This Week in Startups - E1110 The Next Unicorns E18: Ghost Locomotion CEO John Hayes builds self-driving kits for consumer vehicles, shares insights on cameras vs. Lidar, targeting highways, 5G’s impact on self-driving & more

Episode Date: September 17, 2020

Check out Ghost: https://driveghost.com FOLLOW John: https://twitter.com/ghosthayes FOLLOW Jason: https://linktr.ee/calacanis

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What does unicorn potential mean? It's just an arbitrary thing here in Silicon Valley. When a company becomes worth a billion dollars, people tend to think, well, that's a milestone. And I guess it is. It's an arbitrary one, just like trying to pick the next unicorns is an arbitrary one. But sometimes forcing yourself to pick the next 10. requires a process. And our editorial team and researchers went to work looking at where are the signals for a company to be a large, sustainable, humanity-changing company. And we try to challenge

ourselves every year to do this series. And we're eight episodes in. And today's going to be fantastic. We're going to talk a little bit about self-driving and autonomy, which we've been talking about for years on this podcast. And it seems like we're slowly getting there. If you want to look back at the other episodes, which started around episode 1089. Yes, we've done a thousand episodes of this podcast. Daphne, the co-founder of Coursera, was on talking about Encitro, which does drug discovery by machine learning. That was really great.

Nikki Peckett was on, talking about homebound and building new homes from the bottom up. We had a bunch of other episodes. One that came up that people really seemed to like was Cody Freezing. Cody freezing from zero mass water. And they do those hydro panels you put on your roof and magically two cases of bottled water appear. Not literally, but that amount of water appears. And that was pretty inspiring for folks.

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the program is John Hayes from Ghost Locomotion, which they just go by Ghost. Welcome to the podcast, John. Hey, glad to be here. Thanks for doing this. I know we're in the middle of a pandemic. And here in California, we're having wildfires. May I ask, where are you and are you safe? Well, I'm at home right now, so I'm feeling pretty safe. Great. And so is most of our staff. They're at home too. We're safe.

Are you in Los Angeles, Arizona? Oh, no. We're in Mountain View, or I'm in Mountain View. Our company's Mountain View. Most everyone else is within a few miles of there. How's the smoke down there? I'm mid-peninsula. So how's the smoke down there?

It's much better than two days ago. Yeah, two days ago. So now it's only slightly orange and a bit foggy, but it's quite breathable now. It is a crazy moment in time. How is your team handling this? What I would say is like just an incredible run of, I think we're all having humanity in 2020 between, of bad luck, the pandemic. Obviously, we had some of the race protests and racism protests. I think it's an accurate description of them.

The election is causing a lot of people anxiety, work from home, shelter in place. And the economy, I mean, these things are now. these fires. People are, I don't know if you're having this experience in your circle or your company, some people are cracking a little bit under this six months of just bad news. And let's face it, staying at home and being isolated and then having to stay indoors now with this, you know, 10 days of really unbelievably horrible air quality. It's breaking people. Are people holding up okay over there? And are you dealing with that as the chief psychologist officer at your startup? by default.

I have help on the psychology front. People are handling remarkably well. And I think that because we have a huge software focus in our company, people have been mainly able to just keep doing their jobs. And it varies. For a lot of people, it's like working on a great project like this is a huge positive focus for them. And the team has kind of split up.

Like there's some people who have to go in because we do do some hardware development. We do some operations. But overall, it's like I went into this. I'm personally not a fan of remote work. And previously, we basically had a policy that everyone has to be in the office. And we actually have turned down people who wanted to be remote maybe a couple days a week. And so the performance of the team has just been incredibly positive. So, I mean, this kind of changed my mind.

Yeah. So I think we're both old school and we like people to come to work if they have a job. Crazy concept. It's not just that. It's that this is an intensive engineering project where you're closely collaborating. There's a lot of like really hard problems and you need to talk to the people around you. It's not like we can just write out stories and send them off to anyone to develop. Right. So now some number of people, I guess the software team might be working from home and the hardware team needs to be in the office. You have to take certain precautions in the office.

How is that all gone? I'm curious. And then we'll get into sort of the mission of ghost and what you're doing. That's gone okay. I mean, part of it is that there's enough people out of the office that it's pretty sparse. But when you're working on hardware, you have a bunch of equipment and you have labs. And of course, we're building stuff into cars, so we have a car lab. And so, you know, you take precautions. You take breathing precautions, you check.

But it hasn't really changed the actual work. And I think that, you know, almost. everything has gone on. For some self-driving companies that do a lot of driving, they had to suspend a huge part of their operations. And that didn't hit us. And to some degree, I think that it hit us at a lucky time. If we had been at an earlier stage where we were like, say, raising money, saying March or April, we would have been in trouble. If we had been in a later stage where we are like just beginning scaling up consumer operations, that also would have been a huge problem. but we're just in this like really concentrated R&D stage where like most of our staff

is engineers and so engineers just keep engineering. So when you're in the laboratory phase, it's actually heads down and actually not a bad time to, yeah, have never got to have a pandemic. Yeah, it's not a bad time to like hunker down and just do some hard work and get it done. So the company, a ghost, is building add-on hardware that you can put onto any car to make itself driving. That's correct. That's correct.

Now, why take that approach as opposed to building from the bottom up as Tesla is doing as way, no, Wayman was adding equipment, but they are, I believe they were also considering and they had built some test models of their own cars. How does one come to the, or how did you come to the conclusion that the best thing to do in 2017, I think when you started was to add on to existing fleets? So in 2017, we looked at the AV industry and there was a bunch of companies and one of the things we saw was a lot of them were doing the same strategy. This was a very robotics oriented strategy where you put a quarter million to a half million dollars of equipment on a car and then you have a fleet and you drive them around to essentially test your system. And one of the insights we have, I don't have a background of robotics. my background is in software, both enterprise software and consumer software. My co-founder, his background is in verified systems and machine learning. But we looked at AD and said, what if it's not a robotics problem?

And if you look at like California accident reports, like you can read them because every single collision is there, one of the things that you see is when you take sort of an engineering eye when you look at them, you realize that all the problems are caused by software. No one actually has any hardware problems in this space. And a lot of the equipment that they're using is 10 plus years old. Fascinating. So you looked at what I would call maybe the fourth or fifth inning of self-driving. You know, we're like sort of, I think we'd all agree we're more than halfway there.

You looked at this halfway mark and said, wait a second. If I look at Waymo and Tesla's crash reports and Uber self-driving unit, every single time there's a problem. It has not been a failure of LIDAR or the Kemp. camera or the other sensors, it has been a software problem. So we should relentlessly focus on the software part. So then what is the business you're going to be in eventually? Are you building this enabling technology to sell it to other self-driving companies and have components of the software solved? Or are you going to sell it to car companies? Or are you going to sell it to consumers and let them retrofit their cars? What is the business model here? So the first business model is to

actually retrofit consumers' cars. Wow. So you come in with a car. And it's kind of like getting a stereo installed like back in the day where we put down wires. Benzy box, what do they call those? The ones that slide out, a benzibox with the handle. Yeah, we actually put a computer in the trunk where you used to put a CD changer. Perfect. We put cameras in the car. They're glued onto the car onto the front on the sides.

And it's all very small. It's connected to the controls of the car. And so that we can transform someone's car. Wow. With essentially consumer equipment. And is there a specific type of car that you've white labeled and said, hey, we're going to do the Honda core, the Civic, the Prius, because those are so plentiful out there, and that's our target market? Or are you looking at more high-end cars and saying, hey, this is going to be a $10,000 or $5,000 option. We should put it on these other cars.

Answer that question when we get back on this weekend startups. Hey, everybody, you know Dell has been sponsoring this week in startups, and they've been a tremendous supporter of me for many years. And I have been a tremendous supporter of Dell's. Long story short, Dell for entrepreneurs has really been trying to help every single one of our startups. And we're very lucky today to have Mobelagji. So come be on the program. And he runs Dell for entrepreneurs. Yeah, Jason, yeah, thanks.

Thanks for having me. As you said, my name is Mobile IG Supri. I oversee strategic partnerships and the center for entrepreneurship for Dell's small business in the U.S. What do entrepreneurs tell you they need and what do you provide for them? With the current partnership we have right now, Dell's small business in the States, right? We have 500 IT advisors, startup IT advisors dedicated to our program. Half of those are based in Nashville Tennessee our second largest physical location in the U.S. And the other half is based in Roundwalk, Texas, where we have our global headquarters.

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and he is the CEO and co-founder of Ghost locomotion, or just known as Ghost, their website is G-H dot-st-T. And when we left our hero, that's you, John. We were talking about maybe the cost of this to put on a car and then what cars this will be available for, because am I right that this is going to have to be adjusted on a pro-car basis or are cars similar enough that you could pretty much slap it on any car? Well, cars are pretty similar. we do have to do some per car engineering because you can think of cars as like there's an American platform or German platform and Japanese platform.

And so that's sort of the basis of how we integrate into the cars. And then they have a pretty narrow range of things like, say, front windshield angles, you know, various angles around the car. It's all pretty narrow because cars are built to the same safety standards, same aerodynamic standards. But to get to your question about the cost, like right now, our anchor is about. $3,500. And so if you think about... $3,500, so cheap. Wow.

Well, you know, when we looked at this, the comparison is what do people today pay for driver assist features? And what do people? And we know, we did a lot of surveys around what is this sort of worth. But if you translate... And Tesla charges $8,000, I think. Is that the right number or $10?000? They change it every month.

I think right now it's... I think right now you get the first one built in, like basic... Autopilot built in and then there's an extension. Yeah. So if you think about that in terms of what model of car, obviously you're not going to put this on a 10-year-old car that itself is only worth $5,000. We see that the buyers have extremely expensive cars. Like if you're buying a $60,000 plus car, they usually just don't want to do anything to them. And so you're kind of in that mid-range.

And the average new car in the U.S. is about $35,000. So it's like sort of centered around there. Got it. And so what is your test? What is your mule? What do you use as your muse right now in terms of in the lab? What are chopping up in the lab right now in Frankenstein? Yeah. So right now we're using Toyota Camry's because they're really common. No one else uses them because they have no features in them that enable any automation at all. So it's a super basic car very efficiently built. LIDAR or cameras? That was Elon's big bet. He said, I think this can all be done. with cameras. LIDAR is too expensive. Waymo said no way. It has to be this giant 3D model and we have to create a LiD model. It seems like, am I correct that the world is proving Elon right on this crazy bet? And are you taking the same bet that this can be done with cameras and sensors, not

LiDAR? Yeah, we're on team camera. A lot of that is we're on team any sensor that you can buy that has large scale. And that isn't LiDAR today. What does that mean? Well, so it's a large scale. Yeah. Yeah. So the world market for LIDARs today is a couple hundred thousand units. So it is, you know, outside of self-driving cars, it's primarily used for like architects and land surveyors. Like it's just a very, very specialized piece of equipment.

So the idea of high volume LIDAR doesn't exist. There's lots of companies working on it. So maybe it'll exist in the future. But even then, it's never going to hit the scale of cameras. It's like cameras are sold on the scale billions a year. Like every phone has five to seven cameras. is in it now. Right.

So they're also getting better all the time. Is LIDAR getting better all the time or is it just incrementally getting better? It's incrementally getting better. And I think you're right about the cameras. Like imagine the R&D pressure of like the billions of dollars that are spent on R&D to improve cameras, both the hardware and the software. But LIDAR, I mean, the first LIDAR came out in 2004 and that was a 64 laser LIDAR. Right now, the best LIDAR you can buy is 128 laser LIDAR.

Like that's twice as good over 15 years. Yeah. So it's very incremental. And the price has dropped from 50 to maybe 10,000 to put it on a car. Am I correct? Yeah. Well, that's for one. Most of these cars have about a dozen LIDARs on them.

So they're still up in a hundred thousand range, which is ridiculous. Why are people obsessed or certain folks obsessed with having LIDAR when LIDAR hasn't proven itself and is not growing at an exponential pace? and doesn't seem necessary. What would they argue is the reason that LiDAR is so essential and they keep, Waymo keeps investing year after year in LiDAR? Well, there's definitely an evolutionary track because LiDAR was put in the first AV, like back in the DARPA Urban Challenge days.

Right. But it also answers an obvious question, which is it does do ranging. And it is very, very accurate to say, I can avoid colliding with something and I have a sensor that directly detects whether there's an object. But that's existed and we still have collisions, right? So, LIDAR isn't perfect. Well, what I would say is that the LIDAR itself is very, very good, but that's not the problem.

The problem is what do you do once you get that signal? Because there's still a lot of prediction that you have to do because it's not enough to say that you're going to avoid an obstacle. You have to predict the motion around. it. And then there's like a lot of control choices that you have to make. So we're kind of right back to the software problem, which is what caused all the problems. What, if we look at the problems of identifying objects on the road in the path of the vehicle, that seems to be the problem that people have not yet solved. So is this a plastic bag flying across the road a garbage

bag that just fly off the back of somebody's truck? Or is it a bicyclist? Like, a plasticist? Like, the Uber tragedy that we saw in, I think, Arizona where the safety driver was not actually looking at the road. They were playing Candy Crush or doing WhatsApp. When you look at that specific acute problem, how close are we to solving that one? And then what percentage of the autonomous vehicle accidents have been because of trying to identify? And what is that problem called? Is it called the floating, you know, plastic bag problem? Or what is that problem called in your world? Is there a name for it? If not, we should name it now. There's not a name for it, but if you look at the Uber scenario, okay, you had a clear night, you had a perfectly

straight road, you had exactly one obstacle. This was not a problem of combinatorics. And the system But in TORX means multiple problems at once? Right. This was not a complicated scene. This is one car and one person. And if that car had made any different decision, the collision would not have occurred. So this is, we're not in the world where what you see from these platforms is trying to solve subtle problems like floating bags. Right.

What you need is, it's not a floating bag problem. This was like a major object going across the road problem. So did they ever discover what the problem? was there? Yeah, there was a pretty thorough report on that. And a lot of it was actually around what you discussed is like they had a pipeline, which is fairly standard, say let's identify an object. And then we'll put it to another complex piece of software to say what to do about that.

And then they had like other control software that tries to override it. And they had a problem where their camera and their LIDAR, even though it definitely appeared on the LIDAR, was kept reclassifying. It was like, is this a vehicle? Is this a bicycle? And it kept kind of forgetting its history. Wow. Whereas any normal person, a human being has an instinct.

It has an intuition. There's something there. We should slow down. So a human might be more precautionous and say, you know what? I slammed on the brakes. I thought I saw something. It was actually a floating bag. Whereas a computer would say, I looked at it.

Is it a bike? No, it's a floating bag. No, it's a cloud. No, it's fog. No. And then it got caught in that loop. Yeah. It didn't make a decision.

It was a non-decision problem. Well, and it was also a classification problem. Classification problem, yeah. Which is, how about not colliding with anything? Like, instead of identifying an object, like, how about you don't collide with it? Or you have a much simpler system that has very simple rules about things you must not do. So when we think about the software problem, it's like, how do you create layers of software where you can have, you know, sometimes very sophisticated software at the top, which is dealing

with complex scenarios. But then you have much simpler software that has very basic rules built into it. Like a prime directive. Like it, if you don't kill your creator. No matter what they are. Like Robocop, don't kill the creator. Like, don't slam into something. But what I want to understand is how you deal was we talked about the floating bag. I think it's a nice way to name it, the floating bag problem. When we get back for this quick break, I want to know about the slamming on your brakes and causing an accident behind your problem. We'll make it back on this week and startups. Let's get down to brass tax here. I'm going to give you 50 bucks for your LinkedIn jobs.

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Because they're giving you 50 bucks. Okay, let's get back to this amazing episode. Hey, everybody, welcome back to this week. And stars my guest is John Hayes. He is with Ghost. G-H.st. Did I get it right? G-H.

dot-st. Or driveghost.com. Or drive-ghost.com. And he's got 80 people. They've raised tens of millions of dollars from our friends over at Kosla, a friend of the show, Keith Rabeau, who's been on, I think, four episodes, now a great guest. And our friend, Vinok Kosla, we had a great interview with him, also on the podcast. And they've got about 80 people here in Mountain View, and they're trying to solve software

and build add-on components. you can drive your, you know, F-150 maybe even into the shop and then put self-driving on it. When do you think you're going to launch the self-driving? When will I be able to drive my, you know, 1990s, you know, Land Rover defender into the shop and have you put stuff on it or whatever, it is Camry as it is? When do you think you'd be ready for market to have trial customers on it? So I think that we're going to begin doing trial customers next year. Wow.

We probably won't be able to support a 1990s defender. We need some electronics in the car. So there's a minimum stack, right? Like the OD. What is it, the ODB port or something? You need to have that? Yeah. That's to be on Canbus.

That's like 2008. The other thing we integrate with is electric steering as opposed to hydraulic steering. The breaking point there is about 2012. So it's most cars past 2012. Got it. All right. So we talked about the floating bag problem. Is there anything we didn't cover in the floating bag problem of why that's so hard? Because we do have fog and you do have, you know, air is got particles in it. And I understand air, it can be difficult for computers to understand what's happening in air. So have we solved air and does air and air density and fog and humidity, is that still an issue with self-driving and the floating bag problem? Or have we kind of solved that? I think that air is not that big a problem because the nice thing is it has a very linear relationship. And you can even see this in video game engines where there's fog in the video game engine.

What it does is as further away objects become less colorful. So it can actually be used as a positive cue to figure out how far away something is. Got it. Further away objects are less colorful. Yes. To the camera and to the human eye. To your eyes as well. It's just a physical effect.

Yeah. And so how close are we to the floating bag problem being solved? Because that's going to solve, that bag problem is going to cause the slam on your brakes problem, which then you get rear-ended. So I guess we'll call that the rear-ended inadvertently slamming on the brakes problem. Is that inadvertently slamming on the brakes problem happened with Tesla's and Waymo's on the road and the Uber soft driving programs because you're studying those problems? Or is it generally clipping something that it should have stopped for? So the majority of collisions that occur with with Teslas, we don't have good data on Uber, are usually, we would say, false negatives in that they hit things that they shouldn't hit.

And there was a spate of them running into emergency vehicles. And some of that is a consequence of automotive radar. And so, you know, what you're raising is a real concern. So these vehicles that have these safety systems tend to be tuned to avoid slamming on their brakes, even if it means that they're going to hit more things in front. And so what they have is they're typically using automotive radar, which has trouble distinguishing things that are stopped in your lane from other fixed objects in the scene like signs or overhead bridges or other things

that also look stopped because its resolution is very low. And so that's why you tend to see that type of failure. The plastic bag thing, I don't believe that that's going to turn out to be a big problem. Got it. So the stationary objects, a police officer pulls off to the side of the road, but one third of their vehicle is still in the road. Or some painting truck is painting the leftmost lanes, leftmost stripe or putting down cones. That's what we see on some of the Tesla accidents is they just clip. And it's the front side driver is going to get that impact, but not the middle of the car or the side of the car. And the reason you're telling me is the rate. not LiDAR, the radar doesn't know, hey, that's the overpass that you're coming up on versus a police cruiser in the side lane. That is the problem. Yeah, because they often filter out everything that's not moving because they're trying to filter out all the fixed objects in the scene. Got it.

And you can see that warning directly in the owner's manual. In the Tesla autopilot owner's manual is that, hey, you have to keep your eyes in the road because we haven't solved this problem yet. You're trying to solve that problem. What is the solution to that problem? Is it using LiDAR? Would LiDAR solve that? Or is it using more cameras at different angles or more radar? What solves that problem?

So for us, we're solving it with cameras and particularly we're solving it with video. So most of the time, like camera systems, like go back to ProPilot 1 or what's available in most cars, they go through two stages. They try and recognize what a vehicle looks like in a still frame. And they put a box around that. And then they use that box size to estimate how far away the car is. And what we do instead is we use sequences of video and search for parts of the scene that are expanding. And this is a lot of, this is just like how your retina works, where you have specific sensors for

looking for expanding or contracting objects. And so we build a model that's similar to that. And what's nice is that provides first, close collision avoidance that isn't dependent on the texture of the car. So we don't have to learn every single type of car. That's really important when you want to make a universal collision avoider at a video. But the other thing is it's also perceptual. And so if you think about the rag quality, sometimes you get like autopilot or other company's products can be laggy in how they respond to vehicles looming because they don't

use the same perceptual model that people have for deciding what's a comfortable distance or what's a comfortable rate of expansion. What does looming mean in this context when you say looming? Looming just means, you know, you don't see the world in 3D. You see it in 2D. Right. And so it's just how quickly is that expanding over your retina? Got it.

So like Jurassic Park objects in the review mirror may appear larger than they actually are, or small in them or whatever, when something is charging at you like a T-Rex, it's getting bigger. Yeah. So this is why we have this in our system specifically is because we fear predators. Predators come charging at us and it does something to our. somatic system when something gets big real quick. It makes us alert. Yeah, you have a direct sensor that connects from your retina directly to your motor cortex

just for looming objects. Oh, wow, I didn't know that. What a great feature by Darwin. When did Darwin add that one? By the way, there's going to be a lion that's going to charge at you. We don't have the time for you to process it in your brain. We're just going to go right to your spinal cord and make you run. It is true.

You see, and that's why you get a jump scare in a movie. Because in a movie, they'll just have some object come charging at you from the closet of a ghost or something or a serial killer or something. And it makes you jump. And it's called the jump scare because you don't even have a chance to process it. It's just the size of it getting bigger. That makes you jump. That's fascinating. So I guess the big question on everyone's mind when they're listening to this is, when do we think that full self-driving point to point, which I think most people would refer to.

as level four. This is when you don't have to have your eyes on the road. Level one is you give over one function. Level two is you give over two functions, stay in the lane, adaptive cruise control. Level three is you can be somewhat distracted. And level four is you can basically sleep, right? So, yeah, the way it works is level three and level four are hard to distinguish. We would classify ourselves as level three, meaning that there's a protocol for when the system is engaged, you can be disengaged. And if the system decides that, that it can't continue to the destination, it has to pull over. It's still, it can't demand that you wake up and pay attention.

I don't think that's a very good product idea. So it has to come to a safe state. And so the main difference between level three and level four is how much redundancy there is in the system with the main issue being power train redundancy. So no one has really made a level four product today. Ah, interesting. So level four means if it has a problem, it can take over itself. and try to resolve the problem?

Yeah. So if there's certain types of failures, it has a limp home mode. It can be more artistic around getting to a safe condition. But it's a pretty fuzzy distinction. And I look at it from a product point of view. It's like, what do people want? Well, they want to make sure that when they start the system, that it's not going to get them into a state where they suddenly have to take over.

Okay. So this is what's on everybody's mind. We all like driving to a certain extent. What we don't like is having to exclusively drive because we all got ADHD and we'll have smartphones on our dashboards. So what everybody wants to know is not when I'm going to remove the steering wheel because we kind of have an affinity and we don't mind driving, I think by and large. But we would like to be able to pick our podcast or maybe reply to a text message

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Okay, let's get back to this amazing episode. Hey, everybody, welcome back. It's our next unicorn series, number eight of 10. We've got two more to go. Today on the program, John Hayes, from Ghost. And you can go check out the website, G. G-H-nesty. You're working on adding these units to existing cars.

You're going to be able to do that starting next year. So if somebody has a camera, they're going to be able to get it great. And you're working on the software using cameras, LIDARs. You're not opposed to it. You might add it later, but you think you can get it done without LIDAR, correct? That's correct. Okay. So Elon made the right bet there.

Waymo was wrong. Tesla was right. At least the same bet. So we'll take that. Yeah, you're taking the same bet as him. I think it's, I don't think people would doubt that's the wrong bet. It sounds like it's the right bet. So in your estimation, this like level three where I can change the podcast and I can watch a video or I can do an SMS, which candidly, let's be honest, we know that people are in all likelihood doing this already with their Hondas and their Audi's and their Teslas, which have autopilot like features.

People are already starting to maybe take their eyes off the road for five or ten seconds at a time. And they shouldn't, but they do. Is that correct? Just like the Uber driver did? Yeah, that's obviously true. And everyone knows that it's dangerous, but they do it anyways. People are extremely compelled to do things other than driving. Right.

I mean, and this is tragically the Apple engineer who was using his model X and who crashed into a divider. He specifically was not paying attention. on an exit ramp of all places to really push the envelope on a Tesla. One thing in any of these cars, you know, because my Honda also, my Honda Odyssey also has, you can stay in the lane and adaptive cruise control. So it's very much autopilotish. It's just not as good, not even close to as good, I would say, as the Tesla's. I don't think they've really worked on it that much.

But, you know, if you're on the 280, it's one thing. You know, if you're on a straight shot, these things are dialed in. Like, I think they're close to perfect, right, on that kind of synestals. scenario? You would agree? That mission? From the, from the Rhodes point of view, yeah. So it's only if there's a wildcard that happens. But to my question before the cliff hangar in the break, thanks to our sponsors, when will we be able to legally and ethically morally and safety-wise do what pilots of airplanes do? When the pilots put the autopilot on, you know, they'll look down and have a bite of their sam or stuff, some coffee, they'll look at their maps. And then they'll keep

looking out the window. They're not looking out the window constantly to make sure they don't collide with another plane. They have autopilot on and they have sensors and they got a lot more space, obviously, with their X axis, or their Z axis, I guess. When are we going to be able to do that? When won't be able to take a minute or two off, do you think? You had to put a year on it in the United States, in California. When are we going to be able to, for God's sake, you know, flip through our SMS messages without risking our lives? So I think that'll be 2022. And part of the reason why, what? You know, that date is much earlier because the question is, is, is, when you can do it where?

Because what you have on the roads is you have a huge variability in the complexity. And like when we started, people thought that the highway was so simple like in 2017 that the auto companies were going to be able to incrementally innovate their way there. And we've seen that that's not straightforward. And, you know, from our perspective, is like the highway is plenty complex. But you go from there to, you know, from a freeway to a highway, from a regional road, from regional road to like a structured sort of suburban road. And each time you take one of those steps, your decision complexity goes up.

So when I think about this from a product point of view, it's like, well, the simplest product is to go where the decision complexity is the lowest. And that is where the speed limit is the highest. Because, you know, we're humans that have constant rate brains. And so the reason that we can put the speed limit up is because the environment has been so simplified that we're not going to have to make a lot of complex decisions. We've reduced a number of variables. On a highway, you know you're not stopping. You know there's not going to be cars or people on the highway. And if there are, there's something tremendously wrong.

They've made a bad decision. You can deal with them as if they're obstacles in a sense. Right. Because you're your delta speed between someone walking around on highway so high that they're effectively not moving. Right. Compared to if you're in mountain, you're driving on Castro Street at 15 miles an hour, well, someone on a bicycle is kind of going the same speed as you. Or faster in that case. And Silicon Valley, they're definitely going faster.

These crazy venture capitalists on their, like, road bikes are like going 40 miles an hour. So you're really focusing on providing this opportunity on the highway. And our regulators also kind of behind this concept of like, hey, let's master the highways, let's give people that freedom and time back on the highways. That's a great milestone for everybody to work towards, or have they not, or they're just leaving it up to the companies working in the space to sort of submit their application that you want to do this?

Like, who's driving this concept of, let's solve highways first? So I think regulators, you know, are interesting because what they tend to do at the federal level is they tend to document existing practice. And so, for example, lane keeping features that you have today do not have controlling regulations. And that's fairly normal that you introduce a new feature, you let it get engineered, you learn the best practices, and then you codify those best practices.

And that's what constitutes the regulation. Got it. And so a lot of what you'd be evaluated on is what is your engineering process. Have you done the reasoning that says you have a positive case for whether this is going to work? And that's kind of the opposite of a lot of other technology companies where you kind of test something to prove that it sort of doesn't, doesn't work. You're making tests for the reason it does work. Here you have to make a positive case. And that's what you're evaluated on. Interesting. Now, do you have to apply for that? Like, are you going to say to them, hey, we want permission to go level three on these specific highways? What is that process going to be like as we navigate through this? Because I know there are some states, it seems, or some regions. I don't know if it's city or state level, but there seem to be.

be people who are aggressively courting your companies to make their cities more attractive for that business, both in the short term, I think, attracting companies like yours. And then in the midterm, attracting individuals to have a better quality of life because these features are allowed and be progressive. So who's the most progressive? And then how does it work that you actually would, by 2022, be able to say to California, hey, can we, are you going to say these specific highways? We think we've got mastered. Can we get permission for level three here? on 101? So the way it works in California is you apply for various levels, degrees of testing permit.

Now, we're not operating a taxi company nor is anyone else. And there's an additional level of permit where you could say I could sell this. Now, people are fundamentally allowed to just modify their cars. And so you get permission from California to have your product deployed. Now, when you talk about progressive, I would say California is the most regulated of any state in that there are controlling laws for autonomous vehicles. In almost every other state, there are no laws. And so I think that it comes down to a direct negotiation with the executive of the state

and probably the state police to get sort of permission or least acceptance. And how are you going about that? Who do you think, what is your process? Do you think it's California is going to be the way to go? And they've been reasonable and you coming up with examples of what you want to do and saying, hey, here's our plan. or do you think you need to go to Texas or someplace, that's like a little more, you know, wild west, so to speak?

I think that right now we're on the well-worn path of getting testing permits, that you have to make a lot of certifications around your process, how much did you test off the road before you test on the road. No one has proceeded much beyond that point. Got it. And so I think that we're going to discover that probably in 2022. Hopefully we discover it before.

than 2021. Right now, no one knows what the acceptable standard is. And I think that this is the case where AV is such a frontier that no one can point to a formula and say, this is exactly how you engineer it. So we need an existence proof. And then you can take that apart and figure out how they did it. And then that's how you build a standard. Michigan has approved an AV-only highway.

What is the nature of that corridor and is that material for what the AV industry is doing, the autonomous vehicle industry is doing? And do you see that as the future of this? I'm not familiar with what Michigan is doing specifically. I think that for at least the medium term, at least the next 10 or 15 years, you generally shouldn't make a startup that depends on government building expensive infrastructure. And so I believe it's like we're going to be in the world immersed in human driven cars for a very long time. Is AI the way to go about building this rule set in software and just plugging in all of the inputs, all of historical inputs?

So you have all the times it's been disengaged, right? All the times autonomy has been disengaged. All the times there's been access. documents, documented, all the GPS data, every videotape of every accident, all just put into an AI that says go and protect the driver at all cost. Or is this going to be more verticalized and narrow AI or narrow machine learning? We're going to really work on this in sort of segments. And then how is the model shaping up in that regard? So AI is a really big term.

The way we approach it is like you're going to make a model and you make a model to answer a very particular question. Like where are there things I'm going to collide with? Where is paint on the road? What is the sky versus other things we should ignore? And then you can break that question down into what are all the ways we can analyze the data to figure out what are, you know, what are the axes along which that is important? So if I were to want to say all of the things I can collide with, I could start with a very simple thing to say, okay, well, I could have obstacles colliding with me from all different angles.

Like, let's say I'm going to do a clock face. I have my 12 o'clock, my 1 o'clock, 1, 2, a 1. I could have different colors. I could have different sizes. And out of that, you build axes for what you want the system to detect. And so when we think about model building, it's very much an engineering process to say, well, first you have to be very specific about what you want the model to, what question you want

to answer. You have to be very specific about the domain, like what are all the types of inputs. And then out of that, we go into the world because we have cameras on cars and we have a fleet and we look for those situations to occur naturally in the world. And then you can iterate on that and you can say, okay, well, we had two scenarios that we thought were the same that gave us a different answer. So now you put that back into a creative process that says, okay, what axes did we miss so that we can make a new training data set? And so, and out of that, you also get things like holes, like what combinations of the world have a nice scene? And then you can pull that in from your fleet. So when I think about it,

it's like the AI or the neural network itself, yeah, that's how you execute it. But all the engineering goes into the process that actually got you that data and how much human knowledge can you encode into that system? And what is your process for detecting gaps? Yeah. And a gap I always think about because most of these companies are based in California is snow and ice. And are those problems solvable in the near to midterm? Because it seems to me when I drive back from Tahoe, you know, and it's just one sheet of white,

I can't imagine when the highway is a giant sheet of white with a little bit of black ice under it, that an autonomous vehicle is going to be able to figure out a sheet of white and where the lanes are because the lanes become, you know, up for interpretation during these kind of conditions. And people just do the best they can. And they kind of self-form, right? If there was a three-lane highway, it kind of becomes a two-lane. And then you just- You're following ruts and you kind of divide up the space.

Yeah. So what do you think about ice and snow conditions? And I think that goes to, you want to, you want to. answer a very specific question. So this comes back to more narrow AI applications. Right. Like I think you just create a completely new driving mode for snow and ice. Interesting. You just say, look, it's very easy to detect whether it's snow. You look at the road, you say, what is the albedo of the road, or how much light is it reflecting? And you can come up with four different values. This is pretty well understood in the automotive engineering space.

So to detect, like, what is the material of the road surface? And then you can just create completely separate models from that. So you just switch between modes. Your car just might say to you, it appears you're in a blizzard. Your peers are in snow conditions. Please confirm you would like to go into blizzard mode. So it'd be kind of like... Or should just do it.

You should just do it. But I mean, in the midterm or the near term, just like a plane might, you know, during a wind shear, say wind shear, wind shear, or whatever, you know, or like, you might give you some guidance and you kind of help it along. What about ice specifically? Can you detect ice? easily or is ice just, you know, some, you know, we'll get to that 10 years from now. How do you think about ice? I think you detect ice the same way people detect ice, which is you can,

you finally measure how much friction you have at any given moment, which you can do from the car right now. So you get the wheel rotation speeds. The oh shit indicator. Like the car is floating. The car is not gripping. That's, yeah. Basically it's basically the, what do they call it? The all-wheel drive, I guess, when it shifts the power to different wheels. So you actually know that because you're plugged into the computer's main system. Yeah. So you know that. Yeah, the computer tells you how fast all the wheels are rotating. And so detecting that you're on ice is actually a problem that's been pretty well solved in the automotive industry. Interesting. And when they solve that problem, did they hard code that or did they, I can't imagine the automotive industry used AI to figure

out how to manage ice conditions. So there's probably an opportunity there. However good, all-wheel drive is today, and AI all-will drive would probably be better because it would learn about the specific ice at that specific moment and adapt to it. Well, I think what you could have, and this goes to the development process. So when you're developing a car, I mean, you have fixed test scenarios and you put it through that fixed test suite. and what you have the opportunity to do once you put a computer in every single car that can communicate,

you can now collect up all of the ice scenarios in the world. Wow. And so a lot of what I think about for building a system like this is what is your QA process? And that how do you discover everything? Like what are all the possible ice scenarios so that we can bring it back to our data center? So every time we change our control system, we can test them all simultaneously. Right. And that's how you build up that learning system.

It's incredible. And what about car-to-car communication? So in the same ice scenario, you left, you know, truckie at 11 a.m. And the road was, you know, light flurries. I left at 1 p.m. And it was, you know, icing up. And it was kind of icy, slushy. And then somebody else got pure ice at 3 p.m.

But we all had that data, you know, is car-to-car data or recent data, like in the ways we have traffic data. And that's been a game changer for routing. is there an opportunity for the real-time data to make it into the self-driving function? Or is that overkill? Like, something could be on the road, like an object on the road is the obvious one. The other cars should know there's an object on the road and avoid it. But there could also be things like, you know, paint or a construction area that changes, you know, or snow, you know, those kind of conditions.

I think that there's a whole industry around V to X, which is vehicle to vehicle or vehicle to infrastructure. Me personally, I'm not bullish on it because you have a huge security problem, which is, how do you know that someone broadcasting information is a good actor? Right. And when you look at how those standards evolve, they sort of said publicly infrastructure something something, which is not a good answer for how you get a good actor or a bad actor in there. And I think that even if you had that system, you would still want to develop an observant,

visual-based system that was as accurate as you can make it, if not 100% accurate. Yeah, it does seem rife with complications because the data gets old and it could be compromised. What about changing the road itself in some way? I've seen demonstrations on the YouTube and other places where people specifically paint a middle lane in the highway for self-driving cars to kind of lock into. And the Tesla kind of does that when you're in autopilot, it draws a middle lane that it's kind of dialed into when you navigate on autopilot. Are there things that could happen on roads that would help self-driving? Or is it good enough now that that's not worth pursuing? I believe that it's good enough now.

And again, we solve that kind of the same way that we would solve something like ICE is we actually have libraries of videos of all the roads that we drive on. And so we don't have to make a heuristic and guess whether it works. We actually have original sensor data from every single road that we drive on. And so we can tell ahead of time whether it's good enough. And something we don't do is we don't actually use map data. So a lot of companies use HD maps where they say, hey, if I can just figure out where I am in the world through localization, then maybe instead of reading the road, I'm going to use what's stored in my map. So that's not something we do, but what we do store on a map is maybe some clues about

how that road surface was constructed. And so, you know, some highways are like brand new and it's like very bright white, on black. Some of them like parts of 280 are old where it's old concrete, it's very faded. And giving the visual recognition system, those sorts of clues can help it perform, you know, well on each type of road. And so again, you can break down the problem in that you don't need to make a universal lane detector like a person would have.

We want to simplify the problem for the computer and say, look, let's make a few stereotypes of road. And it kind of maps to the decade it was built. And just say, depending on that stereotype, we know how long ago that road was built. Black asphalt with white, you know, bright white line dividers is going to be one problem set versus the faded concrete with faded white on the 280. Although that 280 is just beautiful driving with. those concrete highways. Are concrete highways better for self-drash? Is there a difference between the

highways? It's like New York asphalt, like just too random and like California highways are just perfect with that beautiful concrete? We have to work on everything. I'm not going to judge the roads. It's like it's our job to make sure that they all work. I'm curious as we wrap up here. Is there a wild card that nobody's expecting that would advance self-driving quicker because most people seem to be thinking that, you know, the steering wheel comes out in year 10. And, you know, to your point, like on highways, we might be able to get a little bit of our time back, you know, in, you know, whatever it is, 2022, 2022, 28, somewhere in that time frame. We'll be able to take our eyes off the road for a small amount of time or periods of time.

But is there any technology that could be game changing either in chip technology or sensor technology that's in the work that people are looking at going, well, if that goes right, boy, that could be a game changer. Are there any game changers you can think of? So the interesting sensor technology I'm looking at, and no one quite builds it. I've seen presentations, they all say 18 months away, which is like never. Yeah, that's the standard technology. And that is 5G, and not using it for a data transport.

One of the things that 5G has is it has a beam forming radar in it. So 5G at its high frequencies operates at the same frequencies as radar. Yeah. But instead of just being a single pulse that you send out in the scene, you can actually scan the scene by controlling the direction of the beam. Wow. And so what's interesting to me about that is first you could make a high-resolution

radar return, but based on solid-state devices that are in every single phone. So you get high quantity, high quality, you know, that'll drive the power down, drive the cost down. So you have those forces. So there's something there in, you're going to have one side of it, the 5G people, you know, driving the cost on the intent of design, driving the cost on the DSPs or however you process the signals. Yeah.

And that would be very interesting for us as a really, call it, unintelligent collision avoider. Right. In that it doesn't have to understand anything. All it has to do is say, I want to avoid frontal collisions. and you could build a very, very simple system that would prevent all forward collisions. I think that would be a very interesting advance.

Why doesn't that exist as a standard safety technology that every car is required to have, you know, under 35 miles an hour, slam on the brakes if you're going to hit something within five feet of you, period? Like, shouldn't that be much easier than self-driving to create as a, you know, airbag type, you know, stepping stone along the way and just get rid of every single fender bender and then retrofit every old car.

That could be a $300 product for you that insurance companies would pay for and say, you know what, no more fender benders. Technology in the auto industry involved very slowly. I think that's what it comes down to. It's like it already has to be proven. And some of that is because when you build something into a car as opposed to adding it on, cars have like a 20-year lifetime.

Right. And so that's often why technology in the auto industry tends to lag the consumer industry by five to seven years. because it takes a bunch of extra time to prove that some piece of electronics is going to last 20 years into the future. And so I think it's something that they'll probably, it'll probably will be introduced into cars. That'd be the ultimate aftermarket thing. Just no fender benders. You put this device like those in-grill radar detectors you can get aftermarket, like an in-grill device that just slams on the brakes.

if there's something right in front of it, you just solve that one acute problem. That's got to be half of all accidents or tiny 35 mile an hour or less fender benders, right? Yeah. All right, John, I know you're hiring, right? So how can people find out about the positions you're hiring for and what are you looking for? What's the culture like? Well, the culture is a very intensive engineering culture because we have to make a product that's safety critical. It's a very simple product, but in some ways, it's, you know, we cover every single area of computer science.

So we cover compiler building, data centers, and of course, machine learning infrastructure and math. And you can find out about the company. You could actually reach out to me directly on Twitter. Cool. And we could. Ghost jose. So just ghost haze.

DMs open. If you're looking for a job, you want to save human life. There's 30,000 people die on the roads every year. And we're going to get that number below 30. I think it's going to be like people intentionally crashing their cars is going to be the only way to crash. And even then, you could make cars impossible to crash. Like, if somebody tried to run off the road, I could just not do it.

Yeah. I think that's further out. That puts a lot of faith in technology. But I think we're going to get there. This driver is trying to run this off the road. It's like, no, we're not going to let you do that. Sorry, I'm not opening the bay doors. All right, listen, continued success, John.

Thanks for coming on the pod. Stay safe during this crazy time here in California. And everybody go ahead and check out Ghost Locomotion. and follow Ghost Hayes on the Twitter. We'll see you all next time. Bye-bye.