[Narrator] - You are a CUBE Alumni. (cheerful music) Live, from Silicon Valley, it's theCUBE. Covering Google cloud Next '17 (rhythmic electronic music) >> Welcome back everyone. We're live here in the Palo Alto Studio for theCUBE, our new 4500 square foot studio we 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 at the end of the day to find out what the reaction is to the news, the key-notes, and all the great stuff on Day one and 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, he's 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 long time. You're a product guy, and The cloud is the future hope, it's happening big-time. Data, the Edge, with IOT is certainly part of this network transformation trend. And, certainly now, machine-learning and AI is 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 and Factual and Mapbox, your other companies you worked 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 you take on the data intersecting with the cloud? What are the key paradigms that are colliding together? >> Yeah, I mean the reason IOT is so hot right now is really 'cause it's connecting a number of things that are also hot. So, together, you get this sort of conflagration of fires, technology fires. So, on 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, it's actually accessible and it's affordable. 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, a laser-based sensor on a car. So, this combination of massive, 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 to think about, 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 merging in your mind? 'Cause you've been doing a lot of this big data stuff. When I say big, I mean large amounts, large-scale data. But as it comes in, as they say, the world's, the future's here, but it's evenly distributed. You could also say that same argument for data. Data's everywhere, but it's not evenly distributed. So, what are 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, what you see, or what you have seen previously is the idea of data, in many people's minds, has been a data base or it's been sort of a CSV file of rows and columns and it's been this sort of fixed entity. And what you're seeing now is that, and that's sort of known as structure 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 people haven't been able to do so previously. And so, whereas you used to have just a data base 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 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. >> 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 we extend that out to IOT, is how do you make data available for software programs, for the learning piece? Because that means that data's now an input into the software development process, whether that's algorithms on the fly being developed in the future or data being part of the software development kit, if you will. Is that a fantasy or is that gettable, is that in reach? Is it happening? Making data part of that agile process, not just a call to a data base? >> Exactly, 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 structured 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 machine behavioral data, where to teach a car how to drive, cars need to understand what that actually entails. >> Yeah, one of the things we talked about on Google Next earlier in the day, when we saw a couple earlier segments. I was talking about, I didn't mean this as a criticism to the enterprise, but 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-edge stuff. 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 the 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? it's something that 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 going on that's exciting, is really at this nexus between the real world, that you and I operate in now and 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 affecters 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, data-centric, electric world connect with the real world. And all of that's really, all that information is being collected is through an explosion of sensors. Because you just have, 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 one thousand dollar 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, 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 as boring as 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 and reconstruction. Thoughts on what the cloud needs to do to be truly IOT ready? >> Yeah, 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 entire stack and it really goes from the device of a 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 cloud services, the distinguishing features are things like being able to have record digital state of sensors and devices, sort of 'shadow' states, increased focus on streaming technology over MAP-reduced batch technology, which you got in the last 10 years, through 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 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 smart city, or in a smart building. Cameras are, 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, it's physics, too. There's a lot of physics, right? >> Exactly, and 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 out 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 cuts 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, in IOT, especially look for companies that understand, especially hardware companies, that understand that the product, as 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 'cause if you believe what you just said, which I do believe is right on the money, that this new functionality, vis-a-vis, the cloud, and the smart ads and learning ads, 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, it's the software game 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, at the app level, changes. Is that kind of what's happening? 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 VCs, they won't think about apps much anymore either. It's rather, it tends to, you and I still think, and I think so many of us in Silicone Valley, still think of mobile phones as being the end point 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 intelligence, that's baked into the silicon layer, or the firmware of the device. >> Yeah, I tried to get you on their 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, the new LG phones, 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 at 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 100 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 and one phone and much more about thousands and millions of sensors. And then how you can develop data-centric products built on that conflagration of all of that data coming in. And how quickly you can build them. >> We're here with Tyler Bell, IOT Expert, but also data expert, good friend. 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 tease-out the future and connect the dots, what are you excited about this next generation's shift that happening? If you could tease-out some of the highlights in your mind for, as our kids grow up, right, you got to start thinking about the societal impact from algorithms that might have gender bias, or smart cities that need to start thinking about services for residents that will require certain laning for autonomous vehicles, or will cargo (mumbles). Certainly, car buying might shift. They're 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, 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-- >> John: With no software. >> Yeah, with really just our eyeballs and our hands, and no guidance and no safety. Safety's 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 curb sides 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 at any time, 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 our grandchildren will grow up in an automotive world, but it will be so changed 'cause it will impact entirely what our cities and our urban spaces look like. >> The good news is when they take our drivers licenses 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 (laughs) >> Exactly, exactly, the time 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 or fleet. >> A whole new man cave definition to bring to the auto,. Tyler, thanks for sharing the insight, really appreciated 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 theCUBE. I'm John Furrier, thanks for watching. More coverage coming up after this short break. (cheerful music) (rhythmic electronic music) >> I'm George--