The following is a conversation with Sertesh Karman, a professor at MIT, cofounder 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, 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 on YouTube,

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for young people around the world. And now here's my conversation with SirTash Karaman.

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 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 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 than 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 at massive scale, large scale being deployed and flown and so on. And I think that's

going to be after we resolve some of the large scale deployments of autonomous driving.

So what's the hard part? What's your intuition behind why 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 as academics or we business? I think we as 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 in isolated environments 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 like 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, legal 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 that, 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, you know, we can bend the environment a little bit. We can especially kind of

making them safe is a lot easier when they're like 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, you know, 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

the sky? You know, I think it's possible, to be honest. Delivery drones is one thing, but you

know, you can imagine for transportation, like in important use cases, 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 going to do it in one and a half hours.

And that's a big opportunity, I think. Personal transport. So like you and maybe a friend,

like almost like an Uber. Yeah, or almost like a like an Uber. So like four people, six people,

eight people. In our work in autonomous 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 I could take

that trip together. We could have lunch. 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, you know, many people would feel like they would spend an extra hundred

dollars 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 is 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? Next 50 years, I don't know. I mean, we could get what's going

to happen in transportation in the next 50. 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 agile 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 and 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, you know, flying several thousand feet above. And that airspace is underutilized.

Or it's actually kind of not utilized at all. Yeah, that's right. So there's, you know, there's

like recreational people kind of fly every now and then. But it's very few. Like 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 or 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's safe. And so that becomes like building that kind of complicated hardware and software

becomes a challenge, especially when, you know, you build that hardware, I mean, you build that

software with data. And so, you know, it's, of course, there's some rural based software in

there that kind of do a certain set of things. But, but then, you know, there's a lot of training

there. Do you 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

no other way. And I don't know how else they could be done. And, you know, there's always this

conundrum. I mean, we could like, could we like, 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 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, you know, 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, you know, there's people in the automotive

industry have been using dynamic simulation for like decades now. And it's pretty standard

that, you know, you would build and you would simulate. If you want to build an embedded

controller, you plug that kind of embedded computer into another computer, that other

computer would simulate tiny and so on. And I think, you know, 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 them. We were able to simulate them before. And that's, I think we

just hit the elbow on that kind of improvement. I would imagine that with improvements in hardware

especially, and with improvements in machine learning, I think that we would get to a point

where we can simulate cameras very, very well. Simulate cameras means 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's, it has been easier to simulate what we would call introspective sensors,

like internal sensors. So for example, inertial sensing has been easy to simulate. It has also

been easy to simulate dynamics, like, like physics that are governed by ordinary differential

equations. I mean, like how a car goes around, maybe how it rolls on the road, how it interacts

with, interacts with the road, or even an aircraft flying around, like the dynamic, the physics of

that. What has been really hard has been to simulate extroceptive sensors, sensors that kind

of like look out from the vehicle. And that's a new thing that's coming, like 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 like, how a human driven car would act around, even that is

hard. But imagine trying to simulate, you know, a model of a human, just doing a bunch of gestures

and so on. And, and, you know, it's, it's actually simulated. It's not captured like with motion

capture, but it is simulated. That's, that's very hard. In fact, today, I get involved a lot with

like sort of this kind of very high end rendering projects. And I have like this test that I've

passed it to my friends or my mom, you know, I send like two photos, two kind of pictures, and I say

rendered, which one is rendered, which one is real. And it's pretty hard to distinguish,

except I realized, 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, per se, we evolved into sort of being

humans. But, but humans were always there. Same thing happens, for example, you look at like

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'd put like a human walking in a in a in anything. And they, 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's would pass the mom slash friend test, a simulation of a human

walking? So do you think that's something we can creep up to by just doing 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 we simulate human behavior.

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 this thing called ray tracing. I mean, you 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 bouncing on things and come in your eye. But human behavior,

developing a dynamic like 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, 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 front tests, you know, you can say this or that and this

or that and that will be labeling. Anything that requires human labeling ultimately, we're limited

by the number of humans that, you know, we have available at our disposal, and the things that

they can do, you know, they have to do a lot of other things than also labeling this data. So, so

that modeling human behavior part is, is I think going we're going to realize it's very tough.

And I think that also affects, you know, our development of autonomous vehicles. I see them

self driving as well, like you want to use. So you're building self driving, you know,

at the first time, like right after urban challenge, I think everybody focused on localization,

mapping and localization, you know, slam algorithms came in, Google was just doing that.

And so building these HD maps, 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 that what

you're going to do next. And then you want to predict what other people are going to do in that

last bit has been a real, real challenge. What do you think is the role your own of your, of your

the ego vehicle, the robot, you, the you, the robotic you in controlling and having some control

of how the future on roles 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 this kind of game

theoretic 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 how we interact with things versus we interact

with humans. Like, so if if you see a vehicle that's completely empty, and it's trying to do

something, all of a sudden, it becomes a thing. So interact it 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 and

understand each other for the most part, we'll see you just that's the thing is that, like, for

example, you and I might interact through YouTube comments. And, you know, the conversation may

go a totally different angle. And so I think the people kind of abusing as autonomous vehicles is

a real issue in some sense. And so when you're an ego vehicle, you're trying to, you know,

coordinate your way, make your way, it's actually kind of harder than being a human.

You know, it's like, it's you, you not only need to be as smart as kind of humans are, but you

also you're a thing. So they're going to abuse you a little bit. So 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, you know, some people are aggressive, some people not so much. And,

and you know, like a robot could understand that by just looking at how people drive.

And as they kind of come an approach, you can actually understand, like if someone is going

to be aggressive or, 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 it really is. And so I'm not entirely sure like how to,

how to go about, but I know, I know for a fact that how these robots interact with other people

in there is going to be, and that interaction is always going to be there. I mean, you could be

interacting with other vehicles or other just people kind of like walking around.

And like I said, the movement, 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, so people, and maybe abuse is the wrong word, but you know, people maybe rightfully

even they feel like, you know, this is a human present environments designed for humans to be,

and they kind of they want to own it. And then, you know, the robots, they would,

they would need to understand it and they would need to respond in a certain way.

And I think that, you know, this actually opens up like quite a few interesting societal

questions for us as we deploy, like we talk 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 tradeoffs, like they're going to be right in there. And we're

going to have to make some choices, like we're not going to 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, you know, we can we can be a lot nicer and a lot of other

people to kind of coding code on 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

or environmental 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 some delays in transportation and this and that. So you're always balancing

that. And I think this 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 gonna have to like, I think that the 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 we have focused on technology development, like, hundreds of years, and,

you know, the products somehow followed. And then, you know, we got to make these choices

and things like that. But this is, this is a good time that, you know, we even think about,

you know, 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,

it's, it's, it's, 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 tradeoff that you're in that somewhere.

And we understand that pretty now that it pretty well now is that if we don't test,

the, at least the, 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,

we don't, I don't know, but, but the thing is that those kinds of tradeoffs we already are making.

And as these systems become more ubiquitous, I think those tradeoffs will just really hit.

So you are one of the founders of Optimus ride and autonomous vehicle company. We'll talk about

it. But let me, on that point, ask maybe a good examples, keeping Optimus ride out of this question.

Sort of exemplars of different strategies on the spectrum of innovation and safety or caution.

So like 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 adjective you want to use aggressive, innovative, I don't know. But

what, what do you think about the difference you need to do strategies in your view?

What's more likely, what's needed and is more likely to succeed in the short term and in the

long term? Definitely some sort of a balance is, is kind of the right way to go. But I, I do think

that the thing that is the most important is actually like an informed public. So I don't,

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,

you know, 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 the, of course, they, you know, one of them seems a bit safer, the other one,

or, you know, whatever the objective for that is, and the other one seems more aggressive,

or whatever the objective for that is. But, but I think, you know, when you turn the tables,

there are actually there are two other orthogonal dimensions that these two are focusing on.

On the one hand, for Vamo, I can see that, you know, they're, I mean, they, I think they a little

bit see it as research as well. So they kind of, they don't, I'm not sure if they're like really

interested in like an immediate product. You know, 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, and, you know, an autonomous vehicles 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, you know,

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, you know, like, 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, the automation itself. I mean, you know, like, it kind of drives itself,

you still have to be kind of, 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's, it's one of the other reasons why people buy a

Tesla 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. And he seems like a visionary person. And so that adds

like 5k to the value of the car. And then maybe another 5k is the autopilot. And, and, you know,

it's useful. I mean, it's useful in the sense that like, people are using it. And so I can see

Tesla 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, you know,

it's basic, you know, demand supply market. And people like it, they're happy to pay another 5k,

10k for that novelty or whatever that is. They, and they use it. It's not like they get it, and

they try it a couple of 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. Like 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, you know, they're both going to be sort of one day kind of using a similar

almost like an internal internal combustion engine. It's a very bad metaphor, but similar

internal combustion engine, and maybe one of them is building like 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, right? Let's take a step back. It's one of the success stories in the

autonomous vehicle space. It's a great 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 bunch of places, including New York? I would say that I think

that, you know, what happened to us 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. You know, 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.

You know, I want to build the autonomous car. And I think that was that was kind of like a

process where we really learn, 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, 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 just not 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 teleoperation.

So can you can you just say what it 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 what people think then is that people think

away from the vehicle sits a person sees like maybe put some 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. And so

imagine like, like a room where people can see what the other vehicles are doing and everything.

And, you know, there will be some people who are more like, more like air traffic controllers,

call them like AV controllers. And so these AV controllers would actually see kind 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 the 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 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 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 if you go down 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 need

people to be able to set high level behavior to vehicles. That's the other thing with autonomous

vehicles. You know, I think a lot of people kind of think about it as follows. I mean,

this happens with technology a lot. You know, you think, all right, so I know about cars,

and I heard robots. So I think how this is going to work out is that I'm going to buy a car,

press a button, and it's going to drive itself. And when is that going to happen?

You know, 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 then later, we might open our eyes

up to a world in which you go around walking them all. 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 printed 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 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 Optimus 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 asleep.

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 like 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 places that I like to target honestly is like these places where

transportation is required within an environment, like people typically call it geofenced. So you

can imagine like 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 Yard that you know, 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

Elsevier, that becomes the Elsevier becomes the priority and these places become totally

transportation deprived. And then what happens is that, you know, 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 the

launch place. And I think that one of the things that can be done is that, you know, 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. And 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 100 years ago. Like today, no one walks in the middle of the street. It's for cars. We,

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 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 this thing called like new lab, it's kind of like an

innovation center, there's a bunch of startups there. And so, you know, 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, you know, they have,

they actually rent shuttles. And so, you know, 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 our Optimus Ride, we tell them, we're going to give you like

four seaters, six seaters. And we get asked like, how about like 20 seaters? Like, you know,

you don't need 20 seaters. You want to split up those seats 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 agile 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 to 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 know, 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, you know, 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 of 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, I think one of the things that, you know, 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

the operation, like, we don't do shuttles. You know, 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 would normally

rent a shuttle service that people wouldn't like as much. And we want to tilt it into something

that people love. So you mentioned this 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'll be two Optimus Ride vehicles within line of

sight. Is that the right number to, well, at least one. For example, that's the density. So

meaning that if you see one vehicle, you look around, you see another one too. Imagine like,

you know, Tesla would tell you they collect a lot of data. Do you see that with Tesla? Like,

you just walk around and you look around and you see Tesla? Probably not.

Very specific areas of California, maybe. Maybe. You're right. Like, there's a couple zip codes

that, you know, just, but I think, but I think that's kind of important because, you know, like,

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 tangents today. And 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 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're drawing, like,

10,000 miles around the country, you've seen that only once. And so, trying to predict what

people do becomes hard. 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 why you need to get to so that, you know,

you get to a certain place where you really, the benefits really kick off in like 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, you're

sitting at home and you want to wait half an hour, that sounds great. People will say it's great.

You want to, 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 know, you can, I think, really get to a point where 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 where, 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 your 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.

You know, they, 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, have a person driving, or if you're in a scenario

where, you know, like, 0.1 person is driving the car, a fraction of a person is kind of

operating the car, because, you know, your one operates several. If you're in that situation,

you realize that the internal combustion engine type of cars are very inefficient, you know,

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. But if you get through those inefficiencies, and if you really build

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 is 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 a 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 I understand it, maybe you

can differentiate, but they seem to want to do more unrestricted movement, meaning move from A

to B, where A to B is all over the place, versus Optimus Ride is really nicely geofenced and really

sort of established mobility in a particular environment before you expand it. And then Tesla

is like the complete opposite, which is the entirety of the world actually is going to be

automated. Highway driving, urban driving, every kind of driving, you kind of creep up to it by

incrementally improving the capabilities of the autopilot system. So when you contrast all of

these, and on top of that, let me throw a question that nobody likes, but is a timeline. When do you

think each of these approaches, loosely speaking, nobody can predict the future, we'll see mass

deployment. So Elon Musk predicts the craziest approach is at the, 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. Even the data that you would, I mean,

if you look at the data, even kind of trying to extrapolate the end state without knowing what

exactly is going to 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 called a 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're looking at 20 years more, maybe, you know,

hopefully 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, you know, 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 at the end of the year. And some of us are more realistic,

they say, like, five years. But, you know, we all, I think, it's just hard to know. And I think

trying to predict, like, products ahead two, three years, it's hard to know in the following

sense, you know, like, we typically say, okay, this is a technology company, but sometimes

sometimes really, you're trying to build something where the technology does, like, there's a technology

gap, you know, like, and Tesla had that with electric vehicles, you know, like, when they

first started, they would look at a chart, much like a Moore's 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

that? We don't know. It goes like this, so it's probably just going to, you know, keep going.

Yeah. With AI that goes into the cars, we don't even have that. Like, we can't, I mean, what can

you quantify? Like, what kind of chart are you looking at, you know? 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 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, I think you've actually argued that it's not a

use, 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, you know, this is not my idea, and it's been, you know,

discussed several times, but this, this kind of like 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, that's important. We try to

understand. And, and I think that, you know, 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 a

literal technology gap. I mean, before, like, you know, you're trying to build, I'm not trying to,

you know, I think these companies are building great technology to, for example, enable internet

search to do it so quickly. And that kind of didn't, didn't, 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 Optimus 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, you know, going back, I think that iterated learning is like

key. That, you know, you're out there, you're running experiments to learn. And that doesn't

mean sort of like, you know, like, like your Optimus Ride, you're kind of doing something, but

like in an environment. But like what Tesla is doing, I think is also kind of like this, 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

inform people about what kind of technology that they're providing, you know, the good and the bad

and then, you know, not just sort of, you know, it works very well. But I think, you know, I'm

not saying they're not doing bad and informing. I think they're kind of trying, they, you know,

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 you know, people get informed. And so that kind of cycle continues. But

you know, 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. You know, I think like we talked about,

you know, people being remote, like it's something, or in the kind of environments that we're in,

or think about a Tesla car, maybe maybe you can enable it one day, like there's, you know, low

traffic, like you're kind of the stop on go motion, you just hit the button, and you can really,

or maybe there's another, you know, lane that you can pass into you go in that, I think they can

enable these kinds of products, 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, I would imagine

that it would be good to do some sort of like a like a one or two year plan, when it's a little bit

more predictable, that you know, the technology gaps you close and, and the kind of sort of product

that would ensue. So I know that from Optimus Ride, or you know, other companies that I get

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 that we want to get

to someday and saying five years, 10 years, I don't think it means anything. But iterated

learning is key, though, to do and learn. I think that is key. You know, I got to 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 got the 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 off some of the awesome stuff, the stuff that works and stuff

that doesn't work. I mean, it's just the stuff I saw with the tracking of different objects and

pedestrians. So I'm incredible stuff going on there. Just maybe it's just the neuro to me,

but I think the world would love to see that kind of stuff.

Yeah, that's that's well taken. I think, you know, I should say that it's not like, you know, we were

unable to, I think we made a decision at some point. That decision did involve me quite a bit

on kind of sort of doing this in kind of coding code stealth mode for a bit. But I think that,

you know, we'll open it up quite a lot more. And I think that we are also at Optimus Ride kind of

hitting a new era. You know, we're big now, we're doing a lot of interesting things. And

I think, you know, some of the deployments that we kind of announced were some of the first bits

of information that we kind of put out into the world will also put out our technology. A lot of

the things that we've been developing is really amazing. And then, you know, we're going to start

putting that out. We're especially interested in sort of like being able to work with the best people.

And I think, and I think it's good to not just kind of show them when they come to our office

for an interview, but just put it out there in terms of like, you know, 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 lighters are crutch. So I've talked to a bunch of people about it,

gotta 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 based, primarily camera based systems is going to be what defines the

future of autonomous vehicles. So what do you think of this idea? Ladders are 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. Like, I think that, you know, you can put in a lot of

autonomy and then you can do great things. And it's very possible that at the time scales,

like I said, we can't predict 20 years from now, like you may be able to do things that we're doing

today only with LiDAR and then 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, you know, that 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, and I think we may be looking at a

future like that. Do you think we're over relying on LiDAR right now? Because we understand the better

it's more reliable in many ways, in terms from a safety perspective. It's 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

LiDAR on a car. And it's kind of easy to build with when you have a LiDAR, 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've maybe seen some of it, is that we believe in computer

vision. We certainly work on computer vision and Optimus 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 LiDARs, 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 LiDARs and everything and the cameras. But, you

know, this, 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, you know, the LiDARs 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, or ensure their safety and things like that, that it's kind of stupid not to put

the LiDAR. Like, imagine this, you either put 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 LiDARs.

And especially either low range or short, either short range or low resolution LiDARs are actually

not that hard to build in solid state. They're still scanning, but like MAMS type of scanning

LiDARs 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 phase arrays that they're a little bit harder,

but, but I think, like, you know, putting a MAMS mirror in 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, but, you know, it's still, it's 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 on this problem? And it's a set of echoes of

the early DARPA challenge in the through the desert that we're seeing now, 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. A 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, and I've been interested in this problem for quite a while, actually, from a roboticist's

point of view. And that's what's happening in Alpha Pilot. Which, 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 humans have, you know, limitations in,

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, you know,

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 then, you know, it gets smarter and smarter and smarter.

And so, drone racing, the same issue. Humans play this game. And, you know, you have to like react

in milliseconds. And there's really, you know, 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, you know, getting information back and forth. But suppose those delays that

don't exist, you just, just a 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, you know,

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 lives 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 had enough compute in a car, and a very

fast camera, right at the time of an accident, you could use all compute you have, 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 there, the autonomy. And you know,

and you can, 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? So you work a lot with NVIDIA 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 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 weigh 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 realized 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 intelligent 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 do? How hard is it to get past the bottleneck 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. Great. So it's aggressive flight. How do we make drones see many more

frames a second in order to enable aggressive flight? That's a super interesting problem.

That's an interesting problem. But think about it. You have CPUs. You clock them at, you know,

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 people realize that. It's great. But like we can't make transistors smaller because like

quantum effects, electrons start 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. 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 embodies that embodies that. And that's why it's exciting. It's exciting

for people, you know, students like it, it embodies all those problems. But going back, we're building

code and code and other engine. And that engine, I hope one day will be just like how impactful

seat belts were in driving. I hope so. Or it could enable, you know, next generation autonomous air

taxis and things like that. I mean, it sounds crazy, but one day we may need to perchland these

things. If you really want to go from Boston to New York in more than a half hours, you may want

to fix big aircraft. Most of these companies that are kind of doing code 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 perch landing, just going to go into into a building.

If you want to do that, like 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 quad

copter, you take a fixed wing. While you're at it, you might as well put some like rocket engines

in the back and 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 to 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 is really

difficult engineering, practically speaking challenge. So that, let me ask about the,

the, the alpha pilot challenge is a, I guess a big prize potentially associated with it.

But let me ask reminiscent 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 is 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 easier, a lot harder. So how many in the space of all possible courses

are would humans win and would 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 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 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. And 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 AlphaPilot is that they built 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 crash.

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 drone and somebody with like a face mask and

a glows are going 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 AlphaPilot Challenge where we have

these 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 I don't think you want to tell people crashing is okay. We

want to be careful here because we don't want people to crash a lot. But certainly, we want them

to push it so that everybody crashes once or twice. And they're really pushing it to their limits.

That's where iterated learning comes in. Every crash is a lesson.

It's a lesson, exactly.

So in terms of the space of possible courses, how do you think about it in the war of human

versus machines? Where do machines win? We look at that quite a bit. I think that you will see

quickly that 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 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

realized 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 easy to do. So that's what they excel in. And also sort of repeatability is

easy 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, a 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 robotics, you have somehow the

field has split into like, there's people who would work on like perception, how robots perceive

the environment, then how do you actually make decisions? And there's people also like how

to interact with robots. There's a whole bunch of different fields. And 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 Bellman's equation. And so it's this person who have realized

like way back, you know, 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's one beautiful equation that, you know, like today

people do reinforcement, we still use it. And 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 computationally,

how hard the problem is. I feel like my, 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, like how can we get it

faster to the point where we now got to like, let's just redesign this chip, like 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 kind of grow, the number of decisions

you can make grows rapidly. Like if you have, you know, 100 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 curse of dimensionality, nevertheless, in practice works among, you know,

despite all those challenges, 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 and decision making how little

we know, just like how little we know about the beginning of time, how little we know about,

how little we know about, you know, our own future. Like, if you actually go into like from

Bauman's 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 consistent. It's just crazy.

Yeah. I think a good 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, it seems like a

good metaphor. Sir Tash, thank you so much for being a friend, a colleague, a mentor. I really

appreciate it. It's an honor to talk to you. Likewise. Thank you, Lex. Thanks for listening to

this conversation with Sir Tash Karaman. And thank you to our presenting sponsor, Cash App.

Please consider supporting the podcast by downloading Cash App and using code Lex podcast.

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And now let me leave you with some words from Hal 9000 from the movie 2001, A Space Odyssey.

I'm putting myself to the fullest possible use, which is all I think that any conscious entity

can ever hope to do. Thank you for listening and hope to see you next time.