 From Milpitas, California, at the edge of Silicon Valley. It's theCUBE, covering autonomous vehicles. Brought to you by Western Digital. Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Autotech Council Autonomous Vehicle Event at Western Digital. Part of our data makes possible program with Western Digital where we're looking at all these cool applications and a lot of cutting edge technology that at the end of the day, it's data dependent and data's got to sit somewhere. But really what's interesting here is that the data and more and more the data is moving out to the edge and edge computing and nowhere is that more apparent than in autonomous vehicles. So we're really excited to have maybe the best title at Western Digital, I don't know. Chris Berge, VP of Product Marketing. That's not so special, but all the areas that he's involved with mobile, compute, automotive, connected homes, smart cities and if that wasn't enough, industrial IOT. Chris, you must be a busy guy. Hey, we're having a lot of fun here. This data world is an exciting place to be right now. So we're here at the autonomous vehicle event. We could talk about smart cities, which is pretty interesting, actually ties to it and internet of things and industrial internet. But what are some of the really unique challenges in autonomous vehicles that most people probably aren't thinking of? Well, I think that we all understand that really all autonomous vehicles will be made possible by just the immense amount of sensors that are being put into the car. Not much different than as our smartphones or our phones evolved from really not having a lot of sensors to today's smartphones of many, many sensors, whether it's sensing your face, gyroscopes, GPS, all those kinds of things. The cars having the exact same thing happen, but many, many more sensors. And of course, those sensors just drive a tremendous amount of data. And then it's really about trying to pull the intelligence out of that data. And that's really what the whole artificial intelligence or autonomous is really trying to do is, okay, we've got all this data. How do I understand what's happening in the autonomous vehicle in a very short period of time? Right. And there's two really big factors that you've talked about and some of the other things that you've done. I did some homework and one of them is the metadata around the data. So there's the raw data itself that's coming off those sensors, but the metadata is a whole another level and a big level. And even more importantly is the context. What is the context of that data? And without context, it's just data. It's not really intelligence or smarts or things you can do anything about. So that baseline sensor data gets amplified significantly in terms of actually doing anything of that information. That's correct. I mean, I think one of the ones that examples I give, it's easier for people to understand is surveillance. We're very familiar with walking into a retail store where there's surveillance cameras and they're recording in the case that maybe there's a theft or something goes wrong. But there's so much data there that's not actually being processed. How many people walked into the store? What was the average time a person came to the store? How many men? How many women? That's the context of the data. And that's what really would be very valuable if you were, say, an owner of the store or a regional manager. So that's really pulling the context out of the raw data. And in the car example, autonomous vehicles, you know, hey, there's gonna be something, my sensor's seeing something. And then of course you use multiple sensors. That's the sensor fusion between them of, hey, that's a person, that's a deer. Oh, don't worry, that's a car moving alongside of us and he's staying in his lane. Those are the types of decisions we're making with this data and that's the context. Right, and even they had in the earlier presentation today the reflection of a car off the side of a bus. I mean, these are the nuanced things that aren't necessarily obvious when you first start exploring. Well, and we're dealing with human life. I mean, so obviously it needs to be right 99.999 plus percent. So that's the challenge, right? It's the corner cases. And I think that's what we see with autonomous vehicles. I mean, it's really exciting to see the developments going on and of course there's been a couple challenges but we just have so much learning to do to really get to that the fifth nine or whatever it is from a probability point of view. And that's where we'll continue to work on those corner cases. But the technology is coming along so fast. It's mind-boggling how quickly we are starting to attack these more difficult challenges and we'll get there but it's gonna take time like anything. Right, the other really important thing, right? Especially now we're in the age of kind of the rise of cloud, if you will. Amazon is going bananas, Google Cloud Platform, Microsoft Azure. So we're seeing this huge move of cloud and enterprise IT. But in a car, right? There's this little thing called latency and this other thing called physics where you've got a real issue when you have to make a quick decision based on data in those sensors when something jumps out in front of the car. So really kind of the rise of edge computing and moving so much of that store, compute and intelligence into the vehicle and then deciding what goes back to the car to retrain the algorithm. So it's really a shift to kind of back out to the edge, if you will, dependent because of this latency issue. Yeah, I mean, they're very complimentary, right? But there's a lot of decisions you can make locally and obviously there's a lot of advantages in doing that, latency being one of them but just cost of communications. And again, what people don't necessarily understand is how big this data is. You know, you see statistics are out there, one gigabit per second, two gigabits per second. I mean, that is just massive data. At the end of the day, actually in some of the development, it's pretty interesting that we have the car developers actually FedExing the terabyte drives that they've captured data because it's the easiest way for them to actually transfer the data. I mean, people think, oh, internet connectivity, no problem. You try to ship 80 terabytes in a cost effective manner, FedEx ends up being the best shot right now. So it's pretty interesting. Oh, sneaker, that. That is pretty funny. But the quantities of this data are so big. I was teasing you on Twitter earlier today. I think we took it up to an exabyte, a Zettabyte, a Yodabyte, and then the crowd responded. No, it's a Brontosaurus byte is even bigger than a Yodabyte. But it's just, we were at Flink Forward earlier this week and really this whole idea of stream processing. It's really taking new approaches to data processing. You'd be able to take all that stuff in in real time, which probably stayed in the market now as financial trading and advertising markets. But to do that now in a car where if you make a mistake, there's really significant consequences is a really different challenge. It is. And again, that's really this advent of the sensor data. The sensor data is going to swamp probably every other data set that's in the world. And, but a lot of it's not interesting because you don't know when that interesting event's going to happen. So what you actually find is that you try to put as intelligence as close as you can to the data and storage, and again, storage may be 30 seconds. If you had an accident, you want to be able to go back 30 seconds. It may be lifetimes. So just thinking about these data flows and kind of what's the half life of the data relative to the value. But what we're actually finding with many of the machine learning is that people that we, data we thought was not valuable. Data we thought, oh, we have the right amount of granularity. Now with machine learning, we're going back and saying, oh, why didn't we record at even higher granularity? We could have pulled out more of these, more of these trends or more of these corner cases. So, you know, I think that's one of the challenges enterprises are going through right now is that everyone's so scared of getting rid of any data, yet there's just tremendous data growth. So, and we're sitting right here in the middle of it as Western Regional. Well, thankfully for you guys, right? You're going to straw that data. And it is really important though, because it used to be a, funny to me, it used to be a sample of things that happened in the past. It's how you would make your decisions. Now it's not a sample, it's all of what's happening now. And hopefully you can make a decision while you still have time to have an impact. So it's a very different world, but sampling is going away when, in theory, you don't know what you're going to need that data for. And you have the ability to store it. Yeah, making real-time decisions, but then also learning how to use that decision to make better decisions in the future. That's really where Silicon Valley is focused right now. All right, Chris. Well, you're a busy guy, so we're going to let you get back to it because you also have to do IoT and industrial internet and mobile and compute. And I try to eat in between there too. And hopefully see your kids Friday night, so hopefully you can take a wipeout to a movie tonight. All right, Chris, great to see you. Thanks for taking a few minutes. Thank you very much. All right, I'm Jeff Frick. You're watching theCUBE from Autotech Council Autonomous Vehicle Event. Thanks for watching.