what's happening here is that data itself is driving this trend where hardware and sensors, even though they're getting cheaper and they're getting increasingly commoditized, they're getting more intelligent. And that intelligence is really driven by fundamentally it's driven by data. Well I was having a conversation yesterday at Stanford, there was a conference going on around bias and data. Algorithms now have bias, gender bias, male bias. But it brings up this notion of programmability. And one of the things that some of the early thinkers around data including yourself and also you extend that out to IoT is how do you make data available for software programs for the learning piece? Because that means that data is now an input into the software development process, whether that's algorithms on the fly being developed in the future or data being a part of the software development kit if you will. What is that fantasy? Is that gettable? Is that in reach? Is it happening? Making data part of that agile process not just a call to a database. Exactly and a lot of the things the most valuable assets now are called basically labeled data sets where you could say that this event or this photo or this sound even has been classified as such. And so it's the bark of a dog or the ring of a gunshot. And those labeled data sets are hugely valuable in actually training systems to learn. The other thing is if you look at it from say AV which has a lot in common with IoT but the data set is less about a specific sort of structure or labeled event or entity. And instead it's doing something like putting, there's one company where you can put your camera on the dashboard of your car and then you drive around. And all this does is just records the images and records which way your car goes and that's actually collecting and learning data. And so that kind of information is being used to teach cars how to drive and how to react in different circumstances. And so on one hand, you've got this highly structured labeled data. On the other hand, it's almost sort of machine behavioral data where to teach a car how to drive, cars need to understand what that actually entails. One of the things we're talking about Google next earlier in the day in a couple earlier segments, I was talking about that and this has a criticism to the enterprise when I was just saying, Google might want to throttle back their messaging or their concepts because the enterprise kind of works at a different pace. Google is just this high energy. I won't say academic but they're working on cutting it stuff and they have things like maps and they're doing things that are just really off the charts technically, it's just great technical prowess. So there's a disconnect between enterprise stuff and what I call pure Google cloud. The question that's now on the table is now with the advent of IoT, industrial IoT in particular, enterprises now have to be smarter about analog data meaning like the real world. How do you get the data into the cloud from a real world perspective? Do you have any insight on that? I mean, it's something that's hard to kind of get but you mentioned that cam on the car, you're essentially recording the world. So that's the sky, that's not digitized. You're digitizing an analog signal. Yeah, that's right. I think I'd have two notes there. The first is that everything that's sort of going on that's exciting is really at this nexus between the real world that you and I operate in now and sort of how that's captured and digitized and actually collected online so it can be analyzed and processed and then affected back in the real world. And so when you hear about IoT and cars, of course, there are sensors which basically do a read type analysis of the real world but you also have effectors which change it and servos which turn your tires or affect the acceleration or the braking of a vehicle. And so all these interesting things that are happening now and it really kicked off, of course, with the mobile phone is how the online sort of data centric electric world connect with the real world. And all of that's really being, all that information is being collected is through sort of an explosion of sensors because you just have sort of the mobile phone supply chains are making cameras and barometers and magnetometers. All of these things are now so increasingly inexpensive that when people talk about sensors, they don't talk about $1,000 sensor that's designed to do one thing. Instead, there's thousands of $1 sensors. So you've been doing a lot of work with IoT and almost the past year you've been out in the IoT world. Thoughts on how the cloud should be enabled or set up for ingesting data or to be architected properly for IoT related activities, whether it's edge data store or edge data. I mean, we have little things that this boring is backup and recovery are impacted by the cloud. I can imagine that the IoT world as it collides in with IT is going to have some reinvention, reconstruction thoughts on what the cloud needs to do to be truly IoT. Yeah, it's, there's some very interesting things that are happening here and some of them seem to be in conflict with each other. So the cloud is a critical part of the IoT sort of entire stack. And it really goes from the device of the sensor all the way to the cloud. And what you're getting is you are getting providers including Google and Amazon and SAP and there's over 370 last count IoT platform providers which are basically taken their particular skill set and adjusted it and tweaked it. And they now say that we now have an IoT platform. And in traditional sort of cloud services the distinguishing features are things like being able to have sort of digital, record digital state of sensors and devices, sort of shadow states, an increased focus on streaming technology over MapReduce batch technology which you got in the last 10 years through sort of the big data movement and the conversations that you and I have had previously. So there is that focus on streaming. There is a IoT specific sort of feature stack. But what's happening is that because so much data is being corrected let's imagine that you and I are doing something where we're monitoring the environment using cameras and we have 10,000 cameras out there. And this could be within a vehicle, it could be in a building or a smart city or in a smart building. Cameras, the cloud traditionally accepts data from all these different resources be it mobile phones or terminals and collects it, analyzes it and spits it back out in some kind of consumable format. But what's happening now is that IoT and the availability of these sensors is generating so much data that it's inefficient and very expensive to send it all back to the cloud. And so all of these- And there's physics too. There's a lot of physics, right? Exactly. And I mean all these cameras sending full raster images and videos back to the cloud for analysis basically the whole idea of real time goes away if you have that much data you can't analyze it. So instead of just the cameras sending a single dumb raster image back, you teach the camera to recognize something. So you could say, I recognize a vehicle in this picture or I recognize a stop sign or a street light. And instead of sending that image back to be analyzed on the cloud, the analysis is done on the device and then that entity is sent back. And so the sensor says, I saw this stop sign at this point at this time in my process. So this gets back to the earlier point you were making about the learning piece and the libraries and these data sets. Is that kind of where that thread connects? Exactly. So to build the intelligence on the device that intelligence happens on the cloud. And so you need to have the training sets and you need to have massive GPUs and huge computational power to instruct. Thanks, Intel and Nvidia, we need more of those, right? Indeed. And so that's what's happening on the cloud and then those learnings are basically consolidated and then put up on the device. And the device doesn't need the GPUs but the device does need to be smart. And so if you, in IOT, I mean, especially look for companies that understand, especially hardware companies, that understand that the product is such is no longer just a device. It's no longer just a sensor. It's an integral combination of device, intelligence platform in the cloud and data. So talk about the notion of, let's talk about the reconstruction of some of the value creation or value opportunities with what you just talked about. Because if you believe what you just said, which I do believe it's right on the money, that this new functionality vis-a-vis the cloud and the smart edge and learning edge and software is going to change the nature of the apps. So if I'm a cloud provider, like Google or Amazon, I have to then have the power in the cloud but it's really the app game. That's the software game then that we're talking about here. It's the apps themselves. So yeah, you might have an atom processor has two cores versus 72 cores and Xeon and the cloud. Okay, that's a device thing but the software itself of the app level changes. Is that kind of what's happening? What's your, I mean, where's the real disruption? I guess what I'm trying to get at is that, is it still about the apps? Yeah, so I tend not to think about apps much anymore and I guess if you talk to some BCs they won't think about apps much anymore either. It's rather, it tends to, and you and I still think, and I think so many of us in Silicon Valley still think of mobile phones as being sort of the endpoint for both data collection and data effusion. But really one of the exciting things about IoT now is that it's moving away from the phone. So it's vehicles, it's the sensors in the vehicles, it's factories and the sensors in the factories and smart cities. And so what that means is you're collecting so much more data but also you're also being more intelligent about how you collect it. And so it's less about the app and it's much more about the actual sort of intelligence that's baked into the Silicon layer or the firmware of the device. Yeah, I try to get you on our Mobile World Congress special last week and we're just booked out but I know you go to Mobile World Congress, you've been there a lot. 5G was certainly a big story there. They had the new devices on the new LG phone and phones for all the sexy glam. But the 5G and the network transformation becomes more than the device so you're getting at the point which is it's not about the device anymore. It's beyond the device, more about the interplay between the back and the network. It is, it's the full stack but also it's not just from one device. Like the phone is one human, one device and then that pipeline goes into the cloud usually. The exciting thing about IoT and the general direction that things are moving now it's what can thousands of sensors tell us? What can millions of mobile phones driven over a hundred million miles of road surface? What can that tell us about traffic patterns or our cities? So the general trend that you're seeing here is that it's less about two eyeballs than one phone and much more about thousands and millions of sensors and then how you can develop data-centric products built on that configuration of all of that data coming in and how quickly you can build them. We're here with Tyler Bell, IoT expert but also a data expert, good friend and we both have kids who play lacrosse together who are growing up in front of our eyes but let's talk about them for a second Tyler because they're going to grow up in a world where it's going to be completely different. So kind of knowing what we know and as we kind of like tease out the future and connect the dots. What are you excited about this next generation shift that's happening? I mean, if you could tease out some of the highlights in your mind as our kids grow up, right? I mean, you're going to start thinking about, I mean, the societal impact from algorithms that might have gender bias or smart cities that need to start thinking about services for residents that require certain laning for autonomous vehicles or we'll transfer, here's not going to go out here, certainly car buying might shift. They're in cloud native, they're digital native. What are you excited about about this future? Yeah, I think it's the thing that's, I think, so huge that I have difficulty looking away from it is just the impact, the societal impact that autonomous vehicles are going to have. And so really as we, not only as our children grow up but certainly their children, our grandchildren will wonder how in the heck we were allowed to drive massive metal machines and just anywhere. With no software. With really just our eyeballs and our hands and no guidance and no safety. Safety is going to be such a critical part of this but it's not just the vehicle, although that's what's getting everybody's attention right now, it's really what's going to happen to parking lots in the cities. How are parking lots and curbsides going to be reclaimed by cities? How will accessibility and safety within cities be affected by the ability to, at least in principle, just call an autonomous vehicle anytime, have it arrive at your doorstep and take you where you need to go. What does that look like? It's going to change how cars are bought and sold, how they're leased, it's going to change the impact of brands, the significance of, are these things going to be commoditized? But ultimately, I think in terms of societal impact, we have for generations grown up in an automotive world and we will, our grandchildren will grow up in an automotive world but it will be so changed because it will impact entirely what our cities and our urban spaces look like. The good news is when they take our driver's license away when we're 90, we'll at least be able to still get into a car. There's places we can go. We can still drive. Exactly, exactly, the timing is right. We may not have immortality but we will be able to get from one place to another in our senility. We might be a demographic to buy a self-driving car. Hey, you're over 90, you should buy a self-driving car. Well, it'll be more like a consortium. Like you, I, and maybe 30 other people, we have access to a car. A whole new man cave definition of a spring to the autumn. Tyler, thanks for sharing the insight. I really appreciate the color commentary on the cloud, the impact of data, appreciate it. We're here for the two days of coverage of Google Next here inside the queue. I'm John Furrier. Thanks for watching more coverage coming up after the short break. I'm George.