YouSaid · the spoken record

Dmitri Dolgov

lines on the record: 133
first: 2020-12-20
most recent: 2020-12-20
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Thanks. Thanks for having me. And it's also a huge fan. It doesn't work. Honest Naka, and I really enjoyed it. Thank you.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Be accumulating. I don't want to stop having fun or experiencing new things. And I think it's important that it just kind of becomes additive as opposed to a replacement or subtraction. Those few is probably as far as I got. But ask me in a few years, I might have one or two more to add to the list.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“And contributions back to technology or society people local or more globally becomes a new thing that drives a lot of your behavior and something that gives you purpose and that you derive positive feedback from and so forth. You go through various stages of life if you have kids like that definitely changes your perspective on things. I have three that definitely flips some bits in your head in terms of kind of what you care about and what you optimize for and what matters, what doesn't matter, right? So and so on and so forth, right? And it seems to me that it's all of those things. And as kind of you go through life, you want these to be editive, right? New experiences, fun, learning impact. Like you want to.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of your existence when you just enter this world, right? It's all about kind of new experiences. You get like new smells, new sounds, new emotions, right? And that's what's driving you, right? You're experiencing new amazing things, right? And that's magical, right? That's pretty, pretty, pretty awesome, right? That gives you kind of meaning. Then you get a little bit older. You start more intentionally learning about things, right? I guess actually before you start intentionally learning, probably fun. Fun is a thing that gives you kind of meaning and purpose in the thing you optimize for, right? And like fun is good. Then you get, you know, start learning. And I guess joy of comprehension and discovery is another thing that gives you meaning and purpose and drives you, right? Then you learn enough stuff and it, you want to give some of it back, right? So impact.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Finally, it gets to the point where it is able to answer that question. But of course, at that point, there's the heat death of the universe has occurred, and that's the only entity, and there's nobody else to provide that answer to. So the only thing it can do is to answer it by demonstration. So it recreates the Big Bang and resets the clock, right? I can try to give kind of a different version of the answer. Maybe not on the behalf of all humanity. I think that that might be a little presumptuous for me to speak about the meaning of life on the behalf of all humans, but at least personally, it changes, right? I think if you think about you and your life meaning and purpose and kind of what drives you, it seems to change over time, right? And that lifespan of, you know,”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't know if that makes it more difficult or easier, actually. Very tempted to. Quote one of the stories. Isaac Asimov actually actually titled appropriately titled The Last Question, a short story, where the plot is that humans build a supercomputer, this AI intelligence. And once it gets powerful enough, they pose this question to it. So computer replies as of yet insufficient information to give a meaningful answer. And then thousands of years go by and they keep posing the same question and the computer gets more and more powerful and keeps giving the same answer as of yet insufficient information to give a meaningful answer or something along those lines. And then keeps happening and happening fast forward like millions of years into the future and billions of years. And at some point, it's just the only entity in the universe. It's absorbed all humanity and all knowledge in the universe and it keeps posing the same question to itself. And”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Loving robots, yeah. I think you're exactly right. Unfortunately, I think it's less of a flamethrower typo next day. I think it's more of a, in many cases, can be more of a slow boil. And that's the danger.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“And you asked what made an impression on me and books that people should read that one, I think, falls in the category of both, you know, definitely it's one of those books that read. And you just kind of put it down and you stare in space for a while, that kind of work. I think there's lessons there people should not ignore. And nowadays with everything that's happening in the world, I can't help it, but have my mind jump to some parallels with what Orwell described. And this whole concept of double think and ignoring logic and holding completely contradictory opinions in your mind and not have that not bother you and stick into the party line at all costs, there's something there.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“In my room, and then I'll like over the holidays, I just my parents couldn't drag me out of the room and I read the whole thing cover to cover. And I really enjoyed it. And that's one more. For the third one, maybe a little bit darker, but it comes to mind is Orwell's 1984.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Science fiction writers about this research institute and like it has deep parallels to serious research, but the setting, of course, is that they're working on magic, right? And there's a lot of. So that's their style, right? And other books are very different, right? Hard to be a god, right? It's about kind of this higher society being injected into this primitive world and how they operate there, like some of the very deep ethical questions there. And they've got this full spectrum some more about kind of more adventure style. But I enjoy all of their books. There's probably a couple. Actually, one I think that they considered their most important work. I think it's the snail on a hill. I don't know exactly how it translates. I tried reading a couple of times. I still don't get it. But everything else I fully enjoyed. And like for one of my birthdays as a kid, I got their entire collection, like occupied a giant shell.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Roadside picnic Hard to be a god. Beetle in an anthill. Monday starts on Saturday. It's not just science fiction. It also has very interesting interpersonal and societal questions and some of the language is just completely hilarious.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“A thing that's really interesting, really challenging problem. If you do read it, Master and Margarita in English, sorry, in Russian, I'd be curious to get your opinion. And I think part of it is language, but part of it is just centuries of culture that the cultures are different, so it's hard to connect that. Okay, so that was my first one, right? You had Tamor. The second one I would probably pick the science fiction by the Strogosky brothers. You know, it's up there with Isaac Asimoff and Ray Bradbury and company, the Stragutsky brothers kind of appealed more to me, I think more made more of an impression on me growing up.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“In Russian, only in Russian. And actually, that is a question I had posed to myself every once in a while. I wonder how well it translates, if it translates at all. And there's the language aspect of it, and then there's the cultural aspect. And actually, I'm not sure if either of those would work well in English.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“It is by a Russian novel by Russian author Mikhail Bulgakov. And it's a great book. It's one of those books that you can reread your entire life. And it's very accessible. You can read it as a kid. And the plot is interesting. It's the devil visiting the Soviet Union. But you read it at different stages of your life and you enjoy it for different, very different reasons. And you keep finding deeper and deeper meaning and kind of affected, hadn't definitely had an imprint on me mostly from the probably kind of the cultural stylistic aspect. It makes you one of those books that is good and makes you think, but also has like this really silly, quirky, dark sense of, you know, humor. Hey, casters.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Three books. So, I would, you know, that impacted me, I would say This one is you probably know it well and not generally well known. I think in the US or kind of internationally the Master and Margarita is one of actually my favorite books.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Stake earlier. Like, it shouldn't be in that situation in the first place, right? And in reality, the system comes up. If you build a very good, safe, and capable driver, you have enough clues in the environment that you drive defensively so you don't put yourself in that situation, right? And again, it has, you know, this, if you go back to that analogy of precision and recall, like, okay, you can make a very hard trade-off of neither answer is really good. But what instead you focus on is kind of moving the whole curve up and then you focus on building the right capability and the right defensive driving so that you don't put yourself in the situation like this.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“It's not how you go about building a system, right? You've talked about how you engineer a system, how you go about evaluating the different components and the safety of the entire thing, how do you kind of inject the various model-based safety-based arguments? And you're like, yes, you reason at parts the system. You reason about the probability of a collision, the severity of that collision, right? And that is incorporated. And you have to properly reason about the uncertainty that flows through the system, right? So those factors definitely play a role in how the car then behave, but they tend to be more of the immersion behavior. And what you see, like you're absolutely right, that these clear theoretical problems that you don't acquire that in system and really kind of back to our previous discussion of like what do you choose? Well, oftentimes you made a mistake.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“I would kill both. I think you're exactly right in that humans are not particularly good. I think they kind of phrased this as what would a computer do, but humans are not very good. And actually, oftentimes, I think that freezing and kind of not doing anything because you've taken a few extra milliseconds to just process and then you end up doing the worst of the possible outcomes, right? So I do think that as you've pointed out, it can be a bit of a distraction and it can be a bit of a kind of red herring. I think it's an interesting phyllist discussion in the realm of philosophy, right? But in terms of what that affects the actual engineering and deployment of self-driving vehicles,”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“And that speaks to all of those components of really good state estimation and tracking. And imagine a person on a bike and they're falling over and they're doing that right in front of you. So you have to be really like things are changing. The appearance of that whole thing is changing, right? And a person goes one way. They're falling on the road, they're being flat on the ground in front of you. The bike goes flying the other direction. Like the two objects that used to be one are now splitting apart and the car has to detect all of that. Like milliseconds matter. And it doesn't, you know, it's not good enough to just brake. You have to steer and brake on there's traffic around you. So like it all has to come together and it was really great to see in this case and other cases like that that we're actually seeing in the wild that our system is performing exactly the way.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Crazy events that contribute to the safety around pedestrians. One example that comes to mind that we actually happened in Phoenix where we were driving along. And I think it was a 45 mile per hour road. So pretty high speed traffic. And there was a sidewalk. Next, and there was a cyclist. The sidewalk. And as we were in the right lane, right next to the sidewalk was a multi-lane road. So as we got close to the cyclist on the sidewalk, it was a woman. She tripped and fell. And our cart, this was actually what a test driver are, test drivers did exactly the right thing. They kind of reacted and came to stop and it requires both very strong steering and strong application of the brake And then we simulated what our system would have done in that situation, and it did exactly the same th”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Very dark nights. So it starts with sensing, then perception has to be incredibly good. You have to be very, very good at detecting. Pedestri In all kinds of situations, in all kinds of environments, including people in weird poses, people kind of running around and being partially occluded. So that's stock number one. Then you have to have in very high accuracy and very low latency in terms of your reactions to these actors might do. And we've put a tremendous amount of engineering and tremendous amount of validation into making sure our system performs properly. And oftentimes it does require a very strong reaction to do the same thing. And we actually see a lot of cases like that, the long tail of really rare. Really.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Incredible amount of performance across your whole stack. Starts with hardware. And again, you want to use all sensing of modalities available to you. Imagine driving on a residential road at night and kind of making a turn and you don't have headlights covering some part of the space. And like, you know, a kid might run out. And lighters are amazing at that. see just as well in complete darkness as they do during the day, right? So again, it gives you that extra. Margin in terms of capability and performance and safety and quality. And in fact, we oftentimes in these kinds of situations, we have our system detect something, in some cases even earlier than our trained operators in the carm I do, especially in conditions like.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“But in all seriousness, safety of vulnerable road users, pedestrians or cyclists is one of our highest priorities. We do a tremendous amount of testing and validation and put a very significant emphasis on the capabilities of our systems that have to do with safety around those unprotected vulnerable road users. Cars just discussed earlier in Phoenix, we have completely empty cars, completely driverless cars, driving in this very large area. And some people use them to go to school. So they will drive through school zones. So kids are kind of the very special class of those vulnerable user road users, right? You want to be super, super safe and super, super cautious around those. We take it very, very, very seriously. And what does it take to... Be good at it.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, just, you know, just state on the record, we care deeply about the safety of pedestrians, you know, even the ones that don't have Twitter accounts.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“There's also kind of larger context that doesn't have that locality, and you also see that in driving, right? What is happening in the scene as a whole has very strong implications on the next step in that sequence, whether you're predicting what other people are going to do, whether you're making your own decisions, or whether in the simulator, you're building generative models of humans walking cyclists riding and how the car is driving.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“In more and bigger models and models that have more structure to them, not just large bitmaps and reasonable temporal sequences. And some of the interesting breakthroughs that we've seen in language models, transformers, GPT-3 inference, there's some really interesting applications of some of the core breakthroughs to those problems of behavior prediction as well as decision making and planning. You can think about it, kind of the behavior, how the path, the trajectories, how people drive, they have kind of a share a lot of the fundamental structure, this problem. There's sequential nature. There's a lot of structure in this representation. There is a strong locality, kind of like in sentences, you know, words that follow each other. They're strongly connected.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“In fact, from the earliest days, I think 2010 is probably the year where Google, maybe 2011 probably, got pretty heavily involved in machine learning, kind of deep nuts. And at that time, it was probably the only company that was very heavily investing in state-of-the-art ML and self-driving cars. And they go hand in hand. And we've been on that journey ever since. We're pushing a lot of these areas in terms of research at Waymore, and we collaborate very heavily with the researchers in alphabet, and like all kinds of Mel, supervised the ML, unsupervised the ML, we've published some interesting research papers in the space, especially recently. It's just a super, super active learning as well. Yeah, so super, super active. And of course, there's kind of the more mature stuff, like, you know,”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, absolutely. I think we've been going back to the earliest days, like DARPA, the DARPA Grand Challenge. Our team was leveraging machine learning as like pregenet. And it was a very different type of ML. And then I think actually it was before my time. But the Stanford team during the grand challenge had a very interesting machine learned system that would use lighter and camera when driving in the desert. And we had built the model where it would kind of extend the range of free space reasoning. We get a clear signal from LIDAR. And then it had a model that said, hey, like this stuff on camera kind of sort of looks like this stuff in LIDAR. And I know this stuff that I've seen in LIDAR, I'm very confident in this free space. So let me extend that free space zone into the camera range that would allow the vehicle to drive faster. And then we've been building on top of that and kind of staying and pushing the state of the art ML in all kinds of different ML over the years.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Or a cost function in your stack, but it is important to be able to inject that clear semantic signal into your stack. And that's what we do. But then the question of like, and that's when you apply it to yourself, when you are making decisions whether you want to stop for a red light or not. But if you think about how other people treat traffic lights, we're back to the ML version of that. Because you know they're supposed to stop for a red light, but that doesn't mean they will. So then you're back in the very heavy. Maldomain where you're picking up on very subtle keys about they have to do with the behavior of objects, pedestrians, cyclists, cars, and the entire configuration of the scene that allow you to make accurate predictions on whether they will in fact stop or run a red light.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Different models with different degrees of inductive bias that you can have and combining kind of model free approaches with some model-based approaches and some rule-based physics-based systems. So one example I can give you is traffic lights. There's problem of the detection of traffic light state, and obviously that's a great problem for computer vision confidence, that's their bread and butter, right? That's how you build that. But then the interpretation of a traffic light, then you're going to need to learn that, right? You don't need to build some complex ML model that infers with some precision and recall that red means stop. It's a very clear engineered signal with very clear semantics. So you want to induce that bias. Like how you induce that bias and that whether it's a constraint.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“And of course, object detection and classification, like you're finding pedestrians and cars and cyclists and cones and signs and vegetation and being very good at estimating detection classification and state estimation, there's just stable stakes. That's step zero of this whole stack. You can be incredibly good at that, whether you use cameras or light as a radar, but that just stable stakes. That's just step zero. Beyond that, you get into the really interesting challenges of semantic understanding at the perception level. You get into scene level reasoning. You get into very deep problems that have to do with prediction and joint prediction and interaction, social interaction between all of the actors in the environment, pedestrian, cyclists, other cars, and you get into decision making, right? So how do you build a lot of systems? So we leverage ML very heavily in all of these components. I do believe that the best results you achieve by kind of using a hybrid approach and having different types of ML, having”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Inductive bias into your system. There's tremendous power in being able to do that. So there's no Part of our system that is not heavily leverage data-driven development or state of the RTML. Whether it's mapping, there's a simulator, there's perception, object level, perception, whether it's semantic understanding, prediction, decision making, so forth and so on.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Back so you can optimize the whole system. Join me. So I would decouple, and I guess what you're seeing in terms of the fusion of the sensing data from different modalities, as well as kind of fusion in the temporal level, going more from frame by frame, where you would have one net that would do frame by frame detection and camera. And then, you know, something that does frame by frame and lighter and then radar. And then you fuse it in a weaker engineered way later. The field over the last decade has been evolving in more kind of joint fusion, more end-to-end models that are solving some of these tasks jointly. And there's tremendous power in that. And that's the progression that you kind of, our stack has been on as well. Now, so I would decouple the sensing and how that information is used from the role of ML in the entire stack. And I guess there's trade-offs and modularity and how do you inject”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I think this is one of the trends. You're seeing more of that. You mentioned end to end. There's different interpretations of end to end. There is kind of the purest interpretation of I'm going to have one model that goes from raw sensor data to steering torque and guest breaks. That's too much. I don't think that's the right way to do it. There's more smaller versions of end-to-end where you're kind of doing more end-to-end learning or core training or depropagation of signals back and forth across the different stages of your system. There's really good ways it gets into some fairly complex design choices where on one hand you want modularity and decompositibility, the composability of your system. But on the other hand, you don't want to create interfaces that are too narrow or too brittle, too engineered where you're giving up on the generality of a solution or you're unable to properly propagate signal, you know, reach signal forward and losses.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Whether it's LIDAR's returns and some auxiliary information, it's not just distance, right? And angle and distance is much richer information that you get from those returns, plus really rich information from the radars. You fuse it all together and you feed it into those massive ML models that then lead to the best results in terms of object deduction classification state estimation. So there's a side to interrupt, but there is a fusion. I mean, that's something that people didn't do for a very long time, which is at the sensor fusion level, I guess, like early on fusing the information together so that the sensory information that the vehicle receives from the different modalities or even from different cameras is combined before it is fed into the machine learning models.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“The question, and I'm not saying you can't do machine learning on LIDAR, but the question is that how much of driving can be learned eventually? Can we do fully autonomous that's learned? Learning is all over the place and play is a key role in every part of our system. As you said, I would decouple the sensing modalities from the ML and the software parts of it. LIDAR, radar cameras, it's all machine learning. All of the object detection classification, of course, like that's what these modern deep nuts in continents are very good at. You feed them raw data, massive amounts of raw data. And that's actually what our custom build lighters and radars are really good at. And radars, they don't just give you point estimates of objects in space. They give you raw, like physical observations. And then you take all of that raw information. There's colors of the pixels.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Really not doing multitask learning, but it's computing the drivable area as a machine learning task and hoping that down the line this level two system that's driving our assistance will eventually lead to allowing you to have a fully autonomous vehicle. Okay, there's an underlying deep philosophical question there, technical question of how much of driving can be learned. So LIDAR is an effective tool today for actually deploying a successful service in Phoenix, right, that's safe, that's reliable, et cetera, et cetera.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“If we look at what Tesla is doing as a machine learning problem, they're doing a multitask learning thing where it's just they break up driving into a bunch of learning tasks and they have a single neural network and they're just collecting huge amounts of data that's training that. I've recently hung out with George Hotz. I don't know if you know George. I love him so much. He's just an entertaining human being. We were off mic talking about Hunter S. Thompson. He's the Hunter S. Thompson of Autonomous Driving. Okay. So I didn't realize this with Comma AI, but they're really trying to do end-to-end, like looking at the machine learning problem.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, the push 9 11, like everyone says the most beautiful no, no, it's like a baby car. It doesn't make any sense. But everyone, it's beauty's an eye, the beholder. You're already looking at me like, what is this kid talking about? I'm happy to talk about you're digging your own hole. I will not comment on the Porsche monologue. Okay. All right. But aesthetics, fine. But there's an underlying philosophical question behind the kind of lighter question is like how much of the problem can be solved. With computer vision, with machine learning. Think without sort of disagreements and so on. It's nice to put On a spectrum because Waymo is doing a lot of machine learning as well. It's interesting to think how much of driving, if we look at five years, 10 years, 50 years down the road can be learned almost more and more and more end-to-end way.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Second one is aesthetics. I don't think that's a real issue either. Beauty is an I, the beholder. You can make LiDAR sexy again. I think he was atheist. I think it is sexy. Like, honestly, I think form function. You know, I was actually, somebody brought this up to me. I mean, all forms of LIDAR. Even like the ones that are big, you can make look. I mean, you can make look beautiful. There's no sense in which you can't integrate it into design. Like there's all kinds of awesome designs. I don't think small and humble is beautiful. It could be like brutalism or like it could be like harsh corners. I mean, like I said, like hot rods. Like I don't like, I don't necessarily like, oh man, I'm going to start so much controversy with this. I don't like Porsches.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“And like, I don't find either of those honestly very compelling. So on the cost side, there's nothing fundamentally prohibitive about the cost of writers. Radars used to be very expensive before people started before people made certain advances in technology and started to manufacture them at massive scale and deploy them in vehicles. Similarly with lighters. And this is where the lighters that we have on our cars, especially the fifth generation, we've been able to make some pretty qualitative discontinuous jumps in terms of the fundamental technology that allow us to manufacture those things at very significant scale and add a fraction of the cost of both our previous generation, as well as a fraction of the cost of what might be available on the market, off the shelf right now. And that improvement will continue. So I think cost is not a real issue.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“What might make sense for one product or one business might not make sense for another one? So if you're talking about driver assist technologies, you make certain design decisions and you make certain trade-offs and you make different ones if you're building a driver that deploy in fully driverless vehicles. And LIDAR specifically, when this question comes up, typically the criticisms that I hear or the counterpoints that cost and aesthetics.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“I wouldn't characterize it exactly that way. I know, I think Leider is very important. It is a key sensor that we use, just like other modalities, as we discussed, our cars use as cameras, lightars, and radars. They are all very important. They are at the kind of the physical level. They are very different. They have very different physical characteristics. Cameras are passive, lighters and radars are active. Use different wavelengths. So that means they complement each other very nicely and together combine much safer and much more capable system. So to me, it's more of a question, why the heck would you handicap yourself and not use it? One or more of those sensing modalities when they undoubtedly just make your system more capable and safer now.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“It's very important. So we put significant effort. Creating those partnerships and those relationships with governments at all levels, local governments, municipalities, state level, federal level. We've been engaged in very deep conversations from the earliest days of our projects whenever at all of these levels, whenever we go to test or operate in a new area, we always lead with a conversation with the local officials. But the result of that, that investment is that, no, it's not challenges we have to overcome. But it is a very important that we continue to have this conversation.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“We've been testing all over the place. I think we've been testing in more than 25 cities. We drive in San Francisco. We drive in Michigan for snow. We are doing significant amount of testing in the Bay Area, including San Francisco.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Is this your version of asking the question, like, you know, Dimitri, I know you can't share your commercial and deployment roadmap?”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“That's what we're doing right now. We're incorporating all those things that we learned into that next system that then will allow us to kind of copy and paste all over the place and to massively scale to more users and more locations. We just talked a little bit about what does that mean along those different dimensions. So on the hardware side, for example, again, it's that switch from the fourth to the fifth generation. And the fifth generation is designed to kind of have that property”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“So, we're not quite there yet. We're not at a point where kind of massively copy and pasting all over the place. But Phoenix, we did in Phoenix, and we very intentionally have chosen Phoenix as our first full deployment area exactly for that reason, to kind of tease the problem apart, look at each dimension, focus on the fundamentals of complexity and de-risking those dimensions, and then bringing the entire thing together to get all the way and force ourselves to learn all those hard lessons on technology, hardware and software, on the evaluation deployment, on operating a service, operating a business using actually serving our customers, all the way so that we're fully informed about the most difficult, most important challenges to get us to that next step of massive copy and pasting, as you said. And, uh,”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“Lisa's just go out the door and get deployed across the fleet. So we've gotten really good at that in Phoenix. That's been a tremendously difficult problem. But that's what we have in Phoenix right now. That gives us that foundation. And now we're working on kind of incorporating all the lessons that we've learned to make it more efficient to go to new places and scale up and just kind of stamp things out. So that's that second dimension of evaluation and deployment. And the third dimension is product commercial and operational excellence. And again, Phoenix there is providing an incredibly valuable platform. That's why we're doing things end-to-end in Phoenix. We're learning, as we discussed a little earlier today, tremendous amount of really valuable lessons from our users getting really incredible feedback. And we'll continue to iterate on that and incorporate all those lessons into.”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source
“So, similarly, hardware is a very discrete jump, but similarly to how we're making that change from the fourth generation hardware to the fifth, we're making similar improvements on the software side to make it more robust and more general and allow us to quickly scale beyond Phoenix. So that's the first dimension of core technology. The second dimension is evaluation and deployment. How do you measure your system? How do you evaluate it? How do you build a release and deployment process where with confidence you can regularly release new versions of your driver into a fleet? How do you get good at it so that it is not a huge tax on your researchers and engineers? So you can, how do you build all these processes, the frameworks, the simulation, the evaluation, the data science, the validation, so that people can focus on improving the system and kind of the”
2020-12-20 · Lex Fridman Podcast · #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars · IDENTIFIED FROM THE TRANSCRIPT · source