Jeff Rick here with the q''-word in Milpitas California and Western Digital offices for the auto tech council autonomous vehicle meetup about 300 people we're looking at all these cool applications and a lot of cutting-edge technologies at the end of the day it's it's data dependent betas got to sit somewhere but really what's interesting here is that the data and more more the data is moving out to the edge and edge computing and nowhere is that more apparent than in autonomous vehicles Preet SIA [Music] [Applause] the technologies that Silicon Valley is famous for inventing cloud-based technology network technology artificial intelligence machine learning historically those may not have been important to a car maker in Detroit so well that's great we had to worry on our transmission and make these ratios better and that era is still with us but they've layered on this extremely important software based in technology based innovation that now is extremely important really autonomous vehicle to be made possible by just the immense amount of sensors that are being put in through the car not much different than as our smartphones or our phones evolved sensing your face gyroscopes GPS all the time things so there's the raw data itself that's coming off the sensors but the metadata is a whole nother level in a big level and even more important ladies the context my sensors are seeing something and then of course you used 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 were making with this data masta context last was just about like mapping for autonomous videos which is amazing little subset there's been a tremendous amount of change in one year you know one thing I can say we're at the top it's critically important is we've had fatalities and that really shifts a conversation and and refocuses everybody on the issue is safety we're dealing with human life I mean so obviously it needs to be right 99.999 you know Plus pers read it's all about intelligent decisions and being to do that robustly across all type of operating conditions is paramount that's mission-critical slow motion high precision one to two centimeter accuracies to to be able to maneuver in parking lots be able to back up and driveways those are very very complex situations essentially these learning moments have to happen without the human fatality human cost they have to happen in software in simulations in a variety of the ways that don't put people in the public at risk people outside the vehicle haven't even chosen to adopt those risks and part of the things of getting safety is being much more efficient on the vehicle because you have to do a lot more software in order to be safe across multiple different kinds of examples of streets and locations because of this case notion these new kinds of cars new range of suppliers are coming into play we don't want piston rods anymore you want electric motors we need rare earth magnets to put in our electric motors and that's a whole new range for suppliers even before autonomous there are so many new systems in the car now that generated our consume data if you think about a full autonomous vehicle out there driving not two hours a day like we are driving today like 20 hours a day suddenly the storage requirements are very very different you see statistics aren't out there one gigabit per second two gigabits per second everyone's so scared of getting rid of any data right yet there's just tremendous data growth if we don't design the future storage solutions today what's gonna end up is that people are gonna pay much more for storage just to make it basically skates work the reality is that are we taking care of the grid locks that are affecting our city are we moving around enough people are we solving the problems of congestion I'll say no we took a bus and we divided the bus in section so you have a longer vehicle the peak time when it is high demand and shorter vehicle when there is very low demand when you're just a few passengers and the magic is that when those parts are connected one to another they shared internal space by the way all of that can be done autonomously right and we can suffer tomorrow because we can have a driver when we begin using the system and when the technology allow it has to be autonomous we're gonna run the utmost operating system that and the cost is even lower than a box in the roush human world were used to when somebody crashes the car they learn a valuable lesson and maybe the people around them learn to value lesson I'm gonna be more careful I'm not gonna have that drink when Adam Thomas car gets involved in any kind of an accident tremendous number of cars learned the lesson so as a fleet learning and that les is not just shared among one car it might be all Tesla's or all who burst that's a super good point the AV revolution will also require a revolution in the maintenance and sustenance of our road network not just the United States but everywhere in the world the quality of the roads made all the difference in the world for these vehicles to move around there are so many difficult problems to solve along this path that no company can really do it themselves right and of course you're seeking big companies investing billions of dollars but it's great because everybody's saying let's find people that specialize whether it's for sensors or computer or all the rest of those things get them in partner with them have everybody solved the right problem of their specialized and focused on the technology is coming along so fast it's just 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 like anything right we're kind of hoping nobody goes out there and trips up to mess it up for the whole industry because we believe as a whole this will actually bring safety to the market right but a few missteps can create a backlash as Elon Musk puts it success is one of the possible outcomes right but not necessarily abilities but we're doing that right startups and large companies trying to solve not that thousands of problems but the millions and billions of problems that are gonna have to be solved to really get autonomous vehicles to their ultimate destination which is what we're all hoping for it's gonna save a lot of lives we're at the Auto Tech Council autonomous vehicle event in Milpitas California thanks for watching specialist [Music]

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's The Cube. Covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody, Jeff Frick here with the Cube. We're at Western Digital in Milpitas, California at the Auto Tech Council, Autonomous vehicle meetup, get-together, I'm exactly sure. There's 300 people, they get together every year around a lot of topics. Today is all about autonomous vehicles, and really, this whole ecosystem of startups and large companies trying to solve, as I was just corrected, not the thousands of problems but the millions and billions of problems that are going to have to be solved to really get autonomous vehicles to their ultimate destination, which is, what we're all hoping for, is just going to save a lot of lives, and that's really serious business. We're excited to have the guy that's kind of running the whole thing, Derek Curtain. He's the chairman of the Auto Tech Council. Derek, saw you last year, great to be back, thanks for having us. >> Well, thanks for having me back here to chat. >> So, what's really changed in the last year, kind of contextually, since we were here before? I think last year it was just about, like, mapping for autonomous vehicles. >> Yes. >> Which is an amazing little subset. >> There's been a tremendous amount of change in one year. One thing I can say right off the top that's critically important is, we've had fatalities. And that really shifts the conversation and refocuses everybody on the issue of safety. So, there's real vehicles out there driving real miles and we've had some problems crop up that the industry now has to re-double down in their efforts and really focus on stopping those, and reducing those. What's been really amazing about those fatalities is, everybody in the industry anticipated, 'oh' when somebody dies from these cars, there's going to be the governments, the people, there's going to be a backlash with pitchforks, and they'll throw the breaks on the whole effort. And so we're kind of hoping nobody goes out there and trips up to mess it up for the whole industry because we believe, as a whole, this'll actually bring safety to the market. But a few missteps can create a backlash. What's surprising is, we've had those fatalities, there's absolutely some issues revealed there that are critically important to address. But the backlash hasn't happened, so that's been a very interesting social aspect for the industry to try and digest and say, 'wow, we're pretty lucky.' and 'Why did that happen?' and 'Great!' to a certain extent. >> And, obviously, horrible for the poor people that passed away, but a little bit of a silver lining is that these are giant data collection machines. And so the ability to go back after the fact, to do a postmortem, you know, we've all seen the video of the poor gal going across the street in the dark and they got the data off the one, 101 87. So luckily, you know, we can learn from it, we can see what happened and try to move forward. >> Yeah, it is, obviously, a learning moment, which is absolutely not worth the price we pay. So, essentially, these learning moments have to happen without the human fatalities and the human cost. They have to happen in software and simulations in a variety of ways that don't put people in the public at risk. People outside the vehicle, who haven't even chosen to adopt those risks. So it's a terrible cost and one too high to pay. And that's the sad reality of the whole situation. On the other hand, if you want to say silver lining, well, there is no fatalities in a silver lining but the upside about a fatality in the self-driving world is that in the human world we're used to, when somebody crashes a car they learn a valuable lesson, and maybe the people around them learned a valuable lesson. 'I'm going to be more careful, I'm not going to have that drink.' When an autonomous car gets involved in any kind of an accident, a tremendous number of cars learn the lesson. So it's a fleet learning and that lesson is not just shared among one car, it might be all Teslas or all Ubers. But something this serious and this magnitude, those lessons are shared throughout the industry. And so this extremely terrible event is something that actually will drive an improvement in performance throughout the industry. >> That's a really good, that's a super good point. Because it is not a good thing. But again, it's nice that we can at least see the video, we could call kind of make our judgment, we could see what the real conditions were, and it was a tough situation. What's striking to me, and it came up in one of the other keynotes is, on one hand is this whole trust issue of autonomous vehicles and Uber's a great example. Would you trust an autonomous vehicle? Or will you trust some guy you don't know to drive your daughter to the prom? I mean, it's a really interesting question. But now we're seeing, at least in the Tesla cases that have been highlighted, people are all in. They got a 100% trust. >> A little too much trust. >> They think level five, we're not even close to level five and they're reading or, you know, doing all sorts of interesting things in the car rather than using it as a driver assist technology. >> What you see there is that there's a wide range of customers, a wide range users and some of them are cautious, some of them will avoid the technology completely and some of them will abuse it and be over confident in the technology. In the case of Tesla, they've been able to point out in almost every one of their accidents where their autopilot is involved, they've been able to go through the logs and they've been able to exonerate themselves and say, 'listen, this was customer misbehavior. Not our problem. This was customer misbehavior.' And I'm a big fan, so I go, 'great!' They're right. But the problem is after a certain point, it doesn't matter who's fault it is if your tool can be used in a bad way that causes fatalities to the person in the car and, once again, to people outside the car who are innocent bystanders in this, if your car is a tool in that, you have reconsider the design of that tool and you have to reconsider how you can make this idiot proof or fail safe. And whether you can exonerate yourself by saying, 'the driver was doing something bad, the pedestrian was doing something bad,' is largely irrelevant. People should be able to make mistakes and the systems need to correct those mistakes. >> But, not to make excuses, but it's just ridiculous that people think they're driving a level five car. It's like, oh my goodness! Really. >> Yeah when growing up there was that story or the joke of somebody that had cruise control in the R.V. so they went in the back to fry up some bacon. And it was a running joke when I was a kid but you see now that people with level two autonomous cars are kind of taking that joke a little too far and making it real and we're not ready for that. >> They're not ready. One thing that did strike that is here today that Patty talked about, Patty Rob from Intel, is just with the lane detection and the forward-looking, what's the technical term? >> There's forward-looking radar for braking. >> For braking, the forward-looking radar. And the crazy high positive impact on fatalities just those two technologies are having today. >> Yeah and you see the Insurance Institute for Highway Safety and the entire insurance industry, is willing to lower your rates if you have some of these technologies built into your car because these forward-looking radars and lidars that are able to apply brakes in emergency situations, not only can they completely avoid an accident and save the insurer a lot of money and the driver's life and limb, but even if they don't prevent the accident, if they apply a brake where a human driver might not have or they put the break on one second before you, it could have a tremendous affect on the velocity of the impact and since the energy that's imparted in a collision is a function of the square of the velocity, if you have a small reduction of velocity, you could have a measurable impact on the energy that's delivered in that collision. And so just making it a little slower can really deliver a lot of safety improvements. >> Right, so want to give you a chance to give a little plug in terms of, kind of, what the Auto Tech Council does. 'Cause I think what's great with the automotive industry right, is clearly, you know, is born in the U.S. and in Detroit and obviously Japan and Europe those are big automotive presences. But there's so much innovation here and we're seeing them all set up these kind of innovation centers here in the Bay area, where there's Volkswagen or Ford and the list goes on and on. How is the, kind of, your mission of bringing those two worlds together? Working, what are some of the big hurdles you still have to go over? Any surprises, either positive or negative as this race towards autonomous vehicles seems to be just rolling down the track? >> Yeah, I think, you know, Silicone Valley historically a source of great innovation for technologies. And what's happened is that the technologies that Silicone Valley is famous for inventing, cloud-based technology and network technology, processing, artificial intelligence, which is machine learning, this all Silicone Valley stuff. Not to say that it isn't done anywhere else in the world, but we're really strong in it. And, historically, those may not have been important to a car maker in Detroit. And say, 'well that's great, but we had to worry about our transmission, and make these ratios better. And it's a softer transmission shift is what we're working on right now.' Well that era is still with us but they've layered on this extremely important software-based and technology-based innovation that now is extremely important. The car makers are looking at self-driving technologies, you know, the evolution of aid as technologies as extremely disruptive to their world. They're going to need to adopt like other competitors will. It'll shift the way people buy cars, the number of cars they buy and the way those cars are used. So they don't want to be laggards. No car maker in the world wants to come late to that party. So they want to either be extremely fast followers or be the leaders in this space. So to that they feel like well, 'we need to get a shoulder to shoulder with a lot of these innovation companies. Some of them are pre-existing, so you mentioned Patti Smith from Intel. Okay we want to get side by side with Intel who's based here in Silicone Valley. The ones that are just startups, you know? Outside I see a car right now from a company called Iris, they make driver monitoring software that monitors the state of the driver. This stuff's pretty important if your car is trading off control between the automated system and the driver, you need to know what the driver's state is. So that's startup is here in Silicone Valley, they want to be side by side and interacting with startups like that all the time. So as a result, the car companies, as you said, set up here in Silicone Valley. And we've basically formed a club around them and said, 'listen, that's great! We're going to be a club where the innovators can come and show their stuff and the car makers can come and kind of shop those wares. >> It's such crazy times because the innovation is on so many axis for this thing. Somebody used in the keynote care, or Case. So they're connected, they're autonomous, so the operation of them is changing, the ownership now, they're all shared, that's all changing. And then the propulsion in the motors are all going to electric and hybrid, that's all changing. So all of those factors are kind of flipping at the same time. >> Yeah, we just had a panel today and the subject was the changes in supply chain that Case is essentially going to bring. We said autonomy but electrification is a big part of that as well. And we have these historic supply chains that have been very, you know, everyone's going as far GM now, so GM will have these premier suppliers that give them their parts. Brake stores, motors that drive up and down the windows and stuff, and engine parts and such. And they stick year after year with the same suppliers 'cause they have good relationships and reliability and they meet their standards, their factories are co-located in the right places. But because of this Case notion and these new kinds of cars, new range of suppliers are coming into play. So that's great, we have suppliers for our piston rods, for example. Hey, they built a factory outside Detroit and in Lancing real near where we are. But we don't want piston rods anymore we want electric motors. We need rare earth magnets to put in our electric motors and that's a whole new range of suppliers. That supply either motors or the rare earth magnets or different kind of, you know, a switch that can transmit right amperage from your battery to your motor. So new suppliers but one of the things that panel turned up that was really interesting is, specifically, was, it's not just suppliers in these kind of brick and mortar, or mechanical spaces that car makers usually had. It's increasing the partners and suppliers in the technology space. So cloud, we need a cloud vendor or we got to build the cloud data center ourselves. We need a processing partner to sell us powerful processors. We can't use these small dedicated chips anymore, we need to have a central computer. So you see companies like Invidia and Intel going, 'oh, that's an opportunity for us we're keen to provide.' >> Right, exciting times. It looks like you're in the right place at the right time. >> It is exciting. >> Alright Derek, we got to leave it there. Congratulations, again, on another event and inserting yourself in a very disruptive and opportunistic filled industry. >> Yup, thanks a lot. >> He's Derek, I'm Jeff, you're watching The Cube from Western Digital Auto Tech Council event in Milpitas, California. Thanks for watching and see you next time. (electronic music)

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE, covering autonomous vehicles, brought to you by Western Digital. >> Hey, welcome back here everybody. Jeff Frick here with theCUBE. We're in Milpitas, California at the Western Digital event. It's the Autotech Council Autonomous Vehicle event. About 300 people talking about all these really complicated issues around autonomous vehicles, a wide variety of start ups and enterprises and it's a really interesting space 'cause there's, as somebody said in the keynote there's literally thousands of problems to solve. But one of the angles is really on the public transportation side. Really excited to have a really innovative start up and welcome Emmanuele Spera. He is the CEO of NEXT Future Transportation. Emmanuele, welcome. >> Hi Jeff, thanks for inviting me out. >> Absolutely, so for the folks that aren't familiar, you can go to the website, there's a great demo video that you guys have this session. What are you guys building? >> We're building something very particular, because, so far, you see all those company presenting what is, well known as an autonomous car. So, let's build something that can let us read our newspaper while we are commuting, and very nice, lot of money that's been invested in that. But the reality is that how we... Are we taking care of the gridlocks that are affecting our city? Are we moving around enough people? Are we solving the problem of congestion? I'll say, no. Because it doesn't matter if we have an EV, an autonomous driving vehicle, or an SUV or a car, you still have congestion, you still need to have large number of car to move around people. >> Jeff: Right, right. So the only viable solution is to use buses. Buses has been there in the last hundred years and they are very expensive, actually the most expensive asset that cities and municipalities are using. So they using taxpayer money to pay those asset, and they are underutilized. Because you have a high demand in peak time, so people use buses, but on the rest of the day, when there are no peak time, there is very low usage rate. I'll say around 20, 25%. So take a look at those buses, they are empty all the time. So our solution is about modularizing this kind of transportation. So, literally, we took a bus and we divided the bus in section, so you have six module that are coupled together, are the same length and capacity of a standard city bus. But we do modularization. We can create a system which literally breathe, because we have longer vehicle in peak time when there is high demand, and shorter vehicle when there is very low demand when you have just a few passenger. The magic is that when those pods are connected, one to another, they share the internal space. By the way, all of that can be done autonomously. >> Jeff: Right. The coupling is already done autonomously, and we can start from tomorrow, because we can't have a driver when we begin using the system. When the technology allows us to be autonomous, we're going to run the autonomous operating system on that. >> Jeff: Right. This can be done autonomously now, in close environment when you don't have a mix traffic environment. But we demonstrated that this could be done. >> That's funny that you came at the problem from a bus, and breaking the bus into modular pieces. When I was prepping for our interview, and doing some research, I looked at it more as kind of a combination of a bunch of individual passenger vehicles that then create almost more like a train. But it's the same concept and it made me think of really kind of IP networks where, when you can bring them all together into an autonomous unit and they operate as one. Much more efficient. >> Emmanuele: Exactly. >> They don't need space in between. And then, really an interesting concept where that packet can kind of jump onto another network if it needs to go down another route. So the fact that these things can couple and uncouple, the fact the people can change units within the structure, you're really adding kind of a smart transportation that then can come together and really act like a city bus. Really fascinating way to look at that problem. >> Absolutely, it's simple, so. The technology to create that, if you look at those parts, seem like very far away, but we were able to create this now using off the shelf components. >> Jeff: Right. Literally, when you give people, passenger, an option, this kind of option, they're going to love it. >> Jeff: Right. Think about now when you need to go from point A to point B, you need to take a taxi, ride a bicycle, take new burr, to change and have an intermodal transportation to reach your destination and it's going to take a while to reach your destination. With this system, you just jump on another pod and you change your destination within the same system. >> Jeff: Right. It can all be controlled by an app that you carry or by screen that are on the pods, that tell you you need to go northbound to go on pod number one, you need to go eastbound go on pod number two. >> Jeff: Right. So the system is able to reorganize itself based on the user's needs, literally. >> So, we're here, we're sponsored by Western Digital, this is part of their whole Data Makes Possible program. From a data perspective and a AI perspective, how did you have to approach that problem a little bit differently and what were some of the challenges that enabled you to overcome, to create this unique solution? >> So, before, you were saying that we are all here at this conference and we'll need to solve, like, thousands of problem. We actually have to solve, like, millions of problem, billions of problem, I mean we are... And AI is the only way we can overcome such problem in some area. Obviously we need to take control of the basics, of the beginning of this journey. Clearly the AI will be amazing when the system is fully working and you can predict information, you can connect with the passenger, with the user of the system directly, and predict behavior, predict needs on the passenger side. And then also, you're going to use the AI to predict how the system is flowing, meaning how the vehicle are using the lane, if there are gridlock somewhere, so how you can, on the fly, reorganize the way those vehicles, those pods, are going to move around the city, to go over obstacle and reach a destination faster and ultimately, in our case, where is the best place to couple with another vehicle based on passenger destination and lengths of the journey. >> Right. So, Emmanuele, this isn't just a concept. You guys actually have working prototypes out in the field, so where, how many do you have deployed? What's your road map and hope for kind of a roll out or do you have, is it a partner strategy? What's your plan to scale? >> We had this concept, the company was made in 2015, we were showing this concept in 2016, the beginning of 2017. In one year, we were able to deliver to our first customer, which is the Dubai government. Last February, during the World Government Summit in Dubai, we showcase two full spec vehicle that were able to couple and uncouple autonomously and move around the venue. We had been testing them since January in Dubai, in a closed area or in particular events where we could showcase and have passengers on both and drive them for a small route. Clearly, our solution is not for OEM car maker. It's for municipalities that really need to solve a problem and have been stuck, literally, with the bus in the last hundred year. There have been no major innovation in bus industry. The only innovation I see now, there are electrifying buses, so now you have way more expensive assets which is still underutilized. >> Right. >> So I spend more and it's still no one use it. So what you are doing, we are going to provide fleets to municipalities and Dubai will be the first, especially since they're having their Dubai 2020 Exhibition. We can provide them with a fleet by that time. Think about that, 120 pods are the same as 20 buses. >> Right, right. That's your targeted first deploy, something like that? >> Yeah, exactly, and the cost is even lower than a bus. >> Alright. Well, Emmanuele, it's really cool technology. >> Emmanuele: Thank you, Jeff. >> I just love the innovation in terms of kind of slicing the problem in a slightly different way, being really innovative and partnering. As you said, you have not raised $100 million in all this craziness, and actually deploying, so. Really exciting story, thanks for sharing with it and we're excited to watch it unfold over the next couple of years. >> Absolutely, thank you, Jeff. >> Alright, he's Emmanuele, I'm Jeff. We're at the Autotech Council, part of Western Digital's Data Makes Possible. Thanks for watching, catch you next time. (techno music)

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's the Cube, covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back here ready, Jeff Frick here with the Cube. We're at Western Digital's office in Milpitas, California at the Autotech Council Autonomous Vehicle event. About 300 people talking about all the various problems that have to be overcome to make this thing kind of reach the vision that we all have in mind and get beyond the cute. Way more cars driving around and actually get to production fleet, so a lot of problems, a lot of opportunity, a lot of startups, and we're excited to have our next guest. He's Dave Tokic, the VP of Marketing and Strategic Partnerships from Algolux. Dave, great to see you. >> Great, thank you very much, glad to be here. >> Absolutely, so you guys are really focused on a very specific area, and that's about imaging and all the processing of imaging and the intelligence out of imaging and getting so much more out of those cameras that we see around all these autonomous vehicles. So, give us a little bit of the background. >> Absolutely, so, Algolux, we're totally focused on driving safety and autonomous vision. It's really about addressing the limitations today in imaging and computer vision systems for perceiving much more effectively and robustly the surrounding environment and the objects as well as enabling cameras to see more clearly. >> Right, and we've all seen the demo in our twitter feeds of the chihuahua and the blueberry muffin, right? This is not a simple equation, and somebody like Google and those types of companies have the benefit of everybody uploading their images, and they can run massive amounts of modeling around that. How do you guys do it in an autonomous vehicle, it's a dynamic situation, it's changing all the time, there's lots of different streets, different situations. So, what are some of the unique challenges, and how are you guys addressing those? >> Great, so, today, for both 8S systems and autonomous driving, the companies out there are focusing on really the simpler problems of being able to properly recognize an object or an obstacle in good conditions, fair weather in Arizona, or Mountain View or Tel Aviv, et cetera. But really the, we would live in the real world. There's bad weather, there is low light, there's lens issues, lens dirty, and so on. Being able to address those difficult issues is not really being done well today. There's difficulties in today's system architectures to be able to do that. We take a very different, novel approach to how we process and learn through deep learning the ability to do that much more robustly and much more accurately than today's systems. >> How much of that's done kind of in the car, how much of it's done where you're building your algorithms offline and then feeding them back into the car, how does that loop kind of work? >> Great question, so the objective for this, we're deploying on, is the intent to deploy on systems that are in the car, embedded, right? We're not looking to the cloud-based system where it's going to be processed in the cloud and the latency issues and so on that are a problem. Right now, it's focused on the embedded platform in the car, and we do training of the datasets, but we take a novel approach with training as well. We don't need as much training data because we augmented it with very specific synthetic data that understands the camera itself as well as taking in the difficult critical cases like low light and so on. >> Do you have your own dedicated camera or is it more of a software solution that you can use for lots of different types of inbound sensors? >> Yeah, what we have today is, we call it, CANA. It is a full end-to-end stack that starts from the sensor output, so say, an imaging sensor or a path to fusion like LIDAR, radar, et cetera, all the way up to the perception output that would then be used by the car to make a decision like emergency braking or turning or so on. So, we provided that full stack. >> So perception is a really interesting word to use in the context of a car, car visioning and computer vision cause it really implies a much higher level of understanding as to what's going, it really implies context, so how do you help it get beyond just identifying to starting to get perception so that you can make some decisions about actions. >> Got it, so yeah, it's all about intelligent decisions and being able to do that robustly across all types of operating conditions is paramount, it's mission critical. We've seen recent cases, Uber and Tesla and others, where they did not recognize the problem. That's where we start first with is to make sure that the information that goes up into the stack is as robust and accurate as possible and from there, it's about learning and sharing that information upstream to the control stacks of the car. >> It's weird cause we all saw the video from the Uber accident with the fatality of the gal unfortunately, and what was weird to me on that video is she came into the visible light, at least on the video we saw, very, very late. But ya got to think, right, visible light is a human eye thing, that's not a computer, that's not, ya know, there are so many other types of sensors, so when you think of vision, is it just visible light, or you guys work within that whole spectrum? >> Fantastic question, really the challenge with camera-based systems today, starting with cameras, is that the way the images are processed is meant to create a nice displayed image for you to view. There are definite limitations to that. The processing chain removes noise, removes, does deblurring, things of that nature, which removes data from that incoming image stream. We actually do perception prior to that image processing. We actually learn how to process for the particular task like seeing a pedestrian or bicyclist et cetera, and so that's from a camera perspective. It gives up quite the advantage of being able to see more that couldn't be perceived before. We're also doing the same for other sensing modalities such as LIDAR or radar and other sensing modalities. That allows us to take in different disparate sort of sensor streams and be able to learn the proper way of processing and integrating that information for higher perception accuracy using those multiple systems for sensor fusion. >> Right, I want to follow up on kind of what is sensor fusion because we hear and we see all these startups with their self-driving cars running around Menlo Park and Palo Alto all the time, and some people say we've got LIDAR, LIDAR's great, LIDAR's expensive, we're trying to do it with just cameras, cameras have limitations, but at the end of the day, then there's also all this data that comes off the cars are pretty complex data receiving vehicles as well, so in pulling it all together that must give you tremendous advantages in terms of relying on one or two or a more singular-type of input system. >> Absolutely, I think cameras will be ubiquitous, right? We know that OEMs and Tier-1s are focused heavily on camera-based systems with a tremendous amount of focus on other sensing modalities such as LIDARs as an example. Being able to kit out a car in a production fashion effectively and commercially, economically, is a challenge, but that'll, with volume, will reduce over time, but doing that integration of that today is a very manually intensive process. Each sensing mode has its own way of processing information and stitching that together, integrating, fusing that together is very difficult, so taking an approach where you learn through deep learning how to do that is a way of much more quickly getting that capability into the car and also providing higher accuracy as the merged data is combined for the particular task that you're trying to do. >> But will you system, at some point, kind of check in kind of like the Teslas, they check in at night, get the download, so that you can leverage some of the offline capabilities to do more learning, better learning, aggregate from multiple sources, those types of things? >> Right, so for us, the type of data that would be most interesting is really the escapes. The things where the car did not detect something or told the driver to pay attention or take the wheel and so on. Those are the corner cases where the system failed. Being able to accumulate those particular, I'll call it, snips of information, send that back and integrate that into the overall training process will continue to improve robustness. There's definitely a deployed model that goes out that's much more robust than what we've seen in the market today, and then there's the ongoing learning to then continue to improve the accuracy and robustness of the system. >> I think people so underestimate the amount of data that these cars are collecting in terms of just the way streets operate, the way pedestrians operate, but whether there's a incident or not, they're still gathering all that data and making judgements and identifying pedestrians, identifying bicyclists and capturing what they do, so hopefully, the predictiveness will be significantly better down the road. >> That's the expectation, but like numerous studies have said, there's a lot of data that's collected that's just sort of redundant data, so it's really about those corner cases where there was a struggle by the system to actually understand what was going on. >> So, just give us kind of where you are with Algolux, state of the company, number of people, where are ya on your lifespan? >> Algolux is the startup based in Montreal with offices in Palo Alto and Munich. We have about 26 people worldwide, most of them in Montreal, very engineering heavy these days, and we will continue to do so. We have some interesting forthcoming news that please keep an eye out for of accelerating what we're doing. I'll just hint it that way. The intent really is to expand the team to continue to productize what we've built and start to scale out, to engage more of the automotive companies we're working with. We are engaged today at the Tier-2, Tier-1, and OEM levels in automotive, and the technology is scalable across other markets as well. >> Pretty exciting, we look forward to watching, and you're giving it the challenges of real weather unlike the Mountain View guys who we don't really deal with real weather here. (laughing) >> There ya go. (laughing) Fair enough. >> All right Dave, well, thanks for taking a few minutes out of your day, and we, again, look forward to watching the story unfold. >> Excellent, thank you, Jeff. >> All right. >> All right, appreciate it. >> He's Dave, I'm Jeff, you're watching the Cube. We're are Western Digital in Milpitas at Autotech Council Autonomous Vehicle event. Thanks for watching, we'll catch ya next time.

>> Announcer: 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're in Milpitas, California at Western Digital offices for the Autotech Council Autonomous Vehicle Meetup. About 300 people, a lot of conversations about the not thousands but millions of problems that have to be solved before we get autonomous vehicles on the road. But there's so many angles to this whole story besides just kind of what you think of as just an Uber, a self driving taxi, or even a self driving car for your personal use and it's really a cool start up here that's actually celebrating their 100th round trip transaction. We're excited to have Daniel Laury. He's a CEO and Chief Product Officer of Udelv. Great to see ya. >> Nice to meet you Jeff. >> So you just came off your keynote presentation and you were showing a great highlight movie of your product, so tell the folks what are you guys all about. >> We're the first public road enabled autonomous driving delivery company. And this is, our aim is to cut the cost of last minute deliveries in half. And to make deliveries easier, more convenient for consumers, more ubiquitous, faster, and cheaper of course. >> So it's pretty interesting. So the use case that you're doing now is you're in San Mateo and you're delivering groceries from Draeger's to the neighborhood. >> Yes, we actually now have four customers. >> Jeff: Oh, you have four, okay. >> Yes, in the matter of a month. We gained three more after Draeger's. Draeger's was our first customer. We've been working with them for the last six months to find the, you know, the best cargo space, the way to organize the compartments and everything. And it's been a fantastic partnership. And so they were our first customer and we're doing deliveries for them almost on a daily basis. And then we added three customers. As people were seeing this orange vehicle in the streets, they started calling us and they say "Hey, can I do it?" So, now we have a florist out of Burlingame and a couple of restaurants as well. >> And how many of these vehicles do you have on the road? >> So for now we have one of them. We are getting our second one next week. There is a third one that is going to be ready in about four weeks from today and then we have a production ramp up from there. >> So what are some of the unique challenges in creating this vehicle and delivering the service that people probably never thought of. >> Right, and it's, in our case, first of all we're not a science project, we're a real business case. Probably one of the first ones in the autonomous driving world. And for us to solve this business case, it's not just about autonomous driving, it's also to have a best customer experience. And so we're not just doing autonomous driving. We're doing a bunch of things. We're building a cargo space, that's mechanical engineering that is adapted which is basically a system of compartments or luggage on wheels if you want. The second thing is we are building apps on the merchant side and the customer side. Third thing is on the autonomy side of things we are doing something that very few other companies are doing which is mastering the first and last hundred feet. Slow motion, high precision. One to two centimeter accuracies. To be able to maneuver in parking lots, be able to back up in driveways and things like that nobody else is doing really that kind of thing. And the last thing is which we're doing and we're probably one of the world's most advanced companies doing this is teleoperations. We have to be able to take control of the vehicle. First of all, monitor the fleet. And second take control of the vehicle in case of a special situation. And we're doing this with an ultra-low latency less than 200 milliseconds between the image we receive from the truck and what the command we're giving back which allows us to actually drive the vehicle in the streets as if it was a video game but it's the reality. >> Right, no we did a piece with Fan Amato. I don't know if you know Fan but we were doing kind of a general purpose. A version of that same capability. It's really, really amazing. >> Frankly I think that autonomous driving is going to need that capability for at least the next decade. >> So the last hundred feet is interesting. You know, I went to a Ford Smart Cities event a little while ago and they talked about kind of curb management because when you have all these kind of fleet vehicles getting people in and out, making deliveries in and out. Kind of the curb in that interchange of the curb is really a tricky thing. It take a lot of nuance, you know. Know when to double park. Can you double park, should you double park. Can you, as you said, get into a driveway. So when you, what your ideal scenario when you do do a grocery drop off, you try to get into the driveway? Get off the double parking situation? >> Yes, absolutely. This is a critical part of what we're doing. And parking lots are actually lawless places. You see cops everywhere but you don't see them in parking lots so you have people backing up from a spot, children pushing carts, pets, you name it. So those are very, very complex situations. Mastering those situations is super important for us because of course our vehicle is going to park in those parking lots to pick up the goods and potentially to deliver. So we developed an AI stack, Artificial Intelligence Stack that starts with a scene estimator. We estimate the scene to see where, what spots are available or if it's a driveway if you have cars parked on the curb and then be able to actually maneuver in that spot. >> Right, but you're writing off a lot. So, you're doing the apps, you're doing all the infrastructure with your partners, you're doing the complexity of the vehicle. And then you've got, you've got to worry about perishable goods, you're taking milk as well as warm stuff. So a lot to chew. How big is your team? Where are you in your development as a company? >> Yes, we're about 30 people right now. And we are going to grow this team quite significantly by probably double the size of the team this year. It's a very ambitious project. It's a very ambitious company and yes, as Elon Musk puts it, success is one of the possibilities. One of the possible outcomes. But not necessarily the likeliest. but we're doing that race, we're in that race. >> So, just before we wrap I want to talk a little bit about the human factors. Cuz a lot of conversation earlier in some of the keynotes about trust and no trust. On one hand people don't trust these things. They said that, you know, they show the survey. I don't trust them. On the other hand we see people in autonomous vehicles as if they were a level five, right? They're sleeping and doing all sorts of crazy stuff. When you engage with customers what are some of their reactions on kind of the trust or not trust? How do they respond to this truck driving up and they walk out and pull their groceries out? >> That's a great question. In our case we're in a very different situation than all the ride sharing and passenger vehicles because we don't, by definition we don't carry passengers. So they only interact with the truck in the sense that they have to retrieve their goods. That's the only thing they do. And so they look at this a lot more favorably than, because it doesn't, they don't have that sense of danger from the vehicle. It's actually more like a wow, this is so interesting. And now I'm getting my deliveries. I know it's going to be exactly 16 minutes. And I get my push notification four minutes before. It gets there and then it's a simple, very, very simple way of doing things. It also will be very, very convenient for returning goods. You will be able to summon the vehicle to your doorstep. You'll put into a locker. It goes for you to UPS. It takes a minute to do it. So people love the service. Their reaction has been overwhelmingly positive. And it's a far less dangerous thing to do than having passengers. >> Right, yeah the first time I saw the video of it I thought was Amazon Lock or which is such a convenient way to interact and so importantly as we move to smart cities because what you don't want is the proverbial sticker on your door that you missed a delivery, like aw rats. So this is such an important part of the enablement of smart cities so really, really cool story. Alright Daniel. So last words, getting excited. Going to get out of individual company relationships and start to have more of a generic service that people can tap into? >> Yes, I, we're tremendously excited about the future of this company. Within two or three weeks from having launched a product on January 30th, we've had, we've received phone calls from every large retailer you name it, in the world wanting to do business with us. So it's a very, very exciting start. >> Alright Daniel, we'll keep an eye. >> Thank you so much, Jeff. >> Thanks for stopping by. Alright, he's Dan, I'm Jeff. You're watching theCUBE. We're at the Autotech Council Autonomous Vehicle Event in Milpitas, California. Thanks for watching. Catch you next time. (upbeat music)

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE covering autonomous vehicles. Brought to you by Western Digital. (upbeat electronic music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Autotech Council Autonomous Vehicles event here at Western Digital. It's part of our ongoing work that we're doing with Western Digital about #datamakespossible and all the really innovative and interesting things that are going on that at the end of the day, there's some data that's driving it all and this is a really crazy and interesting space. So we're excited for our next guest. He's Mark DeSantis. He's the CEO of RoadBotics. Mark, great to see you. >> Welcome. >> Thanks, thanks for having me, Jeff. >> So just to give the quick overview of what is RoadBotics all about? >> Sure, we use a simple cellphone as a data collection device. You put that in the windshield, you drive, it records all the video and all that video gets uploaded to the Cloud and we assess the road's surface meter by meter. Our customers would be Public Works departments at the little town to a big city or even a state, and we apply the same principles that a pavement engineer would apply when they look at a piece of pavement. Looking for all the different subtle little features so that they can get, first of all, get an assessment of the road and then they can do capital planning and fix those roads and do a lot of things that they can't do right now. >> So I think the economics of roads and condition of roads, roads in general, right? We don't think about them much until they're closed, they're being fixed, they're broken up, there's a pothole. >> Mark: Yeah. >> But it's really a complex system and a really high value system that needs ongoing maintenance. >> That's right. I always use the example of the Romans who built a 50,000 mile road network across Europe, the Middle East, and Africa. Some of those roads, like the Appian Way, are still used today. They were very good road builders and they understand the importance of roads. Regrettably, we take our roads for granted. The American Society for Civil Engineers annually rates infrastructure and we're rated about 28% of our nation's 11 million lane miles as poor. Unfortunately, that's- >> Jeff: 28%? >> 28%. And that really means that you need to invest, we'll need to invest at least a million to two million bucks a mile to get those roads back into shape. So we take our roads for granted. I'm enjoying this conference and there's one point that I want to make that I think is very poignant, is the AV revolution will also require a revolution in the maintenance and sustenance of our road network, not just the United States but everywhere in the world. >> So it's interesting, and doing some research before we got together in terms of the active maintenance that's not only required to keep a road in good shape but if you keep the active maintenance in position, those roads will last a very long time. And you made an interesting comment that now the autonomous vehicles, it's actually more important for those vehicles, not only for jolting the electronics around that they're carrying, but also for everything to work the way it's supposed to work according to the algorithms. >> Andrew Ang, who's an eminent computer scientist, machine learning, we were spun out of Carnegie Mellon and he was a graduate of that program, recognized early on that the quality of the roads made all the difference in the world for these vehicles to move around. We, in turn, were spun out of Carnegie Mellon, out of that same group of AV researchers, and in fact, the impetus for the technology was to be able to use the sensing technology that allows a vehicle to move around to assess the quality of roads. And it's road inspection, really, is an important part of road maintenance. The ability to go look at an asset. Interestingly, it's an asset whose challenge is not the fact that it can't be inspected, it's the sheer size of the asset. When you're talking about a small town that might have a 60-mile road network, most and the vast majority of inspection is visual inspection. That means somebody in a car riding very slowly looking down and they'll do that for tens, thousands, hundreds of thousands of miles, very hard to do. Our system makes all that very, much more efficient. The interesting thing about autonomous vehicles is they'll have the capacity to use that data to do that very assessment. So for our company, we ultimately see us embedded in the vehicle itself, but for the time being, cellphones work fine. >> Right. So I'm just curious, what are some of those leading indicator data points? Because obviously we know the pothole. >> Mark: Yeah. >> By then things have gone too far but what are some of the subtle things that maybe I might see but I'm not really looking at? (laughs) >> Well, I think I've changed you right now and you don't know it. You're never going to look at a road the same- >> Oh, I told you, I told you. (laughs) >> After you hear me talk for the next three minutes. I don't look at roads the same and I'm not a civil engineer nor am I a pavement engineer, but as the CEO of this company I had to learn a lot about those two disciplines. And in fact, when you look at a piece of asphalt, you're actually looking for things like alligator cracks, which sort of looks like the back of an alligator's skin. Block cracks, edge cracks, rutting, a whole bunch of things that pavement engineers, frankly, and there is a discipline called pavement engineering, where they look for. And those features determine the state of that road and also dictate what repairs will be done. Concrete pavement has a similar set of characteristics. So what we're looking for when we look at a road is, I always say that, people say, "Well, you're the pothole company." If all you see are potholes, you don't have a business. And the reason is, potholes are at the end of a long process of degradation. So when you see a pothole, there are two problems. One is, you can certain blow out a tire or break an axle on that pothole but also it's indicative of a deeper problem which means the surface of the road has been penetrated which means you to dig up that road and replace it. So if you can see features that are predictive of a road that's just about to go bad, make small fixes, you can extend the useful life of that asset indefinitely. >> Right. So before I let you go, unfortunately, we're just short on time. >> Mark: Yeah. >> I would love to learn about roads. I told you, I skateboard so I pay a lot of attention to smooth roads. >> Mark: (laughs) And you'll pay even more now. >> Now I'll pay even more and call the city. (chuckles) But I want to pivot off what happened at Carnegie Mellon and obviously academic institutions are a huge part of this revolution. >> Yeah, yeah. >> There's a lot of work going on. We're close to Stanford and Berkeley here. Talk a little bit about what happens... It's happening at Carnegie Mellon and I think specifically you came out of the Robotics Institute in something called the Traffic21 project. >> Yeah, Traffic21 is funded by some local private interests who believed that the various technologies that are, really, CMU is known for around computer science, robots, engineering, could be instrumental in bringing about this AV revolution. And as a consequence of that, they developed a program early on to try to bring these technologies together. Uber came along and literally hired 27 of those researchers. Argo, now... Argo, Ford's autonomous vehicle now, is big in Pittsburgh as well. On any given day, by my estimate, it's not an official estimate here, there are about 400 autonomous vehicles, Ford and Uber vehicles, on Pittsburgh's streets every single day. It's an eerie experience being driven around by a completely autonomous Uber vehicle, believe me. >> I've been in a couple. It's interesting and we did a thing with a company called Phantom. They're the ones that step if your Uber gets stuck. >> Oh, yeah. >> Which is interesting. (laughs) So really interesting times and exciting and I will go and pay closer attention for the alligator patterns (laughs) on my route home tonight. (laughs) All right, Mark, thanks for stopping by and sharing the insight. >> Thanks again, Jeff. Appreciate you having me. >> All right, he's Mark, I'm Jeff. You're watching theCUBE from the Autotech Council Autonomous Vehicles event in Milpitas, California. Thanks for watching. (upbeat electronic music)

>> Narrator: 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're in Milpitas, California at the Autotech Council Autonomous Vehicle Event. Autotech Council is an interesting organization really trying to bring a lot of new Silicon Valley technology companies, and get them involved with what's going on in industries. They've done a Teleco Council. This is the auto one. We were here last year. It was all about mapping. This is really kind of looking at the state of autonomous vehicles. We're excited to be here. It's a small intimate event, about 300 people. A couple of cool, dem hook cars out side. And our first guest is here. He's Suneil Mishra. He is the strategic marketing for Tensyr. Nice to be here. >> Thanks, Jeff. Appreciate you having us. >> Yeah. So, give us the overview on Tensyr. >> Sure. So we're a Silicon Valley startup, venture-backed. We're actually just coming out of stealth. So you're one of the first folks to hear about-- >> Jeff: Congratulations. >> what we're up to. And we're basically doing software platforms to actually accelerate autonomous vehicles into production, doing all the things around safety and efficiency, and ROI that will be important when we actually want to make money on all of this stuff. >> Right. So what does that mean because obviously, you're in Palo Alto. I'm in Palo Alto. We see the Waymo cars driving around all the time. And it seems like every day I see a few more cars running around with LIDAR stacks on top. You know, those are all kind of R and D login miles, doing a lot of tests. What are some of the real challenges to get it from where it is today to actual production? And how are you guys helping that process? >> Sure. So yeah, I mean a lot of what people don't think about is these R and D kind of pilot cars. They actually are doing R and D. It's trial and error. That's the whole point of R and D. When you get to production, you can't have that error part anymore. And so safety suddenly becomes a critical element. And part of the things of getting safety is being much more efficient on the vehicle because you have to do a lot more software in order to be safe across multiple different kinds of examples of streets, and locations, of weather conditions, and so on. So, we basically provide essentially all of the glue, all of the grunt work, at the lower levels, to make things as efficient as possible, as safe as possible, as secure as possible. And also making things adaptable and flexible. There's lots of different hardware coming down the pipeline from all different vendors. And if you're a production vehicle, it's which ones you choose. There may be different configurations for different cost points of vehicles. And then of course when you're looking to the future as a production vehicle manufacturer, how do you know which pieces of hardware to use and whether your software will work or not? We kind of give you a lot of insight into all of those things that allow you to certify that your products are safe. And so we don't build the stacks themselves, but we actually take people self-driving models, and we accelerate them onto the vehicles. >> Jeff: With your software in the ecosystem of the self-driving car hardware. >> Exactly. So we have an actual runtime engine that will set on the end device, in this case a vehicle. And it will actually optimize the scheduling, the orchestration of all of your code. That makes it much more efficient. And we can monitor that so you can mitigate for safety. And if something does go wrong, we're essentially like a black box where you can actually see what actually happened to your software. >> So it's interesting. We talked a little bit before we turned the cameras on that a lot of the self-driving vehicles are Fords. We talked to the guys at Phantom and apparently, it's a really nice system to be able to get computer control into the control mechanisms of the car. But you said there's a whole layer of how do you define being able to interact with the control systems of the car, versus is it safe, is it ready for production, and kind of taking it beyond that R and D level. So what are some of the real challenges that people need to be aware of when we're going to make that big leap. >> Yeah, so I mean, a couple of the big things that happen is when you're seeing these pilot vehicles driving around, the amount of software that they actually have on there to control the vehicles is very tuned for the particular cases. That's why you see a lot of these vehicles out in places like Arizona where it's sunny weather. You're not having to deal with snow and all the rest of that stuff. >> Jeff: Right. >> If they actually take a car and move it to Michigan for the snow test, they'll actually deploy different software to do the snow case. But when you're actually in a production vehicle, and nobody can actually come back and change that software, you're going to have to load all of those types of solution, on at the same time. That requires more space, more compute power. And so for solutions like ours, we actually allow the production manufacturers to figure out what the optimal solutions are in those cases because you can't come back and change the software. You don't have an engineer that can go tweak that code. And you don't have a safety driver, of course, to go grab the wheel if something goes wrong. These things essentially have to be able to go out there in the wilderness for years and years, and actually work. So it's a whole different classification of problem that takes a lot more compute power. And people who are seeing those giant sets of sensor rigs don't probably realize there's also a giant trunk for clarisitive, where if there's compute power in the back, running 3,000 watts of power. When you actually get to deployment, you're going to have an embedded system with maybe 500 watts of power. So you have less compute power, and you're trying to do more with it. So it's quite a challenging problem, to actually jump to production. And we're kind of smoothing out a lot of those wrinkles. >> Right. So, I just want to get your kind of perspective on kind of the Apple approach, which everyone kind of sees Tesla as. Right? It's soup to nuts, it's the car's design, it's the software, versus kind of an industry approach where you have all these different players, obviously, 300 people here at this event. There's autonomous vehicle events going on all over the place where you got all these component manufacturers, and component parts, coming together to create the industry autonomous vehicles versus just the Tesla. So what's kind of the vibe in the industry? It feels like early days. Everybody's cooperating. How is this think kind of coalescing? >> Yeah. I think what we're seeing, we basically talk to people up and down the stack, because anyone who's doing this stuff is a potential customer for us, so automotive OEMs to tier one suppliers, to the AI startups are building these software stacks, they're all potential customers for us. What we're seeing from everyone is they're saying there's so many difficult problems to solve along this path that no company can really do it themselves. And of course, you're seeing big companies investing billions of dollars. But it's great because everybody's saying, let's find people that specialize, whether it's in sensors, or compute, all the rest of those things. And kind of get them, and partner with them, have everybody solve the right problem that they're specialized and focused on. And we essentially can kind of come in and we solve parts of those problems, but we're also kind of the glue that fills a lot of those things together. So we actually see ourselves as being quite advantageous in that anyone who's doing their specialized piece, contributes into the collective. And we kind of build that collective and make it easy for the actual end vendor that's trying to sell a car or run a service, to actually access all those mechanisms. >> And are kind of the old school primary manufacturers still the focal point of the coalescing around this organization or are they losing kind of that position? >> I wouldn't say their losing it. It's kind of an interesting play. So you've got a bunch of traditional automotive guys who actually don't really, not to diss them, but they don't really understand large-scale software because they haven't had that in their vehicles until now. And at the same time you've got kind of your startup mode software experts that don't really understand a lot about automotive. But eventually, it's got to go on a car. And so what we're finding is the automotive manufacturers are really saying to get to production, we need certain kinds of safety guarantees and ROI and so on. So they're really driving from that point of view. The software guys are kind of saying, well, we're just going to throw the software over to you and sort of, good luck. So, we're actually finding both sides care, but nobody's quite sure who should be taking the lead. So I think we're getting to the point where ultimately, automotive manufacturers will be the one shipping vehicles and that software's going to be on their car. So they're going to be the ones that care about it most. So we're actually seeing them being quite proactive about how do we solve these problems. How do we get from the R and D stage to the actual production stage? So that's where we're seeing a lot of the interest on our side. >> All right, Suneil. We could go on forever, but we have to leave it there. And congratulations on your launch and coming out of stealth. And we're excited to watch the story unfold. >> Great. Thanks, Jeff. I appreciate the time. >> All right. He's Suneil. I'm Jeff Frick. You're watching The Cube from the Autotech Council Autonomous Vehicle Event in Milpitas, California. Thanks for watching. (upbeat music)

>> Announcer: From Milpitas, California at the edge of Silicon Valley, it's The CUBE. Covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick here with The Cube. 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 of the data is moving out to the edge and edge computing and nowhere is that more apparent than in autonomous vesicles so we're really excited to have maybe the best title at Western Digital, I don't know. Chris Bergey, 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 her 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 internets, 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, autonomous vehicles are being 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 have many, many sensors. Whether it's sensing your face, gyroscopes, GPS, all these kind of things. The car is having the exact 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 nother 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 with that information. >> That's correct. I think one of the examples I give that's easier for people to understand is surveillance, right? 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 acutely being processed, right? How may 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's 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, hey, there's going to be something, my sensors are seeing something, and then, of course, you'd 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 the car off the side of a bus, I mean, these are the nuance things that aren't necessarily obvious when you first start exploring. >> 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. 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 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 just mind-boggling how quickly we are starting to attack these more difficult challenges. And we'll get there but it's going to take time like anything. >> The other really important thing, especially now where we're in 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 and those sensors when something jumps out in front of the car. So really, the rise of edge computing and moving so much of that stored 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 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 see statistics thrown 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. >> The old sneaker, 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 xobyte, a zedobyte, a yodabyte, and then the crowd responded. No, it's a brontosaurousbyte is even bigger than a yodabyte. 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'll be able to take all that stuff in in real time, which probably state of the market now is financial trading and advertising markets. But to do that now in a car where if you make a mistake, there's really significant consequences. It's a really different challenge. >> It is and again, that's really this advent of the sensor data, right? The sensor data is going to swamp probably every other data set that's in the world, but a lot of it's not interesting because you don't know when that interesting event is going to happen. So what you actually find is that you try to put it's intelligence as close as you can to the data, end storage, and again, storage may be 30 seconds to 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 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 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 an even higher granularity?" We could have pulled out more of these trends or more of these corner cases. So I think that's one of the challenges enterprise are going through right now is that everyone's so scared of getting rid of any data, yet there's just tremendous data growth. And we're sitting right here in the middle of it at Western Digital. >> Well, thankfully for you guys, you're going to store all that data and it is really important, though, because it used to be, it's funny to me. It used to be a sample of things that happened in the past is 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. >> 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's 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 an compute. So thanks for taking ... >> And I try to eat in between there too. >> And you try to eat and hopefully see your kids Friday night, so hopefully you'll take >> Absolutely. your wife out to a movie tonight. >> All right, Chris, great to see you. Thanks for taking a few minutes. >> Chris: Thank you very much. >> All right, I'm Jeff Frick. You're watching The CUBE from Autotech Council Autonomous Vehicle event. Thanks for watching.

>> Announcer: 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're in Milpitas, California, at Western Digital, at the Autotech Council Autonomous Vehicle Event. About 300 people, really deep into this space. It's a developing ecosystem. You know, we think about Tesla, that's kind of got a complete, closed system. But there's a whole ecosystem of other companies getting into the autonomous vehicle space, and as was mentioned in the keynote, there are, literally thousands of problems. A great opportunity for startups. So we're excited to have Oded Sagee, he's a senior director of product marketing from Western Digital. Oded, great to see you. >> Thank you very much, Jeff. >> So you were just on the panel and, really that was a big topic, is there are thousands of problems to solve and this ecosystem's trying to come together, but it's complicated, right? It's not just the big car manufacturers anymore, and the tier one providers, but there's this whole ecosystem that's now growing up to try to solve these problems. So what are you seeing from your point of view? >> Yes, correct. So, definitely in the past automotive was a tough market to play in, but it was simple from the amount of players and people you needed to talk to to design your product inside. With the disruption of connectivity, smart vehicles, even before autonomous, there are so many new systems in the car now that generate data or consume data. And so, for us, to kind of figure out what's the use case, right? How is this going to look in the future? Who's going to define it? Who's going to buy it? Who's going to pay for it? It has become more and more complex. Happily, storage is in the center of all this. >> Jeff: Right. >> So we get a seat at the table and everyone wants to talk to us, but yes, it's a very big ecosystem now. And trying to resolve that problem, it's going to take some time. >> So what are some of the unique characteristics, from a storage point of view, that you have to worry about? Obviously environmental jumps out. We had the guy on before talking about bumpy roads, you know, the huge impacts on vibration. And now you spent a lot of money for a Toughbook back in the day to put a laptop in a cop car, this is a whole other level of expense, investment, and data flow. >> Right. So, for us, I think with all this disruption happening of full autonomous, people are, very much focused on making that autonomous work, right? So, for them it's all about connectivity, it's all about the sensor, whether it's Lidar, or, you know, cameras. Just making that work, right? All the algorithms and the software. And so, for them storage, currently is an afterthought, right? They were saying, once we meet mass production we'll just go and buy some storage and everything's going to be fine. So while they're prototyping, right? They can use any storage that they want. But, if you think about a full autonomous vehicle out there driving, not two hours a day like we are driving today, right? 20 hours a day, from cold to hot, going through areas without connectivity. Suddenly, the storage requirements are very, very different. And this is what we're trying to drive and explain that, if we don't design the future storage solutions today, What's going to end up, is that people are going to pay much more for storage just to make a basic use case work. >> Right. >> But if we start working now, and I'm talking about five, seven years out, we can have affordable solutions to make those business models work. >> And is that resonating in the industry, or are they just too focused on, you know, better cameras? >> It definitely does, but as companies change, right? So let's just take the car makers for a second. They didn't necessarily have a CTO in place, right? To drive engineering and semi-conductor. So you got to find those figures, and you got to start working and educating them. It definitely resonates if you have the right person. Once you find him, yes, it's on the list of priority. So we need to push. But it is happening. Yes, it is resonating. >> And it's so different because you do have this edge case. You have so much data being collected out in the field, if you will, within that vehicle. Some, to go back to the cloud, but you've got latency is always an issue, right? For safety. So, a little different storage challenge. So are there significant design thoughts that you guys are bringing into play on why this is so different and what is it going to take to really have kind of an optimal solution for autonomous vehicles? >> Yes, definitely there are a couple of vectors I would say, or knobs we need to work on. One of them is temperature. So, again vehicles do tend to go between hot and cold. Unlike many other components that just need to make sure that they operate between hot and cold, we actually have a big challenge on keeping data being accurate between hot and cold. So if you program cold and read hot and vice versa, data gets corrupted. >> Oh, even within the structures within the media? >> Yes. >> Okay. >> And people don't know that. So, for us to figure out, what's the temperature range that the car, through its lifetime, is going to go through. And make sure that we meet the use case, that's a big one. What we call the endurance on the cycling of the storage, again, if you cannot rely on connectivity, cannot rely on cloud because of latency, you need to record a lot of data in the car. So, again, a car drives for seven years, 15 years, and you want to record constantly, how much do you need to record? We don't necessarily have the technology today to meet that use case and we need to work with the ecosystem, in figuring it out. So these are just two examples. >> And I would imagine clean power, as you're saying these things, but they can need others. You're not in daddy's data center anymore. This is a pretty harsh environment, I would imagine. >> Very harsh. >> Ugly power, inconsistent power, turning off the car before everything is spun down. There's all kinds of little, kind of environmental impacts in that whole realm that you would never think of in, kind of a typical data center, for instance. >> Correct. And even, you touched power, that's very interesting because even some people think, oh, there's not power limitation in a car. You can just enjoy how much power you want. Actually, it's very, very sensitive. The battery, if you think about an EV car now has so many components to run and so even the power consumption, right? Just the energy that you need to consume is becoming critical for each, and every component >> in the vehicle. >> Right. And it's everybody's AI comparison, right? Is if Kasparov had to fight the computer with the same amount of power, it wouldn't have been much of a match. So the power to run all this AI stuff is not insignificant, so it is going to be a huge drain on these electric vehicles. Pretty exciting times. So when you get up in the morning, what's the biggest thing, when you talk to people about autonomous vehicles, that they just don't get? That people should really be thinking about. >> Yeah, so it goes back to some of the things we've discussed. Definitely, again, we're seeing the use cases change. We are working again with the broad ecosystem to explain the fundamental challenges that we have, right? What is our design cycle? What are the challenges that we have? So we start with educating the ecosystem, so they know what we have. And from that we trigger a discussion because they realize, oh, okay, because I do have a use case that, probably, you don't have a solution for, how do we go together? And we're doing it across the board. It's not only happening in automotive. It's happening in surveillance. It's happening in the home space. A lot of people don't know, but the home space, if you think about it, again, set-top boxes used to be huge, sat outside in the room. People are moving to these sticks, right? And they're behind the TV and they have no ventilation and they're small and they record all the time. And they get to temperatures that we've never seen in the past. So we even need to educate the telcos of the world, the set-top box makers. Everything is changing. Automotive is definitely ahead in a lot of innovation and disruption, but everything is changing for us. >> Right, a lot of those are fond of just the bright shiny object that everybody can see, right? We can't necessarily see a lot of IOT that GE's putting in to connect their factories. Alright, Oded, well thanks for taking a few minutes out of your busy day and I really appreciate the insight. >> Thank you very much. >> All right, he's Oded, I'm Jeff, You're watching theCUBE from Western Digital at The Autonomous Vehicle Event for the Autotech Council. Thanks for watching. Catch you next time. (electronic music)

hey welcome back everybody Jeff Rick here with the cube we're at the mill pedis at an interesting event is called the auto tech council innovation in motion mapping and navigation event so a lot of talk about autonomous vehicles so it's a lot of elements to autonomous vehicles this is just one small piece of it it's about mapping and navigation and we're excited to have with us our first guest again and give us a background of this whole situation just Derick Curtin and he's the founder and chairman of the auto tech council so first up there welcome thank you very much good to be here absolutely so for the folks that aren't familiar what is the auto tech council autofit council is a sort of a club based in Silicon Valley where we have gathered together some of the industry's largest OMS om is mean car makers you know of like Rio de Gono from France and a variety of other ones they have offices here in Silicon Valley right and their job is to find innovation you find that Silicon Valley spark and take it back and get it into cars eventually and so what we are able to do is gather them up put them in a club and route a whole bunch of Silicon Valley startups and startups from other places to in front of them in a sort of parade and say these are some of the interesting technologies of the month so did they reach out for you did you see an opportunity because obviously they've all got the the Innovation Centers here we were at the Ford launch of their innovation center you see that the tagline is all around is there too now Palo Alto and up and down the peninsula so you know they're all here so was this something that they really needed an assist with that something opportunity saw or was it did it come from more the technology side to say we needed I have a new one to go talk to Raja Ford's well it's certainly true that they came on their own so they spotted Silicon Valley said this is now relevant to us where historically we were able to do our own R&D build our stuff in Detroit or in Japan or whatever the cases all of a sudden these Silicon Valley technologies are increasingly relevant to us and in fact disruptive to us we better get our finger on that pulse and they came here of their own at the time we were already running something called the telecom Council Silicon Valley where we're doing a similar thing for phone companies here so we had a structure in place that we needed to translate that into beyond modem industry and meet all those guys and say listen we can help you we're going to be a great tool in your toolkit to work the valley ok and then specifically what types of activities do you do with them to execute division you know it's interesting when we launched this about five years ago we're thinking well we have telecommunication back when we don't have the automotive skills but we have the organizational skills what turned out to be the cases they're not coming here the car bakers and the tier 1 vendors that sell to them they're not coming here to study break pad material science and things like that they're coming to Silicon Valley to find the same stuff the phone company two years ago it's lookin at least of you know how does Facebook work in a car out of all these sensors that we have in phones relate to automotive industry accelerometers are now much cheaper because of reaching economies of scale and phones so how do we use those more effectively hey GPS is you know reach scale economies how do we put more GPS in cars how do we provide mapping solutions all these things you'll set you'll see and sound very familiar right from that smartphone industry in fact the thing that disrupts them the thing that they're here for that brought them here and out of out of defensive need to be here is the fact that the smartphone itself was that disruptive factor inside the car right right so you have events like today so gives little story what's it today a today's event is called the mapping and navigation event what are people who are not here what's what's happening well so every now and then we pick a theme that's really relevant or interesting so today is mapping and navigation actually specifically today is high definition mapping and sensors and so there's been a battle in the automotive industry for the autonomous driving space hey what will control an autonomous car will it be using a map that's stored in memory onboard the car it knows what the world looked like when they mapped it six months ago say and it follows along a pre-programmed route inside of that world a 3d model world or is it a car more likely with the Tesla's current they're doing where it has a range of sensors on it and the sensors don't know anything about the world around the corner they only know what they're sensing right around them and they drive within that environment so there's two competing ways of modeling a 3d world around autonomous car and I think you know there was a battle looking backwards which one is going to win and I think the industry has come to terms with the fact the answer is both more everyday and so today we're talking about both and how to infuse those two and make better self-driving vehicles so for the outsider looking in right I'm sure they get wait the mapping wars are over you know Google Maps what else is there right but then I see we've got TomTom and meet a bunch of names that we've seen you know kind of pre pre Google Maps and you know shame on me I said the same thing when Google came out with a cert I'm like certain doors are over who's good with so so do well so Eddie's interesting there's a lot of different angles to this beyond just the Google map that you get on your phone well anything MapQuest what do you hear you moved on from MapQuest you print it out you're good together right well that's my little friends okay yeah some people written about some we're burning through paper listen the the upshot is that you've MapQuest is an interesting starting board probably first it's these maps folding maps we have in our car there's a best thing we have then we move to MapQuest era and $5,000 Sat Navs in some cars and then you might jump forward to where Google had kind of dominate they offered it for free kicked you know that was the disruptive factor one of the things where people use their smartphones in the car instead of paying $5,000 like car sat-nav and that was a long-running error that we have in very recent memory but the fact of the matter is when you talk about self-driving cars or autonomous vehicles now you need a much higher level of detail than TURN RIGHT in 400 feet right that's that's great for a human who's driving the car but for a computer driving the car you need to know turn right in 400.000 five feet and adjust one quarter inch to the left please so the level of detail requires much higher and so companies like TomTom like a variety of them that are making more high-level Maps Nokia's form a company called here is doing a good job and now a class of car makers lots of startups and there's crowdsource mapping out there as well and the idea is how do we get incredibly granular high detail maps that we can push into a car so that it has that reference of a 3d world that is extremely accurate and then the next problem is oh how do we keep those things up to date because when we Matt when when a car from this a Nokia here here's the company house drives down the street does a very high-level resolution map with all the equipment you see on some of these cars except for there was a construction zone when they mapped it and the construction zone is now gone right update these things so these are very important questions if you want to have to get the answers correct and in the car stored well for that credit self drive and once again we get back to something to mention just two minutes ago the answer is sensor fusion it's a map as a mix of high-level maps you've got in the car and what the sensors are telling you in real time so the sensors are now being used for what's going on right now and the maps are give me a high level of detail from six months ago and when this road was driven it's interesting back of the day right when we had to have the CD for your own board mapping Houston we had to keep that thing updated and you could actually get to the edge of the sea didn't work we were in the islands are they covering here too which feeds into this is kind of of the optical sensors because there's kind of the light our school of thought and then there's the the biopic cameras tripod and again the answers probably both yeah well good that's a you know that's there's all these beat little battles shaping up in the industry and that's one of them for sure which is lidar versus everything else lidar is the gold standard for building I keep saying a 3d model and that's basically you know a computer sees the world differently than your eye your eye look out a window we build a 3d model of what we're looking at how does computer do it so there's a variety of ways you can do it one is using lidar sensors which spin around biggest company in this space is called Bella died and been doing it for years for defense and aviation it's been around pointing laser lasers and waiting for the signal to come back so you basically use a reflected signal back and the time difference it takes to be billows back it builds a 3d model of the objects around that particular sensor that is the gold standard for precision the problem is it's also bloody expensive so the karmak is said that's really nice but I can't put for $8,000 sensors on each corner of a car and get it to market at some price that a consumers willing to pay so until every car has one and then you get the mobile phone aside yeah but economies of scale at eight thousand dollars we're looking at going that's a little stuff so there's a lot of startups now saying this we've got a new version of lighter that's solid-state it's not a spinning thing point it's actually a silicon chip with our MEMS and stuff on it they're doing this without the moving parts and we can drop the price down to two hundred dollars maybe a hundred dollars in the future and scale that starts being interesting that's four hundred dollars if you put it off all four corners of the car but there's also also other people saying listen cameras are cheap and readily available so you look at a company like Nvidia that has very fast GPUs saying listen our GPUs are able to suck in data from up to 12 cameras at a time and with those different stereoscopic views with different angle views we can build a 3d model from cheap cameras so there's competing ideas on how you build a model of the world and then those come to like Bosh saying well we're strong in car and written radar and we can actually refine our radar more and more and get 3d models from radar it's not the good resolution that lidar has which is a laser sense right so there's all these different sensors and I think there the answer is not all of them because cost comes into play below so a car maker has to choose well we're going to use cameras and radar we're gonna use lidar and high heaven so they're going to pick from all these different things that are used to build a high-definition 3d model of the world around the car cost effective and successful and robust can handle a few of the sensors being covered by snow hopefully and still provide a good idea of the world around them and safety and so they're going to fuse these together and then let their their autonomous driving intelligence right on top of that 3d model and drive the car right so it's interesting you brought Nvidia in what's really fun I think about the autonomous vehicle until driving cars and the advances is it really plays off the kind of Moore's laws impact on the three tillers of its compute right massive compute power to take the data from these sensors massive amounts of data whether it's in the pre-programmed map whether you're pulling it off the sensors you're pulling off a GPS lord knows where by for Wi-Fi waypoints I'm sure they're pulling all kinds of stuff and then of course you know storage you got to put that stuff the networking you gotta worry about latency is it on the edge is it not on the edge so this is really an interesting combination of technologies all bring to bear on how successful your car navigates that exit ramp you're spot-on and that's you're absolutely right and that's one of the reasons I'm really bullish on self-driving cars a lot more than in the general industry analyst is and you mentioned Moore's law and in videos taking advantage of that with a GPUs so let's wrap other than you should be into kind of big answer Big Data and more and more data yes that's a huge factor in cars not only are cars going to take advantage of more and more data high definition maps are way more data than the MapQuest Maps we printed out so that's a massive amount of data the car needs to use but then in the flipside the cars producing massive amounts of data I just talked about a whole range of sensors I talked lidar radar cameras etc that's producing data and then there's all the telemetric data how's the car running how's the engine performing all those things car makers want that data so there's massive amounts of data needing to flow both ways now you can do that at night over Wi-Fi cheaply you can do it over an LTE and we're looking at 5g regular standards being able to enable more transfer of data between the cars and the cloud so that's pretty important cloud data and then cloud analytics on top of that ok now that we've got all this data from the car what do we do with it we know for example that Tesla uses that data sucked out of cars to do their fleet driving their fleet learning so instead of teaching the cars how to drive I'm a programmer saying if you see this that they're they're taking the information out of the cars and saying what are the situation these cars are seen how did our autonomous circuitry suggest the car responds and how did the user override or control the car in that point and then they can compare human driving with their algorithms and tweak their algorithms based on all that fleet to driving so it's a master advantage in sucking data out of cars massive advantage of pushing data to cars and you know we're here at Kingston SanDisk right now today so storage is interesting as well storage in the car increasingly important through these big amount of data right and fast storage as well High Definition maps are beefy beefy maps so what do you do do you have that in the cloud and constantly stream it down to the car what if you drive through a tunnel or you go out of cellular signal so it makes sense to have that map data at least for the region you're in stored locally on the car in easily retrievable flash memory that's dropping in price as well alright so loop in the last thing about that was a loaded question by the way and I love it and this is the thing I love this is why I'm bullish and more crazier than anybody else about the self-driving car space you mentioned Moore's law I find Moore's law exciting used to not be relevant to the automotive industry they used to build except we talked about I talked briefly about brake pad technology material science like what kind of asbestos do we use and how do we I would dissipate the heat more quickly that's science physics important Rd does not take advantage of Moore's law so cars been moving along with laws of thermodynamics getting more miles per gallon great stuff out of Detroit out of Tokyo out of Europe out of Munich but Moore's law not entirely relevant all of a sudden since very recently Moore's law starting to apply to cars so they've always had ECU computers but they're getting more compute put in the car Tesla has the Nvidia processors built into the car many cars having stronger central compute systems put in okay so all of a sudden now Moore's law is making cars more able to do things that they we need them to do we're talking about autonomous vehicles couldn't happen without a huge central processing inside of cars so Moore's law applying now what it did before so cars will move quicker than we thought next important point is that there's other there's other expansion laws in technology if people look up these are the cool things kryder's law so kryder's law is a law about storage in the rapidly expanding performance of storage so for $8.00 and how many megabytes or gigabytes of storage you get well guess what turns out that's also exponential and your question talked about isn't dat important sure it is that's why we could put so much into the cloud and so much locally into the car huge kryder's law next one is Metcalfe's law Metcalfe's law has a lot of networking in it states basically in this roughest form the value of network is valued to the square of the number of nodes in the network so if I connect my car great that's that's awesome but who does it talk to nobody you connect your car now we can have two cars you can talk together and provide some amount of element of car to car communications and some some safety elements tell me the network is now connected I have a smart city all of a sudden the value keeps shooting up and up and up so all of these things are exponential factors and there all of a sudden at play in the automotive industry so anybody who looks back in the past and says well you know the pace of innovation here has been pretty steep it's been like this I expect in the future we'll carry on and in ten years we'll have self-driving cars you can't look back at the slope of the curve right and think that's a slope going forward especially with these exponential laws at play so the slope ahead is distinctly steeper in this deeper and you left out my favorite law which is a Mars law which is you know we underestimate in the short term or overestimate in the short term and underestimate in the long term that's all about it's all about the slope so there we could go on for probably like an hour and I know I could but you got a kill you got to go into your event so thanks for taking min out of your busy day really enjoyed the conversation and look forward to our next one my pleasure thanks all right Jeff Rick here with the Q we're at the Western Digital headquarters in Milpitas at the Auto Tech Council innovation in motion mapping and navigation event thanks for watching