Lex Fridman Podcast - #97 – Sertac Karaman: Robots That Fly and Robots That Drive

Episode Date: May 20, 2020

Sertac Karaman is a professor at MIT, co-founder of the autonomous vehicle company Optimus Ride, and is one of top roboticists in the world, including robots that drive and robots that fly. Support this podcast by signing up with these sponsors: – Cash App – use code “LexPodcast” and download: – Cash App (App Store): https://apple.co/2sPrUHe – Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Sertac's Website: http://sertac.scripts.mit.edu/web/ Sertac's Twitter: https://twitter.com/sertackaraman Optimus Ride: https://www.optimusride.com/ This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon. Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. OUTLINE: 00:00 - Introduction 01:44 - Autonomous flying vs autonomous driving 06:37 - Flying cars 10:27 - Role of simulation in robotics 17:35 - Game theory and robotics 24:30 - Autonomous vehicle company strategies 29:46 - Optimus Ride 47:08 - Waymo, Tesla, Optimus Ride timelines 53:22 - Achieving the impossible 53:50 - Iterative learning 58:39 - Is Lidar is a crutch? 1:03:21 - Fast autonomous flight 1:18:06 - Most beautiful idea in robotics

The following is a conversation with Sirta Shkarmon, a professor at MIT, co-founder of the autonomous vehicle company Optimus Ride, and is one of the top roboticists in the world, including robots that drive and robots that fly. To me, personally, he has been a mentor, a colleague, and a friend. He's one of the smartest and most generous people I know, so it was a pleasure and honor to finally sit down with him for this recorded conversation. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe to my YouTube, review 5 stars and Apple podcasts, support on Patreon, or simply connect with me on Twitter, at Lex Friedman spelled F-R-I-D-M-A-N.

As usual, I'll do a few minutes of ads now and never any ads in the middle that can break the flow of the conversation. I hope that works for you and it doesn't hurt the listening experience. This show is presented by CashApp, the number one finance app in the App Store. When you get it, use the code LexPodcast. CashApp lets you send money to friends by Bitcoin and invest in the stock market with as little as $1. Since CashApp allows you to send and receive money digitally, let me mention a surprising

fact about physical money. It costs 2.4 cents to produce a single penny. In fact, I think it costs $85 million annually to produce them. That's a crazy little fact about physical money. So again, if you get cash out from the App Store, Google Play and use the code Lex Podcast, you get $10 and cash app will also donate $10 to the first, an organization that is helping to advance robotics and STEM education for young people around the world. And now here's my conversation with Sir Tash Karman.

Since you have worked extensively on both, what is the more difficult task? Autonomous flying or autonomous driving? That's a good question. I think that autonomous flying, just kind of doing it for consumer drones and so on, the kinds of applications that we're looking at right now, is probably easier. And so I think that that's maybe one of the reasons why it took off, like literally, a little earlier than the autonomous cars. But I think if we look ahead, I would think that the real benefits of autonomous flying,

unleashing them in transportation logistics and so on. I think it's a lot harder in autonomous driving. So I think my guess is that we've seen a few machines fly here and there, but we really haven't yet seen any kind of machine like a massive scale, large scale being deployed and flown and so on. And I think that's going to be after we kind of resolve some of the large scale deployments of autonomous driving. So it was the hard part.

What's your intuition behind why at scale when consumer facing drones are tough? So I think in general at scale when consumer facing drones are tough. So, I think in general, at scale is tough. Like, for example, when you think about it, we have actually deployed a lot of robots in the, let's say, the past 50 years. We use academics or we business entrepreneurs. I think we use humanity. Humanity? A lot of people working on it.

So, we humans deployed a lot of robots. And I think that when you think about it, robots, they're autonomous. They work. They work on their own. But they are either like an isolated environment or they are in sort of, you know, they may be at scale, but they're really confined to a certain environment that they don't interact so much with humans.

And so, you know, they work in, I don't know, factory floors, warehouses, they work on Mars, you know, they are fully autonomous over there. But I think that the real challenge of our time is to take these vehicles and put them into places where humans are present. So now I know that there's a lot of human robot interaction type of things that need to be done and so on. That's one thing, but even just from the fundamental algorithms and systems and the business cases or maybe the business models, even like architecture, planning, societal issues, there's a whole bunch of pack of things that are related to us putting robotic vehicles into human present environments. And these humans, you know, they will not potentially be even trained to interact with them. They may not even be using the services that

are provided by these vehicles. They may not even know that they're autonomous. They're just doing their thing, living in environments that are designed for humans, not for robots. And then I think is one of the biggest challenges I think of our time to put vehicles there. And, you know, to go back to your question, I think, doing that at scale, meaning, you know, you go out in a city and you have, you know, like thousands or tens of thousands of autonomous vehicles that are going around. It is so dense to the point where if you see one of them, you look around, you see another one. It is that dense.

And that density, we've never done anything like that before. And I would bet that that kind of density will first happen with autonomous cars because I think we can bend the environment a little bit, we can especially kind of making them safe is a lot easier when they're on the ground. When they're in the air, it's a little bit more complicated. But I don't see that there's going to be a big separation. I think that there will come a time that we're going to quickly see these things unfold. Do you think there will be a time where there's tens of thousands of delivery drones that fill

this guy? You know, I think it's possible to be honest. Delivery drones is one thing, but you know, you can imagine for transportation, like an important use case is, you know, we're in Boston, you want to go from Boston to New York. And you want to do it from the top of this building to the top of another building in Manhattan and you're gonna do it in one and a half hours. And that's a big opportunity, I think. Personal transport.

So like you and me, a friend, like almost a couple. Yeah, or almost like a like a like an Uber. So like four people, six people, eight people. In our work in a Thomas vehicles, I see that. So there's kind of like a bit of a need for, you know, one person transport, but also like a few people. So you and kind of like a bit of a need for you know one person transport, but also like like a few people So you and I could take that trip together. We could have lunch

You know, I think kind of sounds crazy. Maybe even sounds a bit cheesy But I think that those kinds of things are some of the real opportunities and I think you know It's not like the typical airplane and the airport would disappear very quickly But I would think that many people would feel like they would spend an extra $100 on doing that and cutting that four hour travel down to one and a half hours. So how feasible are flying cars?

It's been the dream. That's like when people imagine the future for 50 plus years, they think flying cars. It's like all technologies, it's cheesy to think about now because it seems so far away but overnight it can change but just technically speaking in your view how feasible is it to make that happen? I'll get to that question but just one thing is that I think you know sometimes we think about what's going to happen in the next 50 years. It's just really hard to guess right. The next 50 years I don't know I mean we could about what's going to happen in the next 50 years. It's just really hard to guess right?

Next 50 years, I don't know, I mean, we could get, what's going to happen in transportation in the next 50 years? We could get flying saucers. I could bet on that. I think there's a 50-50 chance that you know, like you can build machines that can ionize the air around them and push it down with magnets and they would fly like a flying saucer. That is possible. And it might happen in the next 50 years.

So it's a bit hard to guess, like when you think about 50 years before. But I would think that, you know, there's this kind of notion where there's a certain type of airspace that we call the edge-eye airspace. And there's good amount of opportunities in that airspace. So that would be the space that is kind of

a little bit higher than the place where you can throw a stone because that's a tough thing when you think about it. You know, it takes a kid on a stone to take an aircraft down and then what happens? But, you know, imagine the airspace that's high enough so that you cannot throw a stone, but it is low enough that you're not interacting with the very large aircraft that are flying several thousand feet above. And that airspace is underutilized. Or it's actually not utilised at all. So there's recreational people fly every now and then, but it's very few. If you look up in the sky, you may not see any of them at any given time. Every now and then you'll see one airplane kind of utilizing that space and you'll be surprised. And the moment you're outside of an airport a little bit like it just kind of flies off and then it goes out. And I think utilizing that airspace, the technical challenge is

there is, you know, building an autonomy and ensuring that that kind of autonomy is safe. Ultimately I think it is going to be building in complex software, complicated so that it's maybe a few orders of magnitude more complicated than what we have on aircraft today and at the same time ensuring just like we ensure on aircraft ensuring that it at the same time, ensuring just like we ensure on aircraft, ensuring that it's safe. And so that becomes like building that kind of complicated hardware and a software becomes a challenge. Especially when you build that hardware,

I mean, you build that software with data. And so, you know, it's, of course, there's some rule-based software in there that kind of do a certain set of things, but then there's a lot of training there to... I think machine learning will be key to these kinds of delivering safe vehicles in the future, especially flight. Not maybe the safe part, but I think the intelligent part. I mean, there are certain things that we do it with machine learning, and it's just

there's like right now in the hall of the way. And I don't know how else they could be done. And there's always this conundrum. I mean, we could maybe gather billions of programmers, humans, who program perception algorithms that detect things in the sky and whatever, or, you know, we, I don't know, we maybe even have robots like learning a simulation environment and transfer, and they might be learning a lot better in a simulation environment than a billion humans put their brains together and try to program. Humans pretty limited. So what's the role of simulations with drones?

You've done quite a bit of work there. How promising, just the very thing you said just now, how promising is the possibility of training and developing a safe flying robot in simulation and deploying it and having that work pretty well in the real world. I think that a lot of people, when they hear simulation, they will focus on training immediately, but I think one thing that you said, which was interesting, it's developing.

I think simulation environments are actually could be key and great for development. And that's not new. Like for example, there's people in the automotive industry have been using dynamic simulation for decades now. And it's pretty standard that you would build and you would simulate. If you want to build an embedded controller, you plug that embedded computer into another computer, that other computer would simulate, and so on. And I think fast forward these things,

you can create pretty crazy simulation environments. Like for instance, one of the things that has happened recently and that you know we can do now is that we can simulate cameras. A lot better than we used to simulate time. We were able to simulate time before. And that's I think we just hit the elbow of on that kind of improvement, I would imagine that with improvements in hardware, especially, and with improvements in machine learning, I think that we will get to a point where we can simulate cameras very, very much. Simulator cameras mean simulate how a real camera would see the real world. Therefore, you can explore the limitations of that. You can train perception algorithms on that

in simulation, all that kind of stuff. Exactly. So, you know, it's it has been easier to simulate what we would call interceptive sensors, like internal sensors. So, for example, inertial sensing has been easy to simulate. It has also been easy to simulate dynamics, like physics that are governed by ordinary differential equations. I mean, like how the car goes around, maybe how it rolls on the road, how it interacts with the road, or even an aircraft flying around, like the dynamic physics or that.

What has been really hard has been to simulate extraceptive sensors, sensors that kind of look out from the vehicle. And that's a new thing that's coming. Laser range finders that are a little bit easier. Cameras, radars are a little bit tougher. I think once we nail that down, the next challenge, I think, in simulation will be to simulate human behavior.

That's also extremely hard. Even when you imagine how a human driven car would act around, even that is hard, but imagine trying to simulate a model of a human, just doing a bunch of gestures and so on. It's actually simulator. It's not captured with motion capture, but it is simulated. That's very... In fact, today, I get involved a lot with this very high-end rendering projects. I have this test that I've passed it to my friends or my mom.

I send two photos, two pictures, and I say, which one is rendered, which one is real. It's pretty hard to distinguish, except I realize, except when we put humans in there. It's possible that our brains are trained in a way that we recognize humans extremely well. But we don't so much recognize the built environments, because built environments sort of came after Persevy evolved into sort of being humans, but humans were always there. Same thing happens for example, you look at monkeys and you can't distinguish one from another, but they sort of do. And it's very possible that they look at humans, it's kind of pretty hard to distinguish one from another, but we do. And so our eyes are pretty well trained to look at humans and understand, if something is off, we will get it. We may not be able to pinpoint it. So in my typical

friend test or mom test, what would happen is that we put like a human walking in a thing, and they say, you know, this is not right. Something is off in this video. I don't know what, but I can tell you it's the human. I can take the human and I can show you like inside of a building or like an apartment and it will look like if we had time to render it, it will look great. And this should be no surprise. A lot of movies that people are watching, it's all computer generated.

You know, even nowadays, even you watch a drama movie. And like, there's nothing going on action-wise, but it turns out it's kind of like cheaper, I guess, to render the background. And so they would. But how do we get there? How do we get a human that would pass the mom slash friend test, a simulation of a human walking.

Do you think that's something we can creep up to just do kind of a comparison learning where you have humans annotate what's more realistic and not just by watching? Like what's the path? Because it seems totally mysterious. How do you simulate human behavior here. It's hard because a lot of the other things

that I mentioned to you, including simulating cameras, right? It is the thing there is that, you know, we know the physics, we know how it works, like in the real world. And we can write some rules and we can do that. Like for example, simulating cameras, there's a thing called ray tracing. I mean, you're literally just kind of imagine it's very

similar to it's not exactly the same, but it's very similar to tracing photon by photon, they're going around balancing on things and coming to your eye, but human behavior, developing a dynamic like like a model of that that is mathematical so that you can put it into a processor that would go through that that's going to be hard. And so what else do you got? You can collect data right and you can try to match the data or another thing that you can do is that you know you can show the frontest you know you can say this or that and this or that and that will be labeling. Anything that requires human labeling ultimately we were limited by the number of humans that you know We have available at a hard disposal and the things that they can do you know they have to do a lot of other things than also labeling the state

So so that modeling human behavior part is is I think going we're gonna realize it's very tough. And I think that also affects our development of autonomous vehicles. I see that in self-driving as well. You want to use, so you're building self-driving. At the first time, right after urban challenge, I think everybody focused on localization, mapping and localization.

As slam algorithms came in, Google was just doing that. And so building these HD maps, basically basically that's about knowing where you are. And then five years later in 2012, 2013 came the kind of coding code AI revolution and that started telling us where everybody else is. But we're still missing what everybody else is going to do next. And so you want to know where you are, you want to know what everybody else is. Hopefully you know what what you're going to do next, and then you want to predict what other people are going to do.

And that last bit has been a real challenge. What do you think is the role your own of your ego vehicle, the robot, the you, the robotic you in controlling and having some control of how the future enrols of what's going to happen in the future. That seems to be a little bit ignored in trying to predict the future is how you yourself can affect that future by being either aggressive or less aggressive or signaling in some kind of way. So for this kind of game, tereratic dance seems to be ignored for the moment. It's, yeah, it's totally ignored.

I mean, it's quite interesting actually, like how we interact with things versus we interact with humans. Like so if you see a vehicle that's completely empty and it's trying to lose something, all of a sudden it becomes a thing. So, interacted with, like, you interact with this table. And so, you can throw your backpack or you can kick your, kick it,

put your feet on it and things like that. But when it's a human, there's all kinds of ways of interacting with a human. So, if, you know, like, you and I are face to face, we're very civil, you know, we talk, we understand each other for the most part. We'll see, we just, that's the end. You never know. What's gonna happen? But the thing is that like for example,

you and I might interact through YouTube comments and the conversation may go at a totally different angle. And so I think people kind of abusing as autonomous vehicles is a real issue in some sense. When you're an ego vehicle, you're trying to coordinate your way, make your way, it's actually kind of harder than being a human. It's like, you not only need to be as smart as humans are, but you also are a thing. They're going to abuse you a little bit.

You need to make sure that you can get around and do something. So I, in general, believe in that sort of game theoretic aspects, I've actually personally have done, you know, quite a few papers, both on that kind of game theory and also like this, this kind of understanding people's social value orientation, for example. Some people are aggressive, some people not so much, and a robot could understand that by just looking at how people drive. As they come an approach, you can actually understand, if someone is going to be aggressive or not as a robot, and you can make certain decisions. Well, in terms of predicting what they're going to do, the hard question is, you as a robot,

should you be aggressive or not, when faced with an aggressive robot? Right now, it seems like aggressive is a very dangerous thing to do because it's costly from a societal perspective, how you're perceived. People are not very accepting of aggressive robots in modern society. I think that's accurate. So that is really is. And so I'm not entirely sure how to go about, but I know for a fact that how these robots interact with other people in there is going to be, and then interaction is always going

to be there. You could be interacting with other vehicles or other just people kind of like walking around. And like I said, the moment there's like nobody in the seat, it's like an empty thing, just rolling off the street. It becomes like no different than like any other thing. That's not human. And so people, and maybe abuse is the wrong word, but you know, people may be rightfully even.

They feel like, you know like this is a human present and why it's designed for humans to be, and they kind of they want to own it. And then the robots, they would need to understand it and they would need to respond in a certain way. And I think that this actually opens up quite a few interesting societal questions for us as we deploy robots at large scale.

So what would happen when we try to deploy robots at large scale, I think is that we can design systems in a way that they're very efficient, or we can design them that they're very sustainable. But ultimately, the sustainability efficiency trade-offs, like they're gonna be right in there, and we're gonna have to make some choices, like we're not gonna be able to just kind of put it aside.

So for example, we can be very aggressive and we can reduce transportation delays, increase capacity of transportation, or we can be a lot nicer and allow other people to kind of quote-unquote own the environment and live in a nice place, and then efficiency will drop. So when you think about it, I think sustainability gets attached to energy consumption and I'm wirelessly impact immediately. And those are those are there. But like livability is another sustainability impact. So you create an environment that people want to live in. And if robots are going around being aggressive, you don't want to live in that environment, maybe. However, you should note that if you're not being

aggressive, then you know, you're probably taking up some delays in transportation and this and that. So you're always balancing that. And I think this choice has always been there in transportation, but I think the more autonomy comes in, the more explicit the choice becomes. Yeah, and when it becomes explicit, then we can start to optimize it.

And then we'll get to ask the very difficult societal questions of what do we value more? Efficiency or sustainability? It's kind of interesting. That will happen. I think we're going to have to like, I think that the interesting thing about like the whole autonomous vehicles question, I think, is also kind of, I think a lot of times, you know, we have focused on technology development, like hundreds of years, and the products somehow follow, and then we got to make these choices

and things like that. But this is a good time that we even think about autonomous taxi type of deployments and the systems that would evolve from there, and you realize the business models are different, the impact on architecture is different, urban planning, you get into like regulations. And then you get into like these issues that you didn't think about before, but like sustainability and ethics is like right in the middle of it. I mean, even testing autonomous vehicles, like think about it, you're testing autonomous vehicles in human present environments. I mean, the risk may be very small, but still, you know, it's a, it's a,

it's a, you know, strictly greater than zero risk that you're putting people into. And so then you have that innovation, you know, risk trade-off that you're in that somewhere. And we understand that pretty now, that pretty well now is that if we don't test, at least the development will be slower. I mean, it doesn't mean that we're not going to be able to develop. I think it's going to be pretty hard, actually, maybe we can, we don't really, I don't know. But the thing is that those kinds of trade-offs we already are making. And as these systems become more ubiquitous, I think those trade-offs will just really hit.

So you are one of the founders of Optimus Ride and Thomas Vigil company. We'll talk about it. Let me, on that point, ask maybe good examples, keeping Optimus Ride out of this question. Optimus ride out of this question. Sort of exemplars of different strategies on the spectrum of innovation and safety or caution. So the waymo Google self driving car, waymo represents maybe a more cautious approach. And then you have Tesla on the other side,

headed by Elon Musk that represents a more, however, which adjectives you want to use aggressive, innovative, I don't know. But what do you think about the difference in these two strategies in your view? What's more likely, what's needed and is more likely to succeed in the short term and the long term. Definitely some sort of a balance is kind of the right way to go, but I do think that the thing that is the most important is actually like an informed public. So I don't mind, you know, I personally, like if I were in some place, I wouldn't mind so much, like taking a certain amount of risk. Some other people might.

And so I think the key is for people to be informed. And so that they can, ideally, they can make a choice. In some cases, that kind of choice, making that unanimously is, of course, very hard. But I don't think it's actually that hard to inform people. So I think in one case, like for example, even the Tesla approach, I don't know, it's hard to judge how informed it is, but it is somewhat informed.

I mean, things kind of come out. I think people know what they're taking and things like that and so on. But I think the underlying, I do think that these two companies are a little bit kind of representing like, of course, one of them seems a bit safer the other one or whatever the objective for that is

and the other one seems more aggressive or whatever the objective for that is. But I think when you turn the tables, they're actually there are two other orthogonal dimensions that these two are focusing on. On the one hand, for Weimo, I can see that there, I think they a little bit see it as research as well. I'm not sure if they're really interested in an immediate product. They talk about it. Sometimes there's some pressure to talk about it, so they kind of go for it, but I think I think that they're thinking maybe in the back of their minds, maybe they don't put

it this way, but I think they realize that we're building like a new engine. It's kind of like call it the AI engine or whatever that is. And, you know, an autonomous vehicle is a very interesting embodiment of that engine that allows you to understand where the ego vehicle is, the ego thing is, where everything else is, what everything else is going to do, and how do you react, how do you actually interact with humans the right way, how do you build these systems, and I think they want to know that, they want to understand that, and so they keep going and doing that. And so on the other dimension, Tesla is doing something interesting. I mean, I think that they have a good product.

People use it. I think that it's not for me, but I can totally see people, people like it, and people, I think they have a good product outside of automation. But I was just referring to the automation itself. I mean, it kind of drives itself. You still have to pay attention to it, right? But you know

People seem to use it. So it works for something And so people I think people are willing to pay for it people are willing to buy it. I think it It's one of the other reasons why people buy a test of car Maybe one of those reasons is Elon Musk is the CEO and you know, he seems like a visionary person That's what people think. he seems like a visionary person. That's what people think. And he seems like a visionary person. And so it adds like 5K to the value of the car. And then maybe another 5K is the autopilot.

And it's useful. I mean, it's useful in the sense that people are using it. And so I can see Tesla, and sure, of course, they want to be visionary. They want to kind of put out a certain approach. And they may actually get there But I think that there's also a primary benefit of doing all these updates and rolling it out because you know people pay for it And it's it's it's you know, it's basic, you know

demand supply market and people like it. They're happy to pay another 5k time K for that novelty or whatever that is. And they use it. It's not like they get it and they try it a couple times. It's a novelty, but they use it a lot of the time. And so I think that's what Tesla is doing. It's actually pretty different. They are on pretty orthogonal dimensions of what kind of things that they're building. They are using the same AI engine. So it's very possible that they're both going to be They are using the same AI engine, so it's very possible that they're both going to be one day using a similar almost like an internal combustion engine. It's a very bad metaphor, but similar internal combustion engine,

and maybe one of them is building a car, the other one is building a truck or something. So ultimately the use case is very different. So you, like I said, are one of the founders of Optimus Riders. Take a step back. It's one of the success stories in the autonomous vehicle space. It's a greater autonomous vehicle company. Let's go from the very beginning. What does it take to start an autonomous vehicle company? How do you go from idea to deploying vehicles like you

are in a few, a bunch of places, including New York? I would say that I think that, you know, what happened to us is it was the following. I think we realized a lot of kind of talk in the autonomous vehicle industry back in like 2014, even when we wanted to kind of get started. And I don't know, like I kind of, I would hear things like fully autonomous vehicles two years from now,

three years from now, I kind of never bought it. I was a part of MIT's Urban Challenge Entry. It kind of like it has an interesting history. So I did in college and in high school, sort of a lot of mathematically oriented work. And I think I kind of, you know, at some point, it kind of hit me, I wanted to build something. And so I came to MIT's mechanical engineering program, and I now realize, I think my advisor hired me

because I could do like really good math. But I told him that, no, no, no, I want to work on that urban challenge car. I want to build the autonomous car. And I think that was kind of like a process where we really learned, I mean, what the challenges are and what kind of limitations are we up against, you know, like having the limitations of computers

or understanding human behavior, there's so many of these things. And I think it just kind of didn't. And so we said, hey, you know, like, why don't we take a more like a market-based approach? So we focus on a certain kind of market. And we build a system for that. What we're building is not so much of like an autonomous vehicle only, I would say. So we build full autonomy into the vehicles. But you know, the way we kind of see it is that we think

that the approach should actually involve humans operating them, not just sitting in the vehicle. And I think today, what we have is today, we have one person operate one vehicle, no matter what that vehicle. It could be a forklift, it could be a truck, it could be a car, whatever that is. And we want to go from that to 10 people operate 50 vehicles. How do we do that? You're referring to a world of maybe perhaps tell the operation. So can you just say what means for 10, might be confusing for people listening, what does it mean for 10 people to control 50

vehicles? That's a good point. So I think it's a very deliberately, didn't call it teleoperation, because people think that, is that people think, away from the vehicle, sits a person, sees like maybe puts on goggles or something, VR and drives the car. So that's not at all what we mean. But we mean the kind of intelligence whereby humans are in control except in certain places, the vehicles can execute on their own. Imagine a room where people can see what the other vehicles are doing and everything, and there would be some people who are more like air traffic controllers, call them like AV controllers. These AV controllers would actually see a of like a whole map and they would

understand where vehicles are really confident and where they kind of, you know, need a little bit more help. And they help shouldn't be for safety, help should be for efficiency, vehicles should be safe no matter what. If you had zero people, they could be very safe, but they'd be going five miles an hour. And so if you want them to go around 25 miles an hour, then you need people to come in and, and for example, you know, the vehicle come to an intersection and the vehicle can say, you know, I can wait. I can inch forward a little bit, show my intent or I can turn left. And right now it's clear, I can't turn, I know that, but before you give me the go, I won't. And so that's one example. This doesn't mean necessarily we're doing

that actually. I think I think if you go down all the all that much detail that every intersection you're kind of expecting a person to press a button, then I don't think you'll get the efficiency benefits you want. You need to be able to kind of go around and be able to do these things. But I think you You need to be able to kind of go around and be able to do these things, but I think you need people to be able to set high level behavior to vehicles. That's the other thing that autonomous vehicles. I think a lot of people kind of think about it as follows.

I mean, this happens with technology a lot. You think, all right, so I know about cars and I heard robots. So I think how this is gonna work out is that I'm gonna buy a car, press a button, and it's going to drive itself. And then is that going to happen? And people kind of tend to think about it that way. But when you think about what really happens is that something comes in in a way that you didn't even expect. If asked, you might have said, I don't think I need that,

or I don't think it should be that, and so on. And then that becomes the next big thing, coding code. And so I think that this kind of different ways of humans operating vehicles could be really powerful. I think that sooner than later, we might open our eyes up to a world in which you go around walking a mall, and there's a bunch of security robots that are exactly operated in this way. You go into a factory or a warehouse, there's a whole bunch of robots that are exactly operated in this way. You go into a factory or a warehouse, there's a whole bunch of robots that are

exactly in this way. You go to a, you go to the Brooklyn Navy yard, you see a whole bunch of autonomous vehicles, Optimus Ride, and they're operated maybe in this way. But I think people kind of don't see that. I sincerely think that it's, there's a possibility that we may almost see like a whole mushrooming of this technology in all kinds of places that we didn't expect before and then maybe the real surprise. And then one day when your car actually drives itself, it may not be all that much of a surprise at all because you see it all the time. You interact with them, you take the optimist ride, hopefully, that's your choice. And then you know, you hear a bunch of things, you go around, you interact with them, I

don't know, like you have a little delivery vehicle that goes around the sidewalks and delivers your things, and then you take it, it says, thank you. And then you get used to that. And one day, your car actually drives itself, and the regulation goes by, and you know, you can hit the button of sleep. And it wouldn't be a surprise at all. I think that may be the real reality. So there's going to be a bunch of applications that pop up around autonomous vehicles, some of which maybe many of which we don't expect at all. So if we look at Optimus Ride,

what do you think, you know, the viral application, the one that really works for people in mobility, what do you think Optimus Ride will connect with in the near future first? I think that the first place that I like to target honestly is like these places where transportation is required within an environment, like people typically call it geofence. So you can imagine like a roughly two mile by two mile, could be bigger, could be smaller, type of an environment, and there's a lot of these kinds of environments that are typically transportation deprived. The Brooklyn Navy art that we're in today, we're in a few different places, but that was the one that was last

publicized. That's a good example. So there's not a lot of transportation there. And you wouldn't expect, like, I don't know, I think maybe operating an Uber there ends up being sort of a little too expensive. Or when you compare it with operating Uber elsewhere, that becomes the elsewhere, becomes the priority, and these people's places become totally transportation deprived.

And then what happens is that people drive into these places and to go from point A to point B inside this place within that day, they use their cars. And so we end up building more parking. For them, to, for example, take their cars and go to a lunch place. And I think that one of the things that can be done is that you can put in efficient, safe,

sustainable transportation systems into these types of places first. And I think that you know you could deliver mobility in an affordable way, affordable, accessible, you know, sustainable way. But I think what also enables is that this kind of effort, money, area, land that we spend on parking You could reclaim some of that and that is on the order of like even for a small environment like two mile by two mile It doesn't have to be smack in the middle of New York. I mean

anywhere else you're talking tens of millions of dollars if you're smack in the middle of New York You're looking at billions of dollars of savings just by doing that. And that's the economic part of it. There's a societal part, right? I mean, just look around. I mean, the places that we live are like built for cars. It didn't look like this just like a hundred years ago. Like today, no one walks in the middle of the street. It's for cars. No one tells you that growing up, but you grow into that reality. And so sometimes they close the road, it happens here, you know, like the celebration they close the road. Still people don't walk in the middle of the road, like just walking them and people don't. But I think it has so much impact, the car

in the space that we have. And I think we talked about sustainability, livability, I mean, ultimately these kinds of places that parking spots at the very least could change into something more useful, or maybe just like park areas recreational. And so I think that's the first thing that that we're targeting. And I think that we're getting like a really good response, both from an economic societal point of view, especially places that are a little bit forward looking. And like for example, Brooklyn, Navy, Art, they have tenants, there's distinct hierarchal like new lab, it's kind of like an innovation center, there's a bunch of startups there, and so you get those kinds of people, and you know, they're really interested in sort of making that environment more livable. And these

kinds of solutions that Optimus Ride provides almost kind of comes in and becomes that. And many of these places that are transportation deprived, they have, they actually ran shuttles. And so, you can ask anybody, the shuttle experience is like terrible. People hate shuttles. And I can tell you why. It's because, you know, like the driver is very expensive in a shuttle business. So what makes sense is to attach 20, 30 seats to a driver. And a lot of people have this misconception. They think that shuttles should be big. Sometimes we get that at Optimus, right? We tell them we're going to give you like four-seater-six-seaters, and we get asked like, how about like 20-seaters? You know, you don't need 20 cters. You want to spin up those seeds so that they can travel faster

and the transportation delays would go down. That's what you want. If you make it big, not only you will get delays in transportation, but you won't have an edge-eyed vehicle. It will take a long time to speed up, slow down, and so on. It'll, you need to climb up to the thing. So it's kind of like really hard to interact with.

And scheduling too, perhaps, when you have more smaller vehicles, it becomes closer to Uber, where you can actually get a personal, I mean, just the logistics of getting the vehicle to you is, becomes easier when you have a giant shuttle, there's fewer of them, and it probably goes on a route, a specific route that is supposed to hit. And when you go on a specific route and all seats travel together versus, you have a whole bunch of them, you can imagine, the route you can still have,

but you can imagine you split up the seats and instead of them traveling, like I don't know, a mile apart, they could be like, you know, half a mile apart if you split them into two. That basically would mean that your delays, when you go out, you won't wait for them for a long time. And that's one of the main reasons, or you don't have to climb up. The other thing is that I think if you split them up in a nice way, and if you can actually

know where people are going to be somehow, you don't even need the app. A lot of people ask us the app, we say, why don't you just walk into the vehicle? How about you just walk into the vehicle, it recognizes who you are and it gives you a bunch of options, the places that you go and you just kind of go there. I mean, people kind of also internalize the apps. Everybody needs an app. It's like, you don't need an app, you just walk into the thing. But I think one of the things that we really try to do is to take that shuttle experience that no one likes and tilt it into something that everybody loves.

And so I think that's another important thing. I would like to say that carefully, just like TABL operation, like we don't do shuttles. We're really kind of thinking of this as a system or a network that we're designing. But ultimately, we go to places that will normally run the shuttle service that people wouldn't like as much and we want to tilt it into something that people love. So you mentioned this second earlier, but how many

Optimus ride vehicles do you think would be needed for any person in Boston or New York, if they step outside, there will be, this is like a mathematical question, there will be two optimist ride vehicles within line of sight. Is that the right number to, well, at least one? Like for example, that's the density. So, meaning that if you see one vehicle, you look around, you see another one too. Imagine like too. Imagine, Tesla will tell you they collect a lot of data. Do you see that with Tesla? You just walk around and you look around, you see Tesla? Probably not.

Very specific areas of California, maybe. Maybe. You're right. There's a couple zip codes. But I think that's kind of important because maybe the couple zip codes, the one thing that we kind of depend on, I'll get to your question in a second, but now, like, we're taking a lot of tensions today. Hell yes.

And so, so, so I think that this is actually important. People call this data density or data velocity. So it's very good to collect data in a way that, you know, you see the same place so many times. Like, you can drive 10,000 miles around the country, or you drive 10,000 miles in a way that you see the same place so many times. Like you can drive 10,000 miles around the country or you drive 10,000 miles in a confined environment. You'll see the same intersection hundreds of times. And when it comes to predicting what people are going to do in that specific intersection, you become really good at it. Versus if you draw in like 10,000 miles around the country, you've seen that only once.

And so trying to predict what people do becomes our. And I think that, you know, you said what is needed? It's tens of thousands of vehicles. You know, you really need to be like a specific fraction of vehicle. Like for example, in good times, in Singapore, you can go and you can just grab a cab. And they are like, you know, 10%, 20% of traffic,

those taxis. Ultimately, that's where you need to get to. So that you get to a certain place where you really, the benefits really kick off in orders of magnitude, type of a point. But once you get there, you actually get the benefits. And you can certainly carry people, I think that's one of the things. People really don't like to wait for themselves. But for example, they can wait a lot more for the goods

if they order something. Like they're sitting at home and you want to wait half an hour. That sounds great. People will say it's great. You're going to take a cab. You're waiting half an hour. Like that's crazy. You don't want to wait that much.

But I think you can, I think, really get to a point by the system. At peak times, really focuses on kind of transporting humans around. And then it's really, it's a good fraction of the traffic to the point by, you know, you go, you look around, there's something there, and you just kind of basically get in there.

And it's already waiting for you or something like that. And then you take it. If you do it at that scale, like today, for instance, Uber, if you talk to a driver, right, I mean, Uber takes a certain cut. It's a small cut. Or, you know, drivers would argue that it's a large cut, but, you know, it's, it's, it's, when you look at the grand scheme of things, most of that money that you pay Uber kind of goes to the driver. And if you talk to the driver, the driver will claim that most of it is their time.

It's not spent on gas, they think. It's not spent on the car per se as much. It's like their time. And if you didn't have a person driving, or if you're in a scenario where, like 0.1 person is driving the car, And if you didn't have a person driving, or if you're in a scenario where you're like 0.1 person is driving the car. A fraction of a person is kind of operating the car, because you know you want to operate several. If you're in that situation, you realize that the internal combustion engine type of cars are very inefficient. We build them to go on highways, they pass crash

tests, they're like really heavy, they really don't need to be like 25 times the weight of its passengers or or you know like area wise and so on and But if you get through those inefficiencies and if you really built like urban cars and things like that I think the economics really starts to check out like to the point where I Mean, I don't know you may be able to get into a car and it may be less than a dollar to go from A to B. As long as you don't change your destination, you just pay 99 cents and go that. If you share it, if you take another stop somewhere, it becomes a lot better. You know, these kinds of things, at least for models, at least for mathematics and theory,

they start to really check out. So I think it's really exciting what Optimus Riders doing in terms of it feels the most reachable, like it'll actually be here and have an impact. Yeah, that is the idea. And if we contrast that, again, we'll go back to our old friends, Waymo and Tesla. So Waymo seems to have sort of technically similar approaches as Optimus Ride, but are different. They're not as interested as having impact today. They have a longer term sort of investment, it's almost more of a research project still, meaning they're trying to solve as far as understanding maybe you can differentiate, but they seem to want to do more unrestricted

movement, meaning move from A to B or A to B is all over the place. Versus Optimus Ride is really nicely geofence and really sort of establish mobility in a particular environment before you expand it. And then Tesla is like the complete opposite, which is, you know, the entirety of the world actually is going to be automated. Highway driving, urban driving, every kind of driving, you know, you kind of creep up to it by incrementally improving the capabilities of the auto-police system. So when you contrast all of these, and on top of that, let me throw a question that nobody likes, but it's a timeline. When do you think

each of these approaches loosely speaking, nobody can predict the future, we'll see mass deployment. So, you all must predicts the craziest approach is, I've heard figures like at the end of this year. Right? So, that's probably wildly inaccurate, but how wildly inaccurate is it? I mean, first thing to lay out, like everybody else, it's really hard to guess. I mean, I don't know where Tesla can look at, or Elon Musk can look at and say, hey, you know, it's the end of this year. I mean, I don't know what you can look at, you know, even the data that, you know, you would, I mean, if you look at the data,

even kind of trying to extrapolate the end state without knowing what exactly is gonna go, especially for like a machine learning approach. I mean, it's just kind of very hard to predict. But I do think the following does happen. I think a lot of people, you know what they do is that there's something that I call the couple times time dilation in technology prediction happens. Let me try to describe a little bit. There's a lot of things that are so far ahead. People think they're close.

And there's a lot of things that are actually close. People think it's far ahead. People try to kind of look at a whole landscape of technology development. Admittedly, it's chaos. Anything can happen in any order at any time. And there's a whole bunch of things in there. People take it, clamp it, and put it into the next three years. And so, then what happens is that there's some things that maybe can happen by the end of the year or next year and so on. And they push that into like a few years ahead

because it's just hard to explain. And there are things that are like, we years ahead because it's just hard to explain. And there are things that are like, we're looking at 20 years more, maybe, hopefully in my lifetime type of things. And because, you know, we don't know. I mean, we don't know how hard it is even. Like, that's a problem. We don't know like if some of these problems are actually AI complete, like, we have no idea what's going on. And we take all of that, and then we clump it, and then we say, three years from now. And then some of us are more optimistic, so they're shooting it at the end of the year. And some of us are more realistic. They say like five years, but we all, I think, it's just hard

to know. And I think trying to predict products ahead two, three years, it's hard to know in the following sense. We typically say, okay, this is a technology company, but sometimes really you're trying to build something about a technology, there's a technology gap. And Tesla had that electric vehicles. When they first started, they Tesla had that electric vehicles, you know, like, when they first started, they would look at a chart, much like a Moose Law type of chart, and they would just kind of

extrapolate that out, and they'd say, we want to be here. What's the technology to get there? We don't know. It goes like this, so it's probably just going to keep going. We'd AI that goes into the cars. We don't even have that. Like, we don't, we can't, I mean, what can you quantify? Yeah. Like, what kind of chart are you looking at, you know? But so, but so I think when there's that technology gap, it's just kind of really hard to predict. So now, I realize I talked like five minutes and I avoid your question.

I didn't tell you anything about that. It was very skillfully done. That was very well done. And I don't think you've actually argued that it's not even any answer you provide now is not that useful. It's going to be very hard. There's one thing that I really believe in, and this is not my idea, and it's been discussed several times, but this kind of something like a startup or a kind of an innovative company, including definitely Maymo Tesla, maybe even some of the other big companies that are kind of trying things. This kind of like iterated learning is very important. The fact that we're over there

and we're trying things and so on, I think that's important. We try to understand. And I think that, the coding code Silicon Valley has done that with business models pretty well. And now I think we're trying to get to do it where there's the literal technology gap. I mean, before, like, you're trying to build, I'm not trying to, I think this companies are building great technology to, for example, enable Internet search to do it so quickly. And that kind of didn't, what wasn't there so much. But at least like it was a kind of a technology that you could predict to some degree and so on.

And now we're just kind of trying to build, you know, things that it's kind of hard to quantify. What kind of a metric are we looking at? So psychologically, as a sort of, as a leader of graduate students and at optimists ride a bunch of brilliant engineers just curiosity Psychologically, do you think it's good to think that? You know whatever technology gap we're talking about can be closed by the end of the year

Or do you you know because we don't know so the way Do you want to say that everything is going to improve exponentially to yourself and to others around you as a leader, or do you want to be more sort of maybe not cynical, but I don't want to use realistic because it's hard to predict, but yeah, maybe more cynical pessimistic about the ability to close that gap. Yeah, I think that going back, I think that iterated learning is key. You're out there, you're running experiments to learn. That doesn't mean you're like your optimism is right, you're doing something, but I like it in an environment.

But what Tesla is doing, I think is also kind of like this kind of notion. And, you know, people can go around and say, like, you know, this year, next year, the other year, and so on. But I think that the nice thing about it is that they're out there, they're pushing this technology in. I think what they should do more of, I think, that kind of informed people about what kind of technology that they're providing, the good and the bad and not just sort of, it works very well, but I think, I'm not saying they're not doing bad and informing. I think they're kind of trying, they put up certain things, or at the very least, YouTube videos comes out on how the summon function works every now and then, and people get informed. And so that kind of cycle continues, but I admire it.

I think they're kind of go out there, and they do great things. They do their own kind of experiment. I think we do our own. And I think we're closing some similar technology gaps, but some also some are orthogonal as well. I think like we talked about people being remote, like it's something, or in the kind of environments that we're in, or think about people being remote, like it's something or in the kind of environments

that we're in or think about a Tesla car, maybe you can enable it one day, like there's low traffic, like you're kind of the stop on Go-A-Motion, you just hit the button and you can release it. Or maybe there's another lane that you can pass into, you go in there. I think they can enable these kinds of, I believe it.

And so I think that that part, that is really important and that is really key. And beyond that, I think, you know, when is it exactly going to happen and so on? I mean, it's like I said, it's very hard to predict. And I would imagine that it would be good to do some sort of like a one or two year plan, when it's a little bit more predictable, that the technology gaps you close and the kind of sort of product that would ensue. So I know that from Optimus Ride or other companies that I've involved in, I mean, at some point you find yourself in a situation where you're trying to build a product and and people are investing in that in that

you know, building effort. And those investors that they do want to know as they compare the investments they want to make, they do want to know what happens in the next one or two years. And I think that's good to communicate that. But I think beyond that it becomes it becomes a vision vision that we wanna get to someday and saying five years, 10 years, I don't think it means anything. But iterating learning is key, to do and learn. I think that is key. You know, I gotta sort of throw back right at you,

criticism in terms of, you know, like Tesla or somebody communicating, you know, how someone works and so on. I get a chance to visit Optimus ride And you guys are doing some awesome stuff and yet the internet doesn't know about it So you should also communicate more showing off, you know showing awesome the awesome stuff the stuff that works and stuff that doesn't work I mean it's just The stuff I saw with a tracking of different objects and pedestrian. So I mean, incredible stuff going on there. Maybe it's just the nerd of me, but I think the world would love to see that kind of stuff.

Yeah, that's well taken. I think, I should say that it's not like we weren't able to. I think we made a decision at some point. That decision didn't involve me quite a bit on kind of doing this in code and code stealth mode for a bit. But I think that we'll open it up quite a lot more. And I think that we are also an optimist right kind of hitting a new era. We're big now, we're doing a lot of interesting things. I think some of the deployments that we announced were some of the first bits of information that we put out into the world will also put out our technology.

A lot of the things that we've been developing is really amazing. We're going to start putting that out. We're especially interested in being able to work with the best people. And I think it's good to not just kind of show them and they come to our office for an interview, but just put it out there in terms of like, get people excited about what we're doing. So on the autonomous vehicle space,

let me ask one last question. So Elon Musk famously said that lighter is a crutch. So I've talked a bunch of people about it. I've got to ask you, you use that crutch quite a bit in the DARPA days. So, you know, and his idea in general, sort of, you know, more provocative and fun, I think than a technical discussion. But the idea is that camera basedbased, primarily camera-based systems is going to be what defies the future of autonomous vehicles.

So what do you think of this idea? Well, there's a crutch versus primarily camera-based systems. First things first, I think, you know, I'm a big believer in just camera-based autonomous vehicle systems. I think that you can put in a lot of autonomy and you can do great things. It's very possible that at the time scales, like I said, we can't predict 20 years from now, you may be able to do things that we're doing today only with LiDAR and you may be able to do them just with cameras. And I think that you know you can just I think that I will put my name on it too.

Like there will be a time when you can only use cameras and you'll be fine. At that time though, it's very possible that you know you find the LiDAR system as another robustifier or it's so affordable that it's stupid not to, you know, just kind of put it there. And I think we may be looking at a future like that. Do you think we're overrelying on LiDAR right now because we understand the better it's more reliable in many ways in terms of safety. Easier to build with, that's the other thing. I think to be very frank with you,

I mean, you know, we've seen a lot of sort of autonomous vehicles companies come and go and the approach has been, you know, you slap a light R on a car and it's kind of easy to build with when you have a light R, you know, you just kind of code it up and you hit the button and you do a demo. So I think there's, admittedly, there's a lot of people

that you focus on the LiDAR because it's easier to build with. That doesn't mean that, you know, without the camera, just cameras, you can, you cannot do what they're doing, but it's just kind of a lot harder. And so you need to have certain kind of expertise to exploit that. What we believe in, and you know, you know, you've's just kind of a lot harder. And so you need to have certain kind of expertise to exploit that.

What we believe in, and you know, you maybe seeing some of it, is that we believe in computer vision. We certainly work on computer vision and optimist ride by a lot, like, and we've been doing that from day one. And we also believe in sensor fusion. So, you know, we do, we have a relatively minimal use of light hours, but we do use them. And I think, you know, we do, we have a relatively minimal use of light hours, but we do use them.

And I think, you know, in the future, I really believe that the following sequence of events may happen. First things first, number one, there may be a future in which, you know, there's like cars with light hours and everything and the cameras, but, you know, in this 50 year ahead future, they can just drive with cameras as well, especially in some isolated environments and cameras they go and they do the thing. In the same future, it's very possible that the light hours are so cheap and frankly make the software maybe a little less compute intensive at the very least or maybe less complicated

so that they can be certified or insured there of their safety and things like that that it's kind of stupid not to put the LiDAR. Like imagine this, you either pay money for the LiDAR or you pay money for the compute. And if you don't put the LiDAR, it's a more expensive system because you have to put in a lot of compute, like this is another possibility. I do think that a lot of the sort of initial deployments

of self-driving vehicles, I think they will involve lightars. And especially either low range or short range or low resolution lightars are actually not that hard to build in solid state. They're still scanning, but like MEMS type of scanning lightars and things like that, they're like, they're actually not that hard. I think they will maybe kind of playing with the spectrum and the phaserase that are

a little bit harder, but I think like putting a MEMS mirror in there that kind of scans the environment. It's not hard. The only thing is that you know, you just like with a lot of the things that we do nowadays in developing technology, you hit fundamental limits of the universe. The speed of light becomes a problem. In when you're trying to scan the environment, so you don't get either good resolution or you don't get range, but you know, it's still something that you can put in that affordably. So let me jump back to drones. You've, you have a role in the Lockheed Martin Alpha Pilot Innovation Challenge, where teams

compete in drone racing. It's super cool, super intense, interesting application of AI. So can you tell me about the very basics of the challenge and where you fit in, what your thoughts are in this problem. And it's sort of echoes of the early DARPA challenge through the desert that we're seeing now with drone racing. Yeah, I mean, one interesting thing about it is that, you know, people, the drone racing exists as an esport. And so it's much like you're playing a game, but there's a real drone going in an environment. Human being is controlling it with goggles on. So there's no, it is a robot, but there's a real drawing going in an environment. Human being is controlling it with goggles on, so there's no, it is a robot, but there's

no AI. There's no AI, yeah. Human being is controlling it, and so that's already there. And I've been interested in this problem for quite a while actually, from a robotist's point of view, and that's what's happening in alpha pilot. Which problem, of aggressive flight? Of aggressive flight. Fully autonomous aggressive flight.

The problem that I'm interested in, you asked about alpha pilot, and I'll get there in a second, but the problem that I'm interested in, I'd love to build autonomous vehicles like drones that can go far faster than any human possibly can. I think we should recognize that we as, have limitations in how fast we can process information. And those are some biological limitations. Like we think about this AI this way too. I mean, this has been discussed a lot, and this is not sort of my idea per se,

but a lot of people kind of think about human level AI. And they think that AI is not human level, one day it'll be human level, and humans and AI's, they kind of interact. Versus, I think that the situation really is that humans are at a certain place, and AI keeps improving, and at some point just crosses off, and it gets smarter and smarter and smarter. And so drone racing, the same issue. Humans play this game, you have to like react in milliseconds. And there's really, you see something with your eyes. And then that information just flows through your brain into your hands so that you can command it.

And there's some also delays on getting information back and forth. But suppose those delays that don't exist, you just delay between your eye and your fingers, it is a delay that a robot doesn't have to have. So we end up building in my research group like systems that see things at a kilohertz, like a human eye would barely hit 100 hertz. So imagine things that see stuff in slow motion, like 10x slow motion. It will be very useful. Like we talked a lot about autonomous cars, so you know, we don't get to see it, but 100 bives are lost every day, just in the United States, on traffic accidents. And many of them are like known cases, you know, like the, you're coming through like a ramp going into a highway, you hit somebody and you're off,

or you know, like you kind of get confused. You try to like, swerve into the next lane, you go off the road and you crash, whatever. And I think if you hit enough compute in a car and a very fast camera right at the time of an accident, you could use all compute you had, like you could shut down the infotainment system and use that kind of computing resources. Instead of rendering, you use it for the kind of artificial intelligence that goes in the autonomy. You can either take control of the car and bring it to a full stop, but even if you can't do that,

you can deliver what the human is trying to do. Human is trying to change the lane, but goes off the road, not being able to do that with motor skills and the eyes and you know, you can get in there. And I was, there's so many other things that you can enable with what I would call high throughput computing, you know, data is coming in extremely fast. And in real time, you have to process it. And the current CPUs, however fast you clock it, are typically not enough. You need to build

those computers from the ground up so that they can ingest all that data. That I'm really interested in. Just on that point, just really quick, is the currently what's the bottom, like you mentioned, the delays in humans? Is it the hardware? Does he work a lot within video hardware? Is it the hardware or is it the software? I think it's both. I think it's both. In fact, they need to be co-developed, I think, in the future. I mean, that's a little bit what Nvidia does. Sort of like they almost like build the hardware and then they build the neural networks and then they build the hardware back and the neural networks back. And it goes back and forth, but it's that co-design. And I think that you know, like we try to way back, we try to build a fast drone that

could use a camera image to like track what's moving in order to find where it is in the world. This typical sort of, you know, visual inertial state estimation problems that we would solve. And, you know, we just kind of realize that we're at the limit sometimes of, you know, doing simple tasks. We're at the limit of the camera frame rate. Because, you know, if you really want to track things, you want the camera image to be 90% kind of like or some somewhat the same from one frame to the next.

And why are we at the limit of the camera frame rate? It's because camera captures data. It puts into some serial connection. It could be USB or like there's something called camera serial interface that we use a lot. It puts into some serial connection and copper wires can only transmit so much data. And you hit the channel limit on copper wires. And you know, you hit yet another kind of universal limit that you can transfer the data. So you have to be much more intelligence on how you capture those pixels. You can take compute and put it right next to the pixels. People are building those. How hard is it to get past the bottom

neck of the copper wire? Yeah, you need to do a lot of parallel processing, as you can imagine. The same thing happens in the GPUs. You know, like the data is transferred in parallel, somehow it gets into some parallel processing. I think that, you know, like, now we're really kind of diverted off into so many different dimensions, but great. So as aggressive flight, how do we make drones see many more frames as a second, you know, to enable aggressive flight?

That's a super interesting problem. That's an interesting problem. So, but like, think about it. You have CPUs, you clock them at several gigahertz. We don't clock them faster, largely because we run into some heating issues and things like that. But another thing is that three gigahertz clock, light travels kind of like on the order of a few inches or an inch,

that's the size of a chip. And so you pass a clock cycle and as the clock signal is going around in the chip, you pass another one. And so trying to coordinate that, the design of the complexity of the chip becomes so hard. I mean, we have hit the fundamental limits of the universe in so many things that we're designing. I don't know if we realize that. It's great, but like we can't make transistor smaller because like quantum effects that electron stuck to tunnel around. We can't clock it faster. One of the reasons why is because like information doesn't travel faster in the universe. Yeah. travel faster in the universe. And we're limited by that. Same thing with the laser scanner. But so then it becomes clear that, you know, the way you organize the chip into a CPU or even a GPU, you now need to look at how to redesign that if you're going to stick with silicon.

You could go do other things too. I mean, there's that too, but you really almost need to take those transistors, put them in a different way so that the information travels on those transistors in a different way, in a much more way that is specific to the high speed cameras coming in. And so that's one of the things that we talk about quite a bit. So drone racing kind of really makes that. And body's not the embodies that. And that's what it's exciting. It's exciting for people, students like it, it embodies all those problems. But going back, we're building Coding Code and other engine.

And that engine, I hope, one day will be just like how impactful seatbelts were in driving. I hope so. Or it could enable next generation autonomous air taxis and things like that. I mean it sounds crazy But one day we may need to purchase land these things if you really want to go From Boston to New York in more than a half hours. You may want to fix being aircraft Most of these companies that are kind of doing going flying cars. They're focusing on that But then how do you land it on top of a building? You may need to pull off like kind of fast maneuvers for a robot, like perched landage,

just going to go into a building. If you want to do that, you need these kinds of systems. And so, drone racing, you know, it's being able to go very faster than any human can comprehend. Take an aircraft, forget the quadcopter, you take your fixed spring. While you're at it, you might as well put some like rocket engines in the back and you just light it. You go through the gate and a human looks at it and just said, what just happened? And they would say, it's impossible for me to do that. And that's closing the same technology gap that would, you know, one day steer cars out of accidents. So, but then let's get back to the practical,

which is sort of just getting the thing to work in a race environment, which is kind of what the, it's another kind of exciting thing, which the DARPA Challenge, the Desert, did. You know, theoretically, we had autonomous vehicles but making them successfully finish a race, first of all, which nobody finished the first year. And then the second year, just to get, you know, to finish and go at a reasonable time, it's really difficult

engineering, practically speaking challenge. So that, let me ask about the, the, the alphabet challenge. There's a, I guess guess a big prize potentially associated with it. Blinnie asked, reminiscing of the DARPA days predictions. You think anybody will finish. Well, not not soon. I think that depends on how you set up the race course. And so if the race course is a slow on course, I think people will kind of do it. But can you set up some

course, like literally some core, you get to design it as the algorithm developer, can you set up some course so that you can beat the best human? When is that going to happen? Like that's not very easy, even just setting up some course. If you let the human that you're competing with set up the course, it becomes a lot harder. So how many in the space of all possible courses are would humans win and what machines win? Great question. Let's get to that. I want to answer your other question, which is like the DARPA challenge days, right? What was really hard? I think I think we understand, we understood what we wanted to build But still building things that experimentation that iterated learning that takes up a lot of time actually

And and so in my group for example in order for us to be able to develop fast We build like VR environments. We'll take an aircraft We'll put it in a motion capture room big huge motion capture room and we'll put it in a motion capture room, big, huge motion capture room, and we'll fly it. In real time, we'll render other images and beam it back to the drone. That sounds kind of notionally simple, but it's actually hard because now you're trying to fit all that data through the air into the drone. So you need to do a few crazy things to make that happen, But once you do that, then at least you can try things.

If you crash into something, you didn't actually crash. So it's like the whole drone is in VR. We can do augmented reality and so on. And so I think at some point testing becomes very important. One of the nice things about alpha pilot is that they build the drone and they build a lot of drones. And it's okay to crash. In fact, I think maybe the viewers may kind of like to see things that's around.

That potentially could be the most exciting part. It could be the exciting part. And I think as an engineer, it's a very different situation to be in. Like in academia, a lot of my colleagues who are actually in this race and they're really great researchers, but I've seen them trying to do similar things whereby they built this well-drawn and you know, somebody with like a face mask and a glows are going, you know, right behind the drone, trying to hold it if it falls down. Imagine you don't have to do that.

I think that's one of the nice things about alpha pilot challenge where, you know, we have this drones and we're going to design the courses in a way that we'll keep pushing people up until the crashes start to happen. And we'll hopefully sort of, I don't think you want to tell people crashing is okay. Like we want to be careful here, but because you know, we don't people to crash a lot. But certainly, we want them to push it so that you know, everybody crashes once or twice and And you know, they're really pushing it to their limits That's what iterated learning comes in is every every crash is a lesson is a lesson exactly

So in terms of the space of possible courses, how do you think about it? in in the in the war of Human versus machines Where do machines win? We look at that quite a bit. I mean, I think that you will see quickly that, like you can design a course. And in certain courses like in the middle somewhere,

if you kind of run through the course once, the machine gets beaten pretty much consistently by slightly. But if you go through the course like 10 times, humans get beaten very slightly but consistently. So humans at some point, you know, you get confused, you get tired and things like that versus this machine is just executing the same line of code tirelessly, just going back to the beginning and doing the same thing exactly. I think that kind of thing happens. And as I realize sort of as humans,

there's the classical things that everybody has realized. If you put in some sort of strategic thinking that's a little bit harder for machines that I think sort of comprehend, precision is easier to do. So that's what they excel in. And also sort of repeatability is easier to do. That's what they excel in. You can build machines that excel in strategy as well and beat humans that way too, but that's a lot harder to build. I have a million more questions, but in the interest of time, last question.

What is the most beautiful idea you've come across in robotics? Whether it's simple equation, experiment, demo, simulation, piece of software, what just gives you pause? That's an interesting question. I have done a lot of work myself in decision making, so I've been interested in that area. So, you know, robotics, you have somehow the field has split into, like, you know, there's people who would work on like perception, how robots perceive the environment, then how do you actually make like decisions, and there's people also, like, how to interact with robots, there's a whole bunch of different

fields. And, you know, I have admittedly worked a lot on the more control and decision making than the others. And I think that the one equation that has always kind of baffled me is Belman's equation. And so it's this person who have realized way back more than half a century ago on like, how do you actually sit down? And if you have several variables that you're kind of jointly trying to determine, how do you determine that? And there is one beautiful equation that, you know, like today, people do reinforcement, and we still use it. And it's, it's baffling to me because it both kind of tells you the simplicity, because

it's a single equation that anyone can write down. You can teach it in the first course on decision making. At the same time, it tells you how computation we have hard problems. I feel like a lot of the things that I've done at MIT for research has been kind of just this fight against computational efficiency things. How can we get it faster to the point where we now got to let's just redesign this chip. Maybe that's the way.

But I think it talks about how computationally hard certain problems can be by nowadays what people call curse of dimensionality. And so as the number of variables grow, by nowadays what people call curse of dimensionality. And so as the number of variables kind of grow, the number of decisions you can make grows rapidly. Like if you have, you know, a hundred variables, each one of them take 10 values,

all possible assignments is more than the number of atoms in the universe. It's just crazy. And that kind of thinking is just embodied in that one equation that I really like. And the beautiful balance between it being theoretically optimal and somehow practically speaking, given the cursor dimensionality, nevertheless, in practice works, despite all those challenges, which

is quite incredible. which is quite incredible. Which is quite incredible. So, you know, I would say that it's kind of like quite baffling, actually, you know, in a lot of fields that we think about how little we know, you know, like, and so I think here too, you know, we know that in the worst case, things are pretty hard, but you know, in practice, generally, things work. So it's just kind of, it's kind of baffling, I'm decision making, how little we know, just like how little we know about the beginning of time, how little we know about our own future. If you actually go into from boundless equation all the way down, I mean, there's also how little we know about like mathematics. I mean,

we don't even know if the axioms are like consistent. It's just crazy. Yeah. I think a good, good lesson, the lesson there just like as you said, we tend to focus on the worst case or the boundaries of everything we're studying. And then the average case seems to somehow work out. If you think about life in general, we mess it up a bunch, you know, we freak out about a bunch of the traumatic stuff, but

in the end, it seems to work out okay. Yeah, that seems like a good metaphor. Surfdash, thank you so much for being a friend to colleague and mentor. I really appreciate it. It's an honor to talk to you. Like mine. Thank you, Lex. Thanks for listening to this conversation with Surfdash Carmon and thank you to our

presenting sponsored cash app. Please consider supporting the podcast by downloading cash app and using code Lex Podcast. If you enjoyed this podcast subscribe my YouTube review it with 5 stars and Apple podcasts, support it on Patreon or simply get echoed me on Twitter at Lex Friedman. And now let me leave you with some words from how 9000 from the movie 2001 a space odyssey. I'm putting myself to the fullest possibly use, which is all I think that any conscious entity can ever hope to do.

Thank you.