 Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Milpitas. That interesting event is called the Autotech Council Innovation and Motion Mapping and Navigation Event. So a lot of talk about autonomous vehicles. There'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 to give us the background of this whole situation, just Derek Curtin and he's the founder and chairman of the Autotech Council. So first off Derek, welcome. Thank you very much, good to be here. Absolutely, so for the folks that aren't familiar, what is the Autotech Council? Autotech Council is a sort of a club based in Silicon Valley, where we have gathered together some of the industry's largest OEMs. OEMs mean car makers you know of, like Ford or Toyota or Renault from France and a variety of other ones. They have offices here in Silicon Valley and their job is to find innovation and 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 too 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? Cause obviously they've all got the innovation centers here. We were at the Ford launch of their innovation center. You see the taglines all around. Was there too? Down Palo Alto and up and down the peninsula. So you know, they're all here. So was this something that they really needed and assist with that something opportunity saw or was it, did it come from more of the technology side to say we need an avenue and to go talk to Raja Ford? Well, it's certainly true that they came on their own. So they spotted Silicon Valley said this is now relevant to us. We're historically, we were able to do our own R&D, build our stuff in Detroit or in Japan or whatever the case is. 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 of Silicon Valley where we're doing a similar thing for phone companies here. So we had the structure in place that we needed to translate that into the automotive industry and meet all those guys and say, listen, we can help you. We're gonna be a great tool in your toolkit to work the Valley. Okay, and then specifically what types of activities do you do with them to execute the vision? You know, it's interesting when we launched this about five years ago, we were thinking, well, we have telecommunication background. We don't have the automotive skills but we have the organizational skills. What turned out to be the case is they're not coming here, the car makers and the tier one 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 companies did years ago, Silicon Valley stuff. You know, how does Facebook work in a car? How do all these sensors that we have in phones relate to automotive industry? Accelerometers are now much cheaper because of reaching economies of scale in phones. So how do we use those more effectively? Hey, GPS has reached scale economies. How do we put more GPS in cars? How do we derive mapping solutions? All these things you'll see sound very familiar from that smartphone industry. And in fact, the thing that disrupts them, the thing that they're here for that brought them here 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 give us a little story. What's today's event? It's called the mapping and navigation event. What are people who are not here? What's happening? Well, so every now and then we pick a theme that's really relevant or interesting. So today is mapping and navigations actually specifically today as 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 on board 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, that 3D model world. Or is it a car more like what the Tesla is currently 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 an autonomous car. And I think 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 everything. And so today we're talking about both and how do we fuse 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. Google maps, what else is there, right? But then I see we've got Tom, Tom, and meet a bunch of names that we've seen kind of pre-Google maps and shame on me, I said the same thing when Google came out with the search. I'm like, search into wars are over, who's Google? So I showed you what I know. I'll tell you, I'm way ahead of you. But it's interesting, there's a lot of different angles to this beyond just the Google map that you get on your phone. Well, I think MapQuest won everything. What do you do? You moved on from MapQuest, you printed out, you're good to go, right? Well, that's the- My wife still prints those out, right? There's people printing those out somewhere, burning through paper. Listen, the upshot is that MapQuest is an interesting starting point for me. First it's these maps, folding maps we have in our car. That's the best thing we have. Then we moved to MapQuest era and $5,000 sat-navs in some cars. And then you jumped forward to where Google had kind of dominated, 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 for that car sat-nav. And that was the long-running era that we're having 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, right. 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 400.0005 feet and adjust one quarter inch to the left, please. So the level of detail required is much higher. And so companies like TomTom, like a variety of them that are making more high-level maps. Nokia's a former company called Here is doing a good job and then lots 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 a car from, they say Nokia here, here is the name of the company now, drives down a 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. How do we update these things? So these are very important questions. If you want to have the answers correct and in the car stored well for that car to self-drive. And once again, we get back to something we mentioned just two minutes ago. The answer is sensor fusion. It's a map, it's 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 when this road was driven. Yeah, it's interesting, back of the day, right? When you had to have the CD for your on-board mapping system, you had to keep that thing updated and you could actually get to the edge of the CD, it didn't work anymore. Yeah, so the world is flat. And then the other thing, are they covering it here too? Which feeds into this is kind of the whole, all the optical sensors because there's kind of the LiDAR school of thought and then there's the biopic camera school of thought and again, the answer is probably both, right? Yeah, well, there's all these neat 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 a computer sees the world differently than URI. URI look out a window, we build a 3D model of what we're looking at. How does a computer do it? So there's a variety of ways you can do it. One is using LiDAR sensors, which spin around, the biggest company in this space is called Velodyne, been doing it for years for defense and aviation, spin around pointing lasers and waiting for the signal to come back. So you basically use a reflected signal back and the time difference it takes to build is 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 car maker said that's really nice but I can't put $4,000 sensors on each corner of a car and get it to market at some price that a consumer is willing to pay. So... Until every car has one and then you get the mobile phone effect, right? Yeah, but the economy's a scale at $8,000 and we're looking at that going, that's a little stuff. So there's a lot of startups now saying, listen, we've got a new version of LiDAR that's solid state. It's not a spinning thing point. It's actually a silicon chip with our mems and stuff on it that are doing this without the moving parts. And we can drop the price down to $200, maybe $100 in the future in scale. That starts being interesting. That's $400 if you put it in all four corners of the car. But there's 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, those 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 there's companies like Bosch saying, well, we're strong in car and 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 the laser sensing. So there's all these different sensors. And I think there the answer is not all of them because cost comes into play. So a car maker has to choose what we're gonna use cameras and radar or we're gonna use LiDAR and high definition maps. So they're gonna pick from all of 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 gonna fuse these together and then let their autonomous driving intelligence ride on top of that 3D model and drive the car. Right, so it's interesting you brought up NVIDIA and what's really fun, I think, about the autonomous vehicle and self-driving cars and the advances is it really plays off the kind of Moore's Law's impact on the three tillers of compute, right? Massive compute power to take the data from these sensors, massive amounts of data, whether it's in the pre-program map, whether you're pulling it off the sensors, you're pulling it off a GPS, Lord knows where, Wi-Fi, waypoints, I'm sure they're pulling all kinds of stuff, and then of course storage, you gotta put that stuff, the networking, you gotta worry about latency, is it on the edge, is it not on the edge? So there's really an interesting combination of technologies all bring to bear on how successfully 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 the general industry analyst is. And you mentioned Moore's Law and NVIDIA's taking advantage of that with their GPUs. So let's wrap everything you said into kind of a big answer. Big data and more and more data? Yes, that's a huge factor in cars. Not only are cars gonna take advantage of more and more data, high definition maps are way more data than the map quest maps we printed out. So that's a massive amount of data the car needs to use. But then in the flip side, the car's producing massive amounts of data. I just talked about a whole range of sensors, I talked LiDAR, radar, cameras, et cetera, et cetera. That's producing data. And then there's all the telemetrics 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 wifi cheaply, you can do it over an LTE, and we're looking at 5G radio standards, being able to enable more transfer of data between the cars and the cloud. So that's incredibly important. Cloud data and then cloud analytics on top of that. Okay, now that we've got all this data from the car, what do we do with it? We know, for example, the 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 if I'm a programmer saying if you see this do that, they're taking the information out of the cars and saying, what are the situation these cars are seeing? 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 driving. So there's a massive advantage in sucking data out of cars, massive advantage in pushing data to cars. And we're here at Kingston Sandisk right now today. So storage is interesting as well. Storage in the car, increasingly important for these big amount of data. 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 retrieval flash memory. That's dropping in price as well. All right, so loop in the last thing. That was a loaded question, by the way, and I loved it. And this is the thing I love. This is why I'm bullish and more crazy 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, like I said, we talked briefly about brake pad technology, material science, like what kind of asbestos do we use and how do we dissipate the heat more quickly? That's science, physics, important R&D. It does not take advantage of Moore's Law. So cars have 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 is starting to apply to cars. So they've always had ECU computers, but they're getting more compute put into the car. Tesla has the NVIDIA processes 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 we need them to do. If we're talking about autonomous vehicles, couldn't happen without a huge central process 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 exponential laws in technology. If people look up these other cool things, Criter's Law. So Criter's Law is a law about storage and the rapidly expanding performance of storage. So for each dollar spend, how many megabytes or gigabytes of storage do you get? Well, guess what? Turns out that's also exponential. And your question talked about, isn't big data important? Sure it is. That's why we can put so much into the cloud and so much locally into the car. Huge Criter's Law. Next one is Metcalfe's Law. Metcalfe's Law is a law of networking. And it states basically in its roughest form, the value of a network is valued to the square of the number of nodes in the network. So if I connect my car, great, that's awesome, but who does it talk to? Nobody. You connect your car and now we can have two cars that can talk together and provide some element of car to car communications and some safety elements. Tell me the network is now connected and 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 they're all of a sudden at play in the automotive industry. So anybody who looks back in the past and says, well, the pace of innovation here has been pretty steep. It's been like this. I expect in the future it will carry on and in 10 years we'll have self-driving cars. You can't look back at the slope of the curve and think that's the slope going forward, especially with these exponential laws at play. So the slope ahead is distinctly steeper. Distinctly steeper. And you left out my favorite law, which is Amara's Law, which is we underestimate in the short term or overestimate in the short term and underestimate in the long term. That's all about the slope. It's all about the slope. So Derek, we could go on for probably like an hour now. I know I could. But you got to go, you got to go and see your event. So thanks for taking a minute out of your busy day. Really enjoyed the conversation and look forward to our next one. My pleasure, thanks. All right, Jeff Frick here with theCUBE. We're at the Western Digital Headquarters in Milpitas at the Autotech Council, Innovation in Motion, mapping and navigation event. Thanks for watching.